Table of Contents
Section 1: Introduction………………………………………………………7
- Structure of the Thesis……………………………………………….9
Section 2 Literature Review…………….…………………………………..…10
2.1. Capital Structure Determinants …………………………………………10
2.2. Multinational Capital Structure in an Emerging Market………………….11
2.2.1 Institutions, Environment, and Firm Characteristics…………………….11
2.2.2 Diversification, Exposure to Exchange Rate and Politically Risky Environments…12
2.2.3 MNCs Entry Mode and Cost for Entering Emerging Markets………………….19
2.2.4 Foreign Affiliates Capital Structures…………………………………………….20
2.2.5 Effects of Social Violence………………….……………………………………22
2.2.6 Business Cycles of Capital Inflows, Capital Structure and Debt Maturity………23
Section 3: Data Description……………………………………………………….…..31
3.1 Capital Structure and Variable Definition…………………..…………………….31
3.1.1 The meaning of Capital Structure…………………………..………………..…32
3.1.2 Firm-Specific Information ………………………………….…………………..34
3.1.3. Macroeconomic Factors ………………………………………………….……35
3.1.4. Descriptive Statistics………………………………………………..……………35
3.1.5. Data Source ………………………………………………………………….……36
Section 4: Methodology..……….…………………………………………..………………………….36
4.1 Correlation Analysis…………………………………………………..…………..39
4.2 The Empirical Model ……….……………………………………………………41
4.3 Panel Data Analysis………………………………………………………………42
Section 5: Market Description ………………………………………………………42
5.1 The Indian Market………………………………………………………..…….43
5.1.1 An Emerging Economic Powerhouse………………………..………………45
5.1.2 Foreign Trade and Investment ………………………………………..…….45
5.1.3 More Economic Reforms……………………………………………..……. 46
6. 1. Russian Market………………………………………………………….. 47
6.2 Modigliani and Miller Theorem ………………………………………….. 49
7. China Market ……………………………………………………….…….53
8. Brazilian Market …………………………………………………………..58
9.1. Presentation and Examination of Results ……………………………………..63
9.1.2 Descriptive Statistics …………………………………………………………43
9.2. Correlation Analysis…………………………………………………………….65
9.3. Panel Regression ……………………………………………………………….68
10. Discussion of Results ………………………………………………………………..70
11. Conclusion………………………………………………………………………….. 79
12. Limitations of the Study and Areas for Future Research……………………….…….80
12.1. Key Challenges and Lessons Learnt………………………..……………….…….82
Table of Figures
Figure 1…………………………………………………………………………………….. 34
Macroeconomic Performance, 2000-2007
Figure 2 …………………………………………………………………………………….47
Figure 3 ……………………………………………………………………………………..48
Actual and Fitted Values and Residuals
Table 3 …………………………………………………………………………………47
Table 4 …………………………………………………………………..…………….53
Fixed Effect Results
Equation (1) ………………………………………………………………………………..28
Equation (3) …………………………………………………………………………….….32
Studies on the determinants of MNC (Multinational Corporation) capital structure entering emerging markets have begun to emerge as an extended new line of research. Thus, this study aims to investigate the capital structure of multinational corporations entering the emerging market of India based on firm-specific information and macroeconomic determinants. The model is evaluated and tested by correlation analysis.
The main approach to fitting the regression equation is panel data using the fixed effects approach or least-squares dummy variable (LSDV) regression model. This investigation identifies firm-specific information and macroeconomic variables in driving MNCs financial leverage ratio. The results show that profitability, GDP growth volatility, inflation rate and stock market liquidity correlated positively with capital structure. On the other hand, firm size, business risk, and foreign exchange rate risk indicate an inverse relationship with capital structure.
The results of the panel data regression analysis denote that firm size has a significant effect on capital structure. Thus, if firm size goes up by 1 percent, on average, MNCs capital structure in terms of shareholder-liquidity ratio goes down by about €47.2383 million. GDP growth rate volatility significantly affects capital structure. If GDP growth volatility goes up by 1 percentage point, capital structure also goes down by about €12.4776 million, ceteris paribus. The regression equation also reveals that capital structure decreases by €1.1999 million for every 1-percentage point increase in inflation rate. Inflation rate significantly affect MNCs capital structure. In the case of profitability, business risk and foreign exchange rate the hypothesis of having no significant effect has to be accepted.
Section 1: Introduction
Capital structure in multinational companies is probably one of the major prolific fields of study within the corporate finance. All-embracing study over the last four decades has produced less conclusive guideline for finance managers choosing between equity and debt in financing their organizations.
Companies may raise money from both internal and external sources. They can raise money from internal sources by partly reinvesting back their profit or, they can raise money from external sources by issuing debt or equity. When a company issues shares, shareholders hope to receive dividend on their investment. However, the company is not obliged to pay any dividend. Because dividend is discretionary, it is not considered to be a business expense. When a company borrows money by way of debt, it promises to make regular interest payment and to repay the principal.
All gains go to the shareholders as profits rise and the debt holders continue to receive a fixed interest payment. Conversely, if profits fall, shareholders bear all the pain. In times of economic downturn, the company that has borrowed heavily may not be able to repay its debt. The company is then become bankrupt and shareholders lose their entire investment. Because debt increases returns to shareholders in good times and reduces them in bad times, it creates financial leverage. An unlevered firm uses only equity capital whereas a levered firm uses a mix of equity and various forms of debt. Common ratios such as debt-to-total capital or debt-to-equity quantify this relationship.
The importance of leverage in the capital structure of the company is that its efficient use reduces the cost of capital and in turn increases the net economic returns which, ultimately increases firm value. In sum, the guiding principle of leverage is to choose the course of action that maximizes the firm value and the value of the firm is maximized when the cost of capital is minimized.
Research on the determinants of MNC capital structure entering emerging markets has emerged as an extended new line of research for several reasons. Capital and stock markets in emerging economies are relatively less efficient than their developed counterparts. This causes financing decisions to be incomplete and subject to a considerable degree of irregularity. Companies in emerging markets may not be able to rationalize the financing decisions to follow a clear theoretical approach. This requires a thorough examination of the real determinants of capital structure in an emerging market and the results are to be compared with those reached in developed markets. Also, information asymmetry in emerging stock markets is considerably higher than the developed markets which lead to none optimal financing decisions in terms of the theoretical assumptions of capital structure theories. Finally, the literature on the determinants of capital structure has already been developed in developed markets that have different institutional financing arrangements from those in emerging markets. This requires a thorough examination of the predictors of capital structure in an emerging market.
Thus, this study aims to investigate the capital structure of multinational corporations entering the emerging market of India based on firm- specific information and macroeconomic determinants.
1.2. Structure of the Thesis
The sections that follow provide the outline for the research. Section 2 examines the existing literature on the determinants of capital structure. Section 3 discusses the methodology. Section 4 presents the markets description for India. Section 5 provides a description of the data used for the analysis. The results are presented and examined in Section 6. An analysis and critical discussion of the results follows in section 7. Section 8 provides the conclusion.
Section 2: Literature Review
The existence of three theories is well documented in the literature on determinants of capital structure, these are: pecking order, trade-off, and free cash flow. Each theory presents a different explanation of corporate financing. The trade-off theory is concerned with the trade-off between debt tax shields (or tax saving) and bankruptcy costs, according to which an optimal capital structure is assumed to exist. The pecking order theory assumes hierarchical financing decisions where firms depend first on internal sources of financing and, if these are less than the investment requirements, the firm seeks external financing from debt as a second source, then equity as the last resort. The free cash flow theory assumes that debt presents fixed obligations (debt interests and principals to pay) that have to be met by the firm. These obligations are assumed to take over the firm’s free cash flow (if it exists), therefore prevents managers from over consuming the firm’s financial resources.
It was recognized that the three theories are “conditional” in a sense that each works out under its own assumptions and propositions (Myers 2001). That is, none of the three theories can give a complete picture of the practice of capital structure. This means that firms can pursue capital structure strategies that are conditional as well. That means that when the business conditions change, the financing decisions and strategies may change, moving from one theory to another. This is the main reason that the literature does not include one theory (or one explanation) on the determinants of capital structure. In fact, an interrelationship can be observed between and among the three theories of capital structure. It was also found out that studies on the determinants of capital structure include selected determinants in a regression equation. The results in many cases turned out to be mixed (Fama & French 2002).
- Capital Structure Determinants Copyright 2001 Blackwell Publishers Ltd.
Corporate capital structure remains a controversial issue in modern corporate finance. Since the seminal work by (Modigliani and Miller 1958), a plethora of research has been undertaken in an attempt to identify the determinants of capital structure, particularly on domestic corporations (DCs). Considerably less research has been undertaken to identify the determinants of capital structure for multinational corporations (MCs). Multinational corporations control considerable amounts of wealth and, if capital structure is relevant to firm value, then understanding the determinants of capital structure for MCs is important.
(Modigliani and Miller 1958) are widely regarded as the first authors who began the debate on the relevance of capital structure to firm value. Since then the debate has progressed from academic model to practical reality. It is now generally recognized that capital structure is relevant to firm value. The factors that determine capital structure are a combination of variables. Although these variables have been researched extensively for corporations, few studies have considered the relation these variables have with capital structure for MCs.
Theoretical studies based on international environmental factors predicted that MCs will have lower leverage than DCs (Lee & Kwok 1988; Burgman 1996; Shapiro 1996). Theoretical studies suggest there is a difference between MCs and DCs in terms of the determinants of leverage. Major determinants of capital structure include agency costs, bankruptcy costs, taxation, profitability, size, collateral value of assets and industry sector. For MCs additional determinants include level of overseas diversification, exposure to foreign exchange risk and exposure to political risk.
It is often argued that the international diversification of earnings should enable MCs to sustain a higher level of debt than DCs, without increasing their default risk (Shapiro 1996; Eiteman et al 2001). However, while it is believed that there are several gains to be made by venturing into overseas markets, it can be argued that continued foreign expansion has increasing risks. (Erunza and Senbet 1984) found that the incremental gains from international diversification beyond homemade diversification portfolios have diminished. It is not known whether MCs that have greater levels of geographic diversification of earnings have relatively less leverage. The more sensitive a firm’s cash flows and earnings are to foreign exchange rate fluctuations, the lower the expected level of debt (Burgman 1996). (Choi & Prasad 1995) analyzed the relationship between foreign exchange risk and corporate financing decisions and reported that foreign exchange risk significantly affects a firm’s financing decisions for international investments. Further, exchange rate movements affect both the cash flows of a firm’s operations and discount rate employed to value these cash flows (Bartov et al 1996). Therefore, MCs with higher foreign exchange risk are expected to have lower leverage. Political risk is the chance that political events will have an adverse effect on the operations of the firm. Political risks include expropriation of assets, trade controls, and institutional ineffectiveness, threat of war, social unrest, and disorderly transfers of power, political violence, international disputes, regime changes and regulatory restrictions (Jodice 1985). MCs that face higher political risk are expected to have less leverage due to greater probability of wealth loss.
The remaining determinants are common to capital structures for both MCs and DCs. Higher agency costs are expected to lower debt levels (Jensen 1986; Doukas and Pantzalis 2003). MCs agency costs are expected to be higher relative to DCs due to higher auditing costs, language differences, sovereignty uncertainties and varying legal and accounting systems (Burgman 1996). Therefore, leverage of MCs is expected to be relatively lower than DCs. Higher bankruptcy costs are expected to reduce debt levels. MCs are expected to have lower bankruptcy costs relative to DCs due to their ability to diversify across less than perfectly correlated markets (Burgman 1996; Reeb 1998). To proxy bankruptcy costs, several researchers, including Bradley et al (1984) and (Lee & Kwok 1988) used the standard deviation of the first difference in earnings before interest and taxes (EBIT) scaled by the mean value of the firm’s total assets.
(Myer’s 1984) pecking order theory of capital structure shows that if a firm is profitable then it is more likely that financing would be from internal sources rather than external sources. More profitable firms are expected to hold less debt, since it is easier and more cost effective to finance internally. (Cassar and Holmes 2003) provided support for (Myer’s 1984) pecking order theory in a sample of Australian firms. MCs have better opportunities than DCs to earn more profit mainly due to having access to more than one source of earnings and better chances to have favorable business conditions in particular countries (Kogut 1985; Barlett & Ghoshal 1989). Consequently, this would suggest that MCs are more profitable than DCs and therefore they are expected to have relatively lower debt levels than DCs.
Firm size has been found to be a determinant of capital structure (Agrawal & Nagarajan 1990). In relation to MCs and DCs, it is expected that MCs are larger in size than DCs and therefore would carry higher debt levels. The tangibility of assets, or collateral value of assets held by a firm has found to be a determinant of leverage (Rajan & Zingalis 1995). Firms with high collateral value of assets can often borrow on relatively more favorable terms than firms with high intangible assets or assets without collateral value. This would suggest that there is a positive relationship between leverage and collateral value of assets. In relation to MCs and DCs, it is uncertain whether the level of collateral assets is higher or lower for MCs relative to DCs. Myers (1984) suggests that asset risk, asset type, and requirements for external funds vary by industry. Firm debt ratios are also expected to vary by industry (Harris & Raviv 1991; Michaelas et al 1999). However, whether there is any difference in industry effects between MCs and DCs capital structure is not known.
- Multinational Capital Structure in an Emerging Market
According to the three reasons above-mentioned, “in an emerging market, determinants of capital structure include mixed predictors from three theories: tradeoff, pecking order and free cash flow.”
- Institutions, Environments, and Firm Characteristics
The paper of (Deesomsak et al 2004) contributed to the capital structure literature by investigating the determinants of capital structure of firms operating in the Asia Pacific region, in four countries with different legal, financial and institutional environments, namely Thailand, Malaysia, Singapore and Australia. The results suggested that the capital structure decision of firms is influenced by the environment in which they operate, as well as firm-specific factors identified in the extant literature. The financial crisis of 1997 is also found to have had a significant but diverse impact on firm’s capital structure decision across the region.
Institutions, environments, and firm characteristics are indeed important determinants of capital structure. From a sample of firms across 45 countries, (Cheng and Shiu 2007) found that investor protection plays an important role in the determinants of capital structure: firms in countries with better creditor protection have higher leverage, while firms in countries where shareholder rights are better protected use more equity funds. The other differences in institutions and environments also explain the cross-sectional variation in the aggregate capital structure across counties. Furthermore, firm characteristics identified by previous studies, as correlated in a cross-section with capital structure in developed markets, are similarly correlated in the present sample of countries. The evidence presented in this study indicated that institutional differences are as important as firm characteristics in determining capital structure.
The dynamics of the world economy and global competition patterns are encouraging multinational Corporations (MNCs) to expand into emerging economies. The study of (Luo 2002) validated the proposition that entry mode selection in an emerging economy is influenced by situational contingencies at four levels: nation, industry, firm, and project. Analysis of data collected from China suggested that joint venture is preferred when perceived governmental intervention or environmental uncertainty is high or host country experience is low. The wholly-owned entry mode is preferred when intellectual property rights are not well protected, the number of firms in the industry is growing fast, the need for global integration is high, or the project is located in an open economic region. The importance of these multilevel determinants requires simultaneous and inseparable considerations of the risk, return, control, and resource effects of the entry mode decision. This necessitates a theoretical integration of multiple perspectives such as transaction cost, the eclectic paradigm, bargaining power, and organizational capability.
- Diversification, Exposure to Exchange Rate and Politically Risky Environments
The relationship between corporate diversification and firm performance represents one of the most extensively researched areas in the fields of corporate finance, strategic management and industrial organization. Efficient internal capital market argument typically suggests that corporate diversification creates firm value. A diversified firm has greater flexibility in capital formation because it has more access to internally generated resources as well as external funds. By forming an internal capital market, diversified firms are in a better position to allocate resources to more deserving and capital starved divisions. They do so by directing capital away from slow growing, cash generating operations to businesses that are expanding rapidly and have great commercial potential, but need investment. Both existing divisions as well as new ventures, which lack a track record and for which limited information is available to external sources, would benefit as a consequence.
Diversified firms can also employ a number of mechanisms to create and exploit market power advantages, tools that are largely unavailable to their more focused counterparts. These include predatory pricing (generally defined as sustained price cutting with the design of driving existing rivals from future entry), cross-subsidization (whereby the firm taps excess revenues from one product line to support another), entry deterrence (achieved by constructing a reputation for predatory behavior or by signaling that such a response is likely in the event of a new entry), reciprocal buying and selling (whereby the focal company gives preference in purchasing decisions or contracting requirements to suppliers). Further benefits of diversification include the ability to exploit excess firm specific assets and share resources such as brand names, managerial skills, consumer loyalty and technological innovations. Apart from financial and intangible resources, (Porter 1987) argued that resource sharing at the corporate level can create value by transferring skills and sharing rent-seeking activities among individual business units.
(Berger and Ofek 1995) found that benefits also stem from tax and other financial advantages associated with diversification and increased debt capacity due to reduced bankruptcy probabilities (Lewellen 1971). (Majd and Meyers 1987) for instance, noted that undiversified firms are at a significant tax disadvantage because tax is paid to the government when income is positive, but the government does not pay the firm when income is negative. This disadvantage is reduced, but not eliminated, by the tax code’s ‘carry back’ and ’carry forward’ provisions. Their analysis predicted that as long as one or more segments of conglomerate experience losses in some years, a conglomerate pays less in taxes than its segments would pay separately.
Despite considerable attention being devoted to the relationship between corporate diversification and firm performance, much of prior research has generally assumed away the impact of differences in the organizational form of firms. (George & Kabir 2005) attempted to address this lacuna and investigated how business groups – a widely prevalent organizational form in many developed and emerging markets – influences the diversification-performance relationship. Analyzing a large sample of firms from India, they found that firms affiliated to business groups are significantly more diversified than independent firms. They also documented that corporate diversification by independent firms reduces firm performance, but that undertaken by group affiliated firms has no impact. Additional analysis revealed that the impact of corporate diversification is not homogeneous across all business groups: for firms affiliated to smaller business groups, diversification significantly lowers profitability while for firms affiliated to larger business groups, diversification enhances profitability. Overall, the study points to the importance of factoring in a firm’s organizational form in investigating the influence of corporate diversification on firm performance.
The impact of the exchange rate variations on the firm value has been a focal issue in international finance as increasing exchange rate volatility proved to be a significant risk factor. Foreign currency exposure literature reflects a consensus view that exposure arises from direct involvement in exports, imports, and or foreign currency denominated funding of operations as well as impact of exchange rates on the competitive position of the firm in its industry. Although these four sources may lead to exposure for all firms involved in the global economy, two of these sources, namely, exports and foreign currency liabilities render a point of variation for the difference in the nature of exposure of Emerging Market Multinationals (EMNCs).The export oriented developed country multinationals follow an expansion strategy where they first move into other developed economies and, only at later stages of market expansion prefer to move into emerging markets. As posited in psychic distance theory or the stages model of internationalization (Johanson and Wiedersheim-Paul 1975, Johanson and Vahlne 1977, Kogut and Singh, 1988), multinational firms tend to expand into regions that exhibit economic and institutional similarities and only after they accumulate considerable international experience do they move on to locations with distinct economic and institutional characteristics. Therefore, it is plausible to argue that developed country MNCs are primarily exposed to fluctuations of stable currencies.
Following the same line of thought, it is believed that EMNCs follow an expansion strategy, where they first seek the markets of their peer emerging markets before expanding into economically and institutionally more developed economies (Kumar and McLeod 1981, Lecraw 1977, Ting and Schive 1981, Wells 1983). The Foreign Direct Investment (FDI) data offer unambiguous support for this argument. The FDI outflows from emerging markets have risen from $3 billion in 1991 to $16 billion in 2002 and then to an estimated $40 billion (Global Development Finance 2005). This argument is also supported by the evidence that EMNCs are more adept in dealing with the governments of other emerging markets, which are perceived to be too risky, and therefore are more active in countries largely neglected by the developed country MNCs (Grosse 2003). Obviously, EMNCs operating in politically and economically unstable environments are far more vulnerable to internal and external shocks, and are primarily exposed to fluctuations of more volatile currencies. A similar argument was constructed by (Kwok & Reeb 2000) in an attempt to explain variations in the systematic risk of DMNCs. In their “up-stream down-stream hypothesis” (Kwok & Reeb 2000) indicated that when developed country MNCs expands into a less developed countries, they tend to increase their systematic risks.
In contrast, firms from less developed countries tend to decrease their systematic risk by expanding into developed country markets. However, EMNCs find it difficult to establish operations in more developed economies because of sophisticated and mostly unfamiliar institutional infrastructure, complexity and intensity of rivalry in developed markets. For example Acer, one of the world’s largest computer manufacturers from Taiwan, tried to build a global brand, particularly by entering the developed countries. The branded business grew to significant volumes but continued to generate losses because the competitive environment was challenging for Acer. Meanwhile, customers for Acer’s contract manufacturing product line feared that their business secrets would spill over to competing lines of businesses. They also feared that Acer would cross-subsidize its own brand with profits from contract manufacturing and undercut their prices. In 2000, Acer’s strategy blew apart when IBM cancelled a major order, reducing its share of Acer’s total contract-manufacturing revenue from 53% in the first quarter to only 26% in the second quarter of 2001 (Khanna 2003).
Despite their growing sophistication and ambitious strategic orientation, resource constraints and lack of experience in many EMNCs impose limits on the variety of markets to expand, and as a result, they are not as geographically diversified as their developed country counterparts. The lack of diversification renders EMNCs vulnerable to contagion effects and augments their exposure to business cycle and currency risks. The contagion effects in emerging markets are evident in the 1990’s financial crises, which affected these markets in tandem. Another point of departure stems from the differences and constraints faced in access to external funds. While EMNCs originating from illiquid and segmented markets have limited access to funds in their local currency, developed country MNCs are not constrained to raise capital in a foreign currency in international capital markets. In general, they have multitude of options allowing them to raise capital in their own currency either in their liquid home markets or international capital markets. Consequently, EMNCs are more likely to raise external capital in foreign currencies to fund their operations and particularly large capital investments. The EMNCs’ inherent difficulties to raise capital in their local currencies increase their currency exposure unlike their counterparts in developed economies that do not face similar constraints. The third and the most important distinction between EMNCs and developed country MNCs, lies in the endemic institutional weaknesses in emerging markets. These institutional voids (low transparency, weak corporate governance, macroeconomic and financial instability) increase the cost of capital and constrain access to equity and long-term debt markets. These conditions force EMNCs’ to international capital markets rather too soon, and contribute to their currency exposures as they are not able to raise capital in home currencies. This early entry is coupled with the fact that advanced derivative instruments used by MNCs to hedge their exposures are virtually absent in most emerging markets. In summary, the nature of exposure for EMNCs exhibit different characteristics than developed country MNCs, and naturally vary across different industries, countries, and regions.
American multinational firms responded to politically risky environments by adjusting their capital structures abroad and at home according the study conducted by (Desai et al. 2003). Foreign subsidiaries located in politically risky countries have significantly more debt than do other foreign affiliates of the same parent companies. American firms further limit their equity exposures in politically risky countries by sharing ownership with local partners and by serving foreign markets with exports rather than local production. The residual political risk borne by parent companies leads them to use less domestic leverage, resulting in lower firm-wide leverage. Multinational firms with above-average exposures to politically risky countries have 8.4 percent less domestic leverage than do other firms. These findings illustrate the impact of risk exposures on capital structure.
The existing literature linking politics and the investments of multinational corporations concludes that high levels of political risk deter investors from entering into some emerging markets. In his paper, (Jensen 2006) argued that multinationals have multiple tools to manage risks (rather than simply avoiding them). Thus the risks faced by many MNCs are endogenous to the firm’s operations and investment strategies. Drawing on a confidential data set covering the complete universe of U.S. foreign direct investments abroad, he found that U.S. multinationals restrict the size of their operations in authoritarian regimes relative to democratic regimes in order to minimize the amount of assets at risk. He also found preliminary evidence that firms attempt to increase their political influence by aligning their operations with the preferences of incumbent governments. Specifically, firms increase the number of workers they employ when left-of-center governments come to power.
- MNCs Entry Mode and Cost for Entering Emerging Markets
Using unique firm-level data, (Bhaumik & Gelb 2003) brought into focus a comparison between two emerging markets, South Africa and Egypt, that have very different political and economic legacies, institutions and business environment. The results indicated that the determinants of the choice of MNC’s entry mode would be different for these two countries; MNCs mode of entry in their manufacturing and services sectors also differs. The paper has demonstrated that the largely stylized specification usually used in the context of developed market economies, by and large, yields meaningful result in the context of entry into emerging markets, more so if the emerging market (e.g., South Africa) has well functioning markets and market institutions to some extent. An important upshot of the empirical analysis is that in the context of emerging markets regulations and factors that determine the transactions cost of doing business are the key determinants of the choice of the mode of entry; the role played by the technology embedded in the MNCs’ products in determining the choice of entry mode is largely insignificant.
(Kaldor 2005) explored the costs of foreignness for firms entering emerging markets. This paper assessed the costs of foreignness in Hungary’s commercial banking sector between 1995 and 1999 using econometric panel analysis. This analysis showed that foreign ownership was a significant source of disadvantage. Foreign-owned banks were particularly disadvantaged with poorer returns on their organizational size. When expatriate CEOs represented owner’s interests, foreign banks had better returns on their assets. However, expatriate CEOs achieved lower returns on their bank’s organizational size. All banks achieved better returns on their organizational size as representation of Hungarians among the banks top management increased. Significantly, this effect was magnified at banks with an expatriate CEO. Instead of simply transferring their banking techniques and knowledge from developed economies, foreign banks and expatriate CEOs had to adapt to the Hungarian market. Organizational conditions that increased local managers’ influence improved the use of local knowledge and also encouraged transnational collaboration that compensated for much of the cost of foreignness.
- Foreign Affiliates Capital Structures
(Desai et al. 2003) analyzed the capital structures of foreign affiliates of U.S. multinational firms, thereby obtaining evidence of the workings of their internal capital markets. The used of confidential affiliate-level data made it possible to distinguish the behavior of foreign affiliates of the same parent companies operating in markets with differing tax rates and capital market regimes and to differentiate the determinants of external borrowing and borrowing from parent companies. The sample included information on the activities of roughly 3,700 U.S. multinational firms operating in more than 150 countries through approximately 30,000 affiliates in 1982, 1989, and 1994. Three main empirical findings emerged. First, there was strong evidence that affiliates of multinational firms alter the overall level and composition of debt in response to tax incentives. The estimates imply that 10 percent higher tax rates are associated with 2.8 percent greater affiliate debt as a fraction of assets, internal finance being particularly sensitive to tax differences. While the estimated elasticity of external borrowing with respect to the tax rate is 0.19, the estimated tax elasticity of borrowing from parent companies is 0.35. Second, the level and composition of leverage were influenced by capital market conditions. In countries with weak creditor rights and shallow capital markets, affiliates borrow less externally and more from parent companies. This suggests that internal borrowing may substitute for costly external borrowing. Political risk and inflation also appear to influence affiliate leverage and its composition. Increased political risk is associated with greater overall leverage in the form of expanded external borrowing, while inflation does not appear to affect overall leverage, though higher inflation was associated with greater external borrowing and reduced internal borrowing. Third, the evidence indicated that external borrowing was more costly in environments in which creditor rights are weak and capital markets are shallow and that affiliate’s substitute parent for external borrowing in response to these costs. Interest rates on external debt differ for affiliates of the same American parent company located in different host countries in a manner that corresponds to measures of capital market depth and creditor rights. One percent higher interest rates on external debt due to legal and capital market conditions are associated with external borrowing that falls by 1.3 percent of assets and borrowing from parent companies that rises by 0.8 percent of assets. The analysis concluded that variation in borrowing costs changes debt sourcing decisions of multinational affiliates.
2.2.5 Effects of Social Violence
The fact that previous studies regarding the effects of social violence on foreign direct investment (FDI) flows come to contradictory conclusions motivated (Ayse et al. 2007) to investigate the social violence-FDI relationship in an ethnically heterogeneous and resource-rich country, Indonesia. The empirical analysis used a unique dataset that consists of FDI flows and different types of social violence in 26 provinces of Indonesia during the period 1992-2001. A fixed-effects regression was applied to estimate the effects of social violence on FDI flows in Indonesian provinces. The results indicated that only certain types of social violence such as ethnic and industrial relations violence were detrimental to FDI. Multinational firms seem to differentiate among the several types of social violence and respond only to those that may affect their expected future profits. Accordingly, the immediate policy implication of this result implies that developing countries having the desire to attract FDI flows should be aware of the fact that multinational firms seem to differentiate among the several types of social violence and respond only to those that may affect their expected future profits (Ayse, Evrensel, Kutan 2007)
- Business Cycle of Capital Inflows, Capital Structure and Debt Maturity
According to (Smith & Valderrama 2008) the composition of capital inflows to emerging market economies tends to follow a predictable dynamic pattern across the business cycle. In most emerging market economies, total inflows are pro-cyclical, with debt and portfolio equity flowing in first, followed later in the expansion by foreign direct investment (FDI). To understand the dynamic composition of these flows, they used a small open economy (SOE) framework to model the composition of capital inflows as the equilibrium outcome of emerging market firms’ financing decisions. They showed how costly external financing and FDI search costs generate a state contingent cost of financing such that the cheapest source of financing depends on the phase of the business cycle. In this manner, the financial frictions are able to explain the interaction between the types of flows and deliver a time-varying composition of flows, as well as other standard features of emerging market business cycles. If, as this work suggests, flows are an equilibrium outcome of firms’ financing decisions, then volatility of capital inflows is not necessarily bad for an economy. Furthermore, using capital controls to shut down one type of flow and encourage another is certain to have both short- and long-run welfare implications.
(Demirgüç-Kunt & Maksimovic 1999) examined firm debt maturity in 30 countries during the period 1980-1991. They found that large firms in countries with active markets have more long-term debt, while small firms in countries with large banking sectors tend to have longer maturity debt. (Booth et al 1999) found evidence that debt ratios in developing countries are affected in the same way and by the same types of variables that are significant in industrial countries. However, there are systematic differences in the way these ratios are affected by country-specific factors. Also, knowing the country-of-origin is more important than knowing the size of all the independent variables.
Most empirical studies on debt maturity have concentrated on the United States. (Mitchell 1991) and (Morris 1992) pioneered studies regarding debt maturity. While (Morris 1992) investigated the maturity structure of the firm’s total indebtedness, (Mitchell 1991) focused on the maturity of single bond issues. (Easterwood & Kadapakkam 1994), (Barclay and Smith Jr. 1995), Barclay and (Smith Jr. 1996), (Stohs & Mauer 1996), (Johnson 1997), (Scherr & Hulburt 2001), and Lyandres and Zhdanov (2003) followed the first approach. The second approach is preferred by (Mitchell 1993), (Guedes & Opler 1996), and (Gottesman & Roberts 2003), the latter investigating the maturity of bank loans. (Baker et al 2002) also investigated bond issues, and in the aggregate, found evidence of market timing of bond issues.
In the international setting, (Schiantarelli & Sembenelli 1997) investigated the maturity structure of 604 non-financial firms from the United Kingdom and 750 non-financial firms from Italy and found support for the hypothesis that firm choose the maturity of their liabilities to match those of their assets. Their results are in line with those of (Ozkan 2000) who investigated the maturity issue for 429 non-financial British firms in the period 1983-1996 and (Heyman et al 2003) who investigated the maturity of 1,091 Belgian small firms. (Antoniou et al. 2002) examined the determinants of debt maturity for a sample of 358 French, 582 German, and 2,423 British non-financial firms and found that debt maturity depends on both firm-specific and country-specific factors, opening the question of the degree of influence of each group of factors on the maturity structure.
Larger sets of countries are studied by (Demirgüç-Kunt & Maksimovic 1999) who explored the hypothesis that the financial development of a country determines the maturity of its firms’ debt. Using 9,649 non-financial firms from 30 countries including developing ones in the period 1980-1991, they found support for the hypothesis that legal and institutional differences among countries explain a large part of the leverage and debt maturity choices of firms. (Fan et al 2003) also studied the subject for 11 industries in 39 countries – in addition to 1,524 chemical firms in the period 1991-2000. Their results largely support (Demirgüç-Kunt & Maksimovic 1999) findings.
(Barclay et al. 2003) proposed the requirements for a theory of financial policy to have testable implications and focused their work on the choice between leverage and maturity. They developed their model from the argument that a firm chooses leverage and debt maturity to maximize its value given a set of exogenous firm characteristics such as its investment opportunity set and regulatory status. The authors showed that, for the leverage-maturity problem, the single crossing property holds, but the quasi-supermodularity one does not. The practical implication is that leverage and debt maturity are likely to be substitute policies instead of complementary ones. The authors illustrated their point empirically using data from 5,765 industrial firms in the United States from 1980 to 1999. Besides endogenous variables for capital structure and debt maturity, they employed exogenous variables such as growth opportunities, industry regulation, firm size, profitability, tangibility, asset maturity, average tax rate, net-operating loss carry forwards, and a dummy variable for firms with commercial paper programs. Their empirical analysis suggested that capital structure and debt maturity are substitutes in addressing financial problems of the firms.
The (Barclay et al. 2003) paper, however, ignored the effect that lagged leverage and maturity may have on the determination of the contemporaneous endogenous variables. As a matter of fact, it is likely that the change in a firm’s capital structure and debt maturity is somewhat rigid and by no means costless. If that is the case, the previous period’s level of debt and maturity is a relevant variable in the firm’s choice today. This is one aspect where (Mateus & Terra 2005) improved in their analysis. (Mateus & Terra 2005) investigated the choice between debt and equity simultaneously with the decision between short-and long-term debt for a large sample of emerging markets from Latin America and Eastern Europe. Employing dynamic panel data analysis, they tested (Barclay et al. 2003) model of joint capital structure and debt maturity determination using the Generalized Method of Moments on a system of structural equations. The empirical results supported three main findings. First, capital structure and debt maturity are policy complements in Latin America and policy substitutes in Eastern Europe. Second, there is a substantial dynamic component in the determination of the endogenous variables that have been neglected by previous research. Finally, firms face moderate adjustment costs towards optimal debt maturity, (Terra 2005)
Section 3: Data Description
3.1. Capital Structure and Variable Definition
(Modigliani & Miller 1958) are widely regarded as the pioneers in modeling the relevance of capital structure to firm value. Since then the debate has progressed from theoretical modeling to practical reality. This investigation identifies firm-specific information and macroeconomic variables in driving MNCs financial leverage ratio. Generally, these variables relate to value and risks of the firm as faced by bondholders, equity holders and managers.
3.1.1. The meaning of capital structure
Capital structure measured in terms of corporate leverage ratio is differently calculated in this investigation. Usually, the debt-to-equity ratio, total liabilities and debt to total assets ratio and the total long-term debt to capital are the proxies used for corporate leverage ratio. (Rajan and Zingales 1995) provided an overview about the definition of capital structure.
In relation to the capital structure of multinational firms, this research will define capital structure in alignment with the definition of (Lee and Kwok 1988), (Burgman 1996), (Chen et al 2007). The leverage ratio is treated as:
LEVERAGE= (Long Term Debt)/ (Long Term Debt + Market Value of Equity) (4)
This is in line with definitions used by previous empirical studies by Rajan and Zingales (1995); Fama and French (2002); Baker and Wurgler (2002); Hovakimian (2006).
3.1.2. Firm-specific Information
The firm-specific information variables are chosen between those identified by the capital structure theoretical framework and validated by previous empirical studies: firm size, profitability and business risk.
The corporate size effect on the leverage is ambiguous (Rajan & Zingales, 1995). Corporate size has been found to be a positive determinant of capital structure (Agrawal and Nagarajan 1990). The larger the companies are, the less likely they are to go bankrupt. Hence, in a trade-off theory perspective, leverage and corporate size should be positively related. In order to account for the corporate size, the natural logarithm of total assets is commonly considered a proxy for the size of each firm:
(Myers’ 1984) pecking order theory of capital structure revealed that a firm that is profitable is more likely to prefer internal financing rather than external sources. More profitable firms are expected to hold less debt, since it is easier and more cost effective to finance internally. MNCs have better opportunities to earn more profit mainly due to having access to more than one source of earnings and better chances to have favorable business conditions in particular countries (Kogut 1985, Barlett and Ghoshal 1989). Hence, a negative sign must characterize the relation between the leverage ratio and the profitability. However, according to trade-off theory, highly profitable corporations are concerned with fiscal optimization purposes. In order to benefit from debt tax advantages, highly profitable companies would thus increase their leverage. A positive sign of this variable will then suggests fiscal optimization strategies. The variable chosen to measure profitability for each firm is:
Business risk refers to the risk associated with the future operations of the business. This is the risk that is inherent in the expected net operating income stream generated by the assets of the firm (Bishop, Faff, Oliver and Twite 2004). Since higher income volatility (i.e., higher risk) is associated with a higher probability of default, a negative relationship between leverage and risk should be observed (Burgman 1996). From a statistical point of view, the risk assigned to a variable is quantified by its variance or standard deviation. Herein, the business risk is quantified as follows:
3.1.3. Macroeconomic Factors
In this investigation, the firm-specific explanatory factors are also complemented by macroeconomic indicators to form the complete set of analysis on MNC capital structure entering emerging markets. Numerous empirical studies suggested that macroeconomic factors, including economic growth volatility, inflation, exchange rate risk and stock market liquidity may be important considerations in capital structure decisions (Allayannis et. al. 2003; Bancel and Mittoo 2004; Booth et al 2001; Burgman 1996; McClure et al 1999; Thornhill et al 2004).
Leverage is negatively related to GDP volatility (measured as the standard deviation of quarterly GDP growth over a year) and inflation, indicating that an uncertain macroeconomic environment makes it more difficult for firms to borrow. Foreign exchange risk also significantly affects financing decisions. The more sensitive a firm’s cash flows and earnings are to foreign exchange rates, the lower the expected level of debt. MNCs have high exposure to foreign exchange risk which implies that MNCs would have relatively lower leverage. Finally, the liquidity of the domestic stock market can be important in influencing firms’ debt financing decisions. To the extent that long-term debt and equity are substitutes, firms operating in more liquid markets, in comparison with their peers in countries with less liquid stock markets, are likely able to more easily substitute equity for long-term debt, and carry relatively more short-term debt. Simply, leverage is lower when the stock market is more liquid, as firms may find it relatively less expensive to rely on equity financing.
3.2. Descriptive Statistics
This will contain descriptive statistics of the dependent and independent variables used in the regression model. The frequency distribution of corporate leverage ratios, firm-specific characteristics and macroeconomic indicators presented in histogram will be complemented by standard descriptive statistics: mean median, max and min, standard deviation, skewness, kurtosis, and Jarque-Bera.
3.3. Data Source
In this paper, data will be sourced using the Amadeus Database (Bureau Van Dijk). The financial statement data sheets for individual firms with an annual periodicity will be employed. The IMF International Statistics and World Bank’s World Development Indicators (WDI) provides data on country and year-specific macroeconomic variables.
Moreover, annual data will be analyzed covering the period 2000 to 2007 to ensure that there is sufficient data available for analysis, and to provide a superior set of results, compared to a study covering a narrower time period. The data for the different variables considered in the study was presented and/or summarized in tabular and graphical forms.
Section 4: Methodology
4.1. Correlation Analysis
Because the statistics are cross-sectional time sequence, cross-sectional pool time sequence examination is applied to evade pitfalls of applying cross-sectional substitutes for time-series variables. Moreover, White Heteroscedasticity Test is performed to approve for the chances of heteroscedasticity
Before the model is evaluated and tested by regression analysis, it is essential to first identify if a relationship exists between MNC capital structure and the individual explanatory variables. Paired coefficients of correlation, r, therefore will be calculated.
Correlation is a measure of the between two or more variables. Correlation coefficients can range from -1.00 to +1.00. The value of -1.00 represents a perfect negative correlation while a value of +1.00 represents a perfect positive correlation. A value of 0.00 represents a lack of correlation. The most widely used type of correlation is Pearson r, also called linear or product-moment correlation. As a rule of thumb, a strong correlation or relationship has an r-value range of between 0.70 to 1.00, or -0.70 to -1.00. In a moderate correlation, the r-value ranges from 0.40 to 0.70 or, -0.40 to -0.70. In a weak correlation, one that is not a very helpful predictor, r ranges from 0.20 to 0.40 or -0.20 to -0.40. Though an entirely random relationship equals, 0.00, any relationship that has a correlation r-value that is 0.19 and below is negligible or not considered to be a reliable predictor (Fisher 2004).
4.2. The Empirical Model
This paper hypothesizes that capital structure of MNCs entering emerging markets are related with and affected by firm-specific information and macroeconomic factors. Hence, the model is structured as follows:
(+) (-) (-) (+) (-) (-) (-)
CAPSTRUC = f (SIZE, PROFIT, BRISK, GDPGRW, INF, FOREX, SMLIQUID)(1)
CAPSTRUC = corporate leverage ratio
SIZE = firm size
PROFIT = profitability
BRISK = business risk
GDPGRW = GDP growth rate
INF = inflation rate
FOREX = foreign exchange rate risk
SMLIQUID = stock market liquidity
Intuitively, the above functional model means that MNC capital structure are affected by firm size, profitability, business risk, economic growth, inflation rate, foreign exchange rate risk and liquidity of the stock market. The algebraic sign atop each of the explanatory variables indicates the expected influence of each of these variables on MNC financing decisions.
A pooled cross-sectional time series regression model will be used to analyze MNC capital structure determinants. Thus, the functional model in its stochastic form can be mathematically expressed as follows:
CAPSTRUCit = 0+1SIZE1it+2PROFIT2it+3BRISK3it+4GDPGRW4t
|CAPSTRUCit||=||Leverage ratio of the ith company at tth time period|
|SIZEit||=||Firm size of the ith company at tth time period|
|PROFITit||=||Profitability of the ith company at tth time period|
|BRISKit||=||Business risk of the ith company at tth time period|
|GDPGRWt||=||GDP growth rate at tth time period|
|INFt||=||Inflation rate at tth time period|
|FOREXt||=||Foreign exchange rate risk at tth time period|
|SMLIQUIDt||=||Stock market liquidity at tth time period|
|0,1,.., 7||=||Parameter estimates|
|uit||=||Stochastic disturbance term|
4.3 Panel Data Analysis
Panel data analysis presents several advantages for the treatment of economic problems where cross-sectional variation and dynamic effects are relevant. (Hsiao 1986) raises three advantages possessed by panel data sets: since they provide a larger number of data points, they allow increase in the degrees of freedom and reduce the collinearity among explanatory variables; they allow the investigation of problems that cannot be solely addressed by either cross-section or time series data sets; and they provide a means of reducing the missing variable problem. (Baltagi 1995) adds to these the usually higher accuracy of micro-unit data respective to aggregate data and the possibility of exploring the dynamics of adjustment of particular phenomenon overtime.
The main approach to fitting the above regression equation using panel data is the fixed effects approach or least-squares dummy variable (LSDV) regression model. LSDV as suggested by Hsiao (1986) is particularly appropriate in situations where N (the number of cross-sectional units) is large relative to T (the number of time periods) – precisely the case of this study.
The Generalized Method of Moments (GMM) for dynamic panel-data estimation will be employed in order to control for the simultaneity and endogeneity problems, inherent when manipulating corporate balance sheet data. Dynamic panel models permit to explain the current level of the dependent variable in function of its past realizations (Arellano and Bond 1991).
In the present study, the structure of the underlying panel data is in stacked form. Panel data can be arranged in stacked form, where all of the data for a variable are grouped together, but separated from data for other variables. In the most common form, the data for different cross section are stacked on top of one another, with each column representing a variable (i.e., data are stacked by cross section):
|MNC||Period||CAPSTRUC||Firm Specific Information||Macroeconomic Indicators|
In the present study, the stacked data are balanced. This means that each cross-section unit has the same number of time series observations. In all, therefore, this study will have 488 observations.
The main approach to fitting Equation (2) using panel data is the fixed effects approach or least-squares dummy variable (LSDV) regression model:
|CAPSTRUCit||=||1+2D2+ 3D3+…+ D61+1SIZE1it|
|D2||=||1 if the observation belongs to TTD International SAS, 0 otherwise|
|D3||=||1 if the observation belongs to Huron Graffenstaden, SAS 0 otherwise|
|D61||=||1 if the observation belongs to Jain (Europe) Limited, 0 otherwise|
Since there are sixty one (61) MNCs, the researcher used only sixty dummies to avoid falling into the dummy-variable trap (i.e., the situation of perfect collinearity). Here there is no dummy for NIEF Plastic SA. In other words, 1 represents the intercept of NIEF Plastic SA and 2,3,4,5, …,61, the differential intercept coefficients, tell by how much the intercepts of other MNCs differ from the intercept of NIEF Plastic SA. In short, NIEF Plastic SA becomes the comparison company.
Section 5: Markets Description
5.1. The Indian Market
5.1.1. An Emerging Economic Powerhouse
India is the world’s second most populous country, with 1.1 billion people. Occupying more than three quarters of the land mass of South Asia, it is an emerging economic superpower. The economy of India is the twelfth largest in the world with a GDP of around $1 trillion. It recorded a GDP growth rate of 9.1% for the fiscal year 2007–2008 which makes it the world’s second fastest emerging economy, after China and the world’s third largest stock exchange in terms of transactions. The Indian economy one of the fastest growing in the world expanded by an average of 5.8% annually from 1993 to 2002 GDP growth accelerated to more than 7% in 2003 and 2004, and to 9.2% in 2005 and 2006.
Services are a growing sector and play an increasingly important role in India’s economy. The advent of the digital age, and the large number of young and educated populace fluent in English, is gradually transforming India as an important ‘back office’ destination for global outsourcing of customer services and technical support. India is a major exporter of highly-skilled workers in software and financial services, and software engineering. Other sectors like manufacturing, pharmaceuticals, biotechnology, nanotechnology, telecommunication, shipbuilding, aviation, tourism and retailing are showing strong potentials with higher growth rates.
India’s services output is growing fast, 7.5% in 1991–2000 up from 4.5% in 1951–80. It has the largest share in the GDP, accounting for 55% in 2007 up from 15% in 1950. Business services like information technology, information technology enabled services, business process outsourcing are among the fastest growing sectors. The growth in the IT sector is attributed to increased specialization, availability of a large pool of low cost, but highly skilled, educated and fluent English-speaking workers.
The behavior of GDP growth and other macroeconomic indicators of India covering the period 2000-2007 are illustrated in Figure 1 (Source IMF).
Macroeconomic Performance, 2000-2007
5.1.2. Foreign Trade and Investment
India’s rapid economic growth started after the government gradually opened the economy in the early 1990s. The government instituted economic reforms and reduced control over foreign trade and investment.
India currently accounts for 1.2% of World trade as of 2006 according to the World Trade Organization (WTO). Until the liberalization of 1991, India was largely and intentionally isolated from the world markets, to protect its fledging economy and to achieve self-reliance. Foreign trade was subject to import tariffs, export taxes and quantitative restrictions, while foreign direct investment was restricted by upper-limit equity participation, restrictions on technology transfer, export obligations and government approvals.
Although India is still a net importer, since 1996–97, its overall balance of payments has been positive, largely due to increased foreign direct investment. As per the CIA fact book in 2007, imports were $224bn and exports $140bn. foreign currency reserves stood at $285 billion in 2007, which could be used in infrastructural development of the country if used effectively. India’s major trading partners are China, the US, the UAE, the UK, Japan and the EU.
Due to some positive economic reforms aimed at deregulating the economy, India has positioned itself as a preferred destination for foreign direct investments in the Asia Pacific Region. Industrial policy reforms have substantially reduced industrial licensing requirements, removed restrictions on expansion and facilitated easy access to foreign technology and foreign direct investments. In March 2005, the government amended the rules to allow 100% FDI in the construction business.
FDI inflows into India reached a record US$19.5 billion in fiscal year 2006/07 (April-March), according to the government’s Secretariat for Industrial Assistance. This was more than double the total of US$7.8bn in the previous fiscal year. The FDI inflow for 2007-08 has been reported as $24 billion. (Source: Business Wire 2008)
5.1.3. More Economic Reforms
India faces a fast-growing population and poverty remains a serious problem. According to World Bank’s estimates on poverty based on 2005 data, India has 456 million people, 41.6% of its population, living below the new international poverty line of $1.25 (PPP) per day. From 1991 to 2006, real income per person rose by around 90% in local currency terms. The World Bank classifies India as a low-income economy with per capita income of $2,659, measured by PPP, and $978, measured in nominal terms (revised 2007 estimate, IMF).
For India to sustain its growth momentum and lift more people out of poverty, more economic reforms are needed.
6. Russian market
Since the Russian Revolution in 1989, a major objective of the successive governments was the privatization of 6,200 state enterprises. The economy was completelyrestructured, with the emphasis on private ownership and adherenceto the market for the allocation of resources. By latest 1996,nearly all the country’s agricultural landhad been returnedto private ownership, but only 65% of all eligible recipients had been officiallygiven ownership title. By 2002, Russia had privatized many major state-owned enterprises, with the help of the World Bank, International Monetary Fund (IMF), and the EU. Nonetheless, an estimated 45% of industrial assets remain owned by the state, particularly in the energy and mining sectors. The private sector in 2002 accounted for an estimated 65% of gross domestic product (GDP).
Bank privatization did not begin until 1998 and proceeded slowly with the share of state-owned bank assets exceeding 50% through the late 1990s. The largest state- owned banks were overburdened with non-performing loans, which reached 70% of total loans for some banks. Some 2% of GDP was allocatedfor bank restructuring in
1999. Government support for banks substantially increases domestic debt.
The macro-financial environment of Russia is typical for countries moving from centrally planned to market-oriented economies. Since 2000, the government has implemented macroeconomic policies which are supportivefor growth. A disciplined fiscal policy, which complemented a tight monetary policy led to improved financial discipline in the enterprise sector and placed public finances and the financialsystem on much firmer footing. These improvements resulted in GDP growthof above 7% for five consecutive years (2003-2007). In addition, inflation declined steadily (during the years 2003-2007 it has seen a low of 2,3% and a high of 6,3%), the fiscal deficit was broughtunder control, foreignexchange reserves increased to historic highs and external debt was held at a comfortable level.
Foreign direct investment in Russia has increaseddramatically, growing by 600% since 2000 at around $ 13.6 billion or $ 2.540 per capita by the end of 2004.
From (Modigliani & Miller 1958, 1963) work, a growing attention has been given to themanner in which enterprises adjust or fail to adjust their leverage. The manner of adjustment is important because it can help distinguishing between different theories of capital structure. These theories, namely, the trade-off, peckingorder and agency theories were developed to explain the influence of each factor on the firms’ capital structure choices.
For Russia market the determinants of capitalstructure include: tangible assets, firm size, profitability and growth opportunities. .
A growing attention has been given to the manner in which enterprises adjust or fail to adjusttheir leverage. The manner of adjustment is important because it can help to distinguish different theories of capital structure. Regarding these theories, (Miller’s 1977) theory of tax neutralityto the static theory, which postulates the optimum debt level as a consequence of a trade-off between the tax advantages of borrowed money and financial distress costs.
The first theories are known as classical theories of capital structure and they were followed by so-called modern theories which are scientifically more refined.
6.2 Modigliani and Miller Theorem
The modern theory of capital structure starts with the papers of (Modigliani & Miller 1958) which demonstrate that the market value of an enterprise is independent of its capital structure that is the debt-equityratio. This conclusion was based on the restrictive assumptions that the capital market is perfect and there is no taxation. According to this theory there cannot be an optimal capital structure, becausethe value of the enterprise is unaffected by the manner in which that enterprise is financed. But, in reality firms have very different capitalstructures and the Modigliani and Miller theorem is very difficult to be tested directly.
When corporatetaxes are introduced in equation,Modigliani and Miller proposition proposal changestotally. These authors(1958, 1963) showed that the firm value is a function of leverage in a market with corporate tax. Wheninterest payments are tax deductible and payments to shareholders are not, the capital structure which maximizes firm value implies 100% debt financing.
The Modigliani and Miller model is a startingpoint for the more realistic models which represent classical theories of capital structure.
The trade-off theory- The traditional version of the trade-off theory emerged from the discussion about (Modigliani & Miller 1958) theorem. (Miller 1977) added tax effects into the original framework and concludedthat firms should financetheir projects just through debt in order to maximize corporate value. But, in reality was demonstrated that debt represents only a fractionof the firms’ total capital. Subsequentliterature (Myers,
1984; Kraus & Litzenberg 1973) assumes that the optimal capital structure results from a trade-off between the benefits of tax shield of debt and the costs of financial distress of debt. The benefitsof debt result from its tax deductibility, whichimplies that a higher debt ratio will increase the firm’s value. These benefits can be offset by costs of financial distress, costs that reduce the market value of the firm.
(De Angelo and Masulis 1980) and (Ross 1985) have showed that, in the world of taxes, it is advantageous for a firm with more tangible assets and a lot of taxable income to have a high debt-equity ratio in order to avoid high tax payments.On the trade-off theory perspective for the firms with more intangible assets and less income it is better to rely on equity financing. Firms with high profitability and growth opportunities tend to borrow less (Harris & Raviv, 1991; Rajan & Zingales 1995).
(Myers 2003) considers that the empirical support for the trade-off theory is not as strong as it looks for two reasons: firstly, statistical results consistent with the trade- offtheory can be consistent with other theories as well and secondly, there are many successful, highly profitable firms which have low debt ratios.
Pecking order theory- The peckingorder theory was developed by (Myers 1984) and it is focusedon asymmetric information costs. Asymmetric information means that external investors do not have access to all information regarding the value of the firm’s assets and growth opportunities. The information asymmetry may also explain why existing investors do not favor new equity financing. The cause is that the new investors may require higher returns to compensate the risk of their investment and this request dilutes the returns of existing investors.
(Myers 1984) and (Myers & Majluf 1984) stated that a firm followsthe pecking order theory if it prefers internal to external financing and debt to equity when external financing is used. In their opinion there is no capital structure that could maximize the firm value. In contrast to the trade-off theory, the pecking order theory does not clearly define any target debt ratio. The central issue of the theory is a choice between internal and external sources of financing.
The pecking order theory predicts a negative relationship between profitability and leverage.
Agency theory- Some authors include the agency theory into the trade-off theory because the agency costs, which are the basis of thistheory, are considered indirectcosts of financial distress. Agency costs are generated by conflicts of interest at the level of the enterprise.
(Jensen & Meckling 1976) identified two types of conflict. On the one hand, there are conflicts between shareholders and managers which arise because managers hold less than 100% of the residual claim. Managers, as agents for shareholders, will act in their own interests and will seek private benefits such as: high salaries, luxurious offices, perquisites and, in extreme cases, direct capture of assets or cash-flows. But, throughan increase in the debt ratio of the firm, it reduces the amount of free cash- flowavailable to managers to engage in types of the aforementioned pursuits. The mitigation of the conflicts betweenmanagers and shareholders constitutes a benefit of debt financing.
On the other hand, there are conflicts betweenbondholders and shareholders which arise because the debt contract gives shareholders an incentive to invest in risky projects. If the investment yields large returns,the shareholders will capture most of the gain. If the investment fails, bondholders will support the consequences. As a result, shareholders are more interested in investing in very risky projects, even if they are value-decreasing. These investments determine a decreaseof debt value of the firm. The cost of the incentive to invest in value-decreasing projects created by debt is supported by the shareholders who issue the debt. This effect is called the asset substitution effect and represents the agency costs of debt financing.
(Jensen & Meckling 1976) argued that an optimal capital structure can be obtained by trading off the agency costs of debt against the benefit of debt.
Market timing- Some authorshave focused recently on the impact of equity market timing on capital structure. (Baker & Wurlger 2002), for example, argue that firms adjust their capital structures to target structures in response to changes in the firms’ market values. Firms will tend to issue equity when the market value of their shares is high, and to repurchase equity when the share prices are low. The conclusion is that there should be an inverse relationship betweencapital structure and market value, and the adjustment of the capitalstructure of its target level is a long process.In the opinion of (Frank & Goyal 2004) considerable theoretical development and empirical validation are needed before this theory can stand as an independent one.
Tangible assets- Trade-off theory suggests that tangible assets are used as security for bondholders in the event of financial distress. (Jensen & Meckling 1976) suggested that tangible assets protect lenders from the moral hazard problem caused by the shareholder- bondholder conflict. (Rajan & Zingales 1995), (Chen 2004) and (Delcoure 2005) found significant positive relationships between asset tangibilityand the firm’s debt structure. The asset tangibilitycould be defined as the ratio of tangible assets to total asset.
Firm size- The majority of the studiesagree that firm sizeis an important determinant of capital structure and that large firms are more likely to be debt-financed in comparison with smaller firms. There are a number of reasonsfor this fact. One of the reasons is mentioned by (Rajan & Zingales 1995) who suggested that larger companies tend to be more diversified and, thus, less prone to bankruptcy.
Another reason is stipulatedby the pecking order hypothesis which states that larger firms exhibitlower information asymmetry with financial markets and are able to issue more equity compared to small companies. In Romania the firm sizecan be measured either trough the number of employees or trough net sales.
Profitability- (Jensen & Meckling 1976) stated that managers of a profitablefirm will attempt to reduce agency costs of equity by increasing the firm’s debt ratio. According to pecking order theory companies retain financial funds in order to be able to subsidizeprojects with positive net present value with internally generated funds. This idea is also reiterated in (Myers & Majluf 1984) work, which states that more profitable firms will have a lowerdebt ratio. Working in the same direction, (Rajan& Zingales 1995), (Wald 1999) and (Chen 2004) found statistically significant negative relationships between profitability and the debt ratio.
Growth opportunities- (Myers 1977) observed that high growth firms may hold more optionsfor future investments than low growth firms. This statement is congruentwith pecking order theory which argues thatthe high growth firms should use less debt for financing, (Myers and Majluf 1984). Furthermore,according to the trade-off theory, firms with great growth opportunities tend to borrow less than firmsholding more tangible assets, because growth opportunities cannot serve as tangible assets.
Conclusions- Russian firms had low leverage ratios. The negative relationship betweenleverage and tangibility might be explainedby the lack of long- term debt financing and contradictsthe trade-off theory. The more profitable companies had less debt than less profitable ones, result indicated also by the pecking order theory. The relationship between leverage and company size is a positive one andprovides some indication of the importance of the credits for the Russian enterprises. The coefficient for growth opportunitiesis not statistically significant, which means that either the measure for this factor was not appropriate or this determinant do not influence the capital structure of the sample of Russian firms. Finally we can say that the peckingorder theory explain partial the financial decisions of the Russian MNC’s firms.
It is not difficult to understand that China has different institutional structures from developed as well as many developing countries. For example, in the world of Modigliani and Miller, tax should have no effect on firms’ capital structure in a command economy. This is because in China the government or state is the owner of firms and banks, as well as the beneficiary of tax. The proxies for financial crisis cost (size and volatility) in China firms are expected to have less or no effects on capital structure.
According to static tradeoff models, the optimal capital structure does exist. A firm is regarded as setting a target debt level and gradually moving towards it. The firm’s optimal capital structure will involve the tradeoff among the effects of corporate and personal taxes, bankruptcy costs and agency costs, etc. Firms are said to prefer retained earnings (available liquid assets) as their main source of funds from investment. Next in order of preference is less risky debt, and last comes risky external equity financing. It is so because the existence of the asymmetric information problem between insider and outsider investors.
Debt ratios change when there is an imbalance of internal cash flow, net of dividends, and real investment opportunities while the factors considered in the tradeoff model are regarded as the second-order. (ShyamSunder1999) and (Myers1999) claim that tradeoff model can be rejected and pecking order model has much greater time-series explanatory power than tradeoff model by testing the statistical power of alternative hypotheses.
Theoretical and empirical studies have shown that profitability, tangibility, tax, size, non-debt tax shields, growth opportunities, volatility, and so on affect capital structure. On the relationship between these factors and companies’ capital structure, (Harris and Raviv1990) summarizing a good number of empirical studies from US firms, suggest that leverage increases with fixed assets, non-debt tax shields, investment opportunities and firm size and decreases with volatility, advertising expenditure, the probability of bankruptcy, profitability and uniqueness of the product.” However, recent studies have updated our understanding about the determinants of capital structure. For example, (Wald 1999) shows that leverage decreases rather than increases with non-debt tax shields.
Profitability- Although much theoretical work has been done since (Modigliani & Miller 1958) no consistent predictions has been reached of the relationship between profitability and leverage.
However, pecking order theory suggests firms will use retained earnings first as investment funds and then move to bonds and new equity only if necessary. In this case, profitable firms tend to have less debt. Agency-based models also give us conflicting predictions. On the one hand, (Jensen 1986) and (Williamson 1988) define debt as a discipline device to ensure that managers pay out profits rather than build empires. For firms with free cash flow, or high profitability, high debt can restrain management discretion. On the other hand, (Chang 1999) shows that the optimal contract between the corporate inside and outside investors can be interpreted as a combination of debt and equity, and profitable firms tend to use less debt.
Tangibility- On the relationship between tangibility and capital structure, theories generally state that tangibility is positively related to leverage. In their pioneering paper on agency cost, ownership and capital structure, (Jensen and Meckling 1976) point out that the agency cost of debt exists as the firm may shift to riskier investment after the issuance of debt, and transfer wealth from creditors to shareholders to exploit to the option nature of equity. If a firm’s tangible assets are high, then these assets can be used as collateral, diminishing the lender’s risk of suffering such agency costs of debt. Hence, a high fraction of tangible assets is expected to be associated with high leverage. Also, the value of tangible assets should be higher than intangible assets in case of bankruptcy. (Williamson 1988) and (Harris and Raviv1990) suggest leverage should increase with liquidation value and both papers suggest that leverage is positively correlated with tangibility.
Tax- The impact of tax on capital structure is the main theme of pioneering study by (Modigliani and Miller, 1958). Almost all researchers now believe that taxes must be important to companies’ capital structure. Firms with a higher effective marginal tax rate should use more debt to obtain a tax-shield gain. However, (MacKie-Mason,1990) comments that the reason why many studies fail to find plausible or significant tax effects on financing behaviors, which is implied by Modigliani and Miller theorem, is because the debt/equity ratios are the cumulative result of years’ of separate decisions and most tax shields have a negligible effect on the marginal tax rate for most firms. MacKie-Mason, contrary to other researchers, studies the incremental financing decisions using discrete choice analysis. He focuses especially on the effect of taxes (tax loss carry-forwards and investment tax credit) upon the debt-equity choice conditional on going public, and finds that the desirability of debt financing at the margin varies positively with the effective marginal tax rate, which is consistent with MM theorem.
Size- Many studies suggest there is a positive relation between leverage and size. (Marsh 1982) finds that large firms more often choose long-term debt while small firms choose short-term debt. Large firms may be able to take advantage of economies of scale in issuing long-term debt, and may even have bargaining power over creditors. So the cost of issuing debt and equity is negatively related to firm size. However, size may also be a proxy for the information that outside investors have. (Fama and Jensen1983) argue that larger firms tend to provide more information to lenders than smaller ones. (Rajan and Zingales1995) argue that larger firms tend to disclose more information to outside investors than smaller ones. Overall, larger firms with less asymmetric information problems should tend to have more equity than debt and thus have lower leverage.
Volatility -Volatility or business risk is a proxy for the probability of financial distress and it is generally expected to be negatively related with leverage. However, (Hsia 1981), based on the contingent claim nature of equity, combines the option pricing model (OPM), the capital asset pricing model (CAMP), and the Modigliani-Miller theorems to show that as the variance of the value of the firm’s assets increases, the systematic risk of equity decreases. So the business risk is expected to be positively related with leverage. Several measures of volatility are used in different studies, such as the standard deviation of the return on sales (Booth et al. 2001) standard deviation of the first difference in operating cash flow scaled by total assets., or standard deviation of the percentage change in operating income. All these studies find that business risk is negatively correlated with leverage. In this study, we follow (Booth et al.,2001) in using standard deviation of earnings before interest and tax to measure volatility.
Conclusions- The forces working on firms’ capital structure in other countries also work in a quite similar way in China. Although China is still transforming its economy from a command economy to a market-based economy and the state is still the controlling shareholder for most listed companies, the factors which affect firms’ leverage in other countries also affect Chinese companies’ leverage in a similar way. Specifically, leverage, as measured by long-term debt ratio, total debt ratio and total liabilities ratio, decreases with profitability and increases with company size. Tangibility has a positive effect on long term debt ratio. Firms that have experienced quick sales growth rate tend to have higher leverage while firms that have bright growth opportunities tend to have less leverage.
Why do Chinese firms have such a low long-term debt ratio? One possible reason is that Chinese firms prefer and have access to equity financing once they go public as most firms enjoy a favorable high stock price.
8. Brazilian Market
Multinational companies (MNCs) have gathered interesting pieces of evidence regarding both financing decisions and the ability to shift income from high- to low-tax jurisdictions. It is well-known, indeed, that income can be shifted by means of debt policies, and that the amount of income shifted depends on tax rate diﬀerentials. Moreover we know that debt policies are affected not only by tax factors but also by other determinants, such as distress costs and risk.
(Modigliani and Miller, 1958) derived a model that under some drastic simplifications stated that capital structure does not affect the company’s value. In their conclusion, the assumptions used in their approach were a necessary step to start to solve the problem, and “they can now be relaxed in the direction of greater realism and relevance”.
Assumption – Perfect Capital Market- There are no transaction costs to investors and firms when they issue or trade securities; bankruptcy likewise involves no costs; there are no taxes; and there are no costs in keeping a firm’s management decision rules set by its security holders.
Equal Access- Individuals and firms have equal access to the capital markets. This means that the types of securities that can be issued by firms can be issued by investors on personal account. Moreover, the prices of securities are determined by the characteristics of their payoff streams and not by whether they are issued by investors or firms. Equal access could logically be included as a characteristic of a perfect capital market, but it plays such an important role in capital structure.
Complete Agreement or Homogeneous Expectations- Any information available is freely available to all market agents (investors and firms), and all agents correctly investigate the consequences of the information for the future prospects of firms and securities.
Only wealth Counts- Aside from effects on security holder wealth, the financing decisions of a firm do not affect the characteristics of portfolio opportunities available to investors. Thus the effect of a firm’s financing decisions on the welfare of its security holders can be equated with effects on security holder wealth.
Given Investment Strategy- To center on the impacts of a company’s financing decisions on the welfare of its security holders, all proofs of capital structure propositions take the investment strategies of firms as given. Although decisions to be considered in the future are mysterious, the regulations that company use to make current and future investment decisions are given. In addition, investment decisions are made independently of how the decisions are financed.
One of the reasons for corporations to not take 100% debt in their capital structure is the risk of insolvency. Since this ‘risk of ruin’ is probably not linear but increasing with higher levels of debt, firms can limit their leverage to avoid incurring bankruptcy costs (Baxter, 1967; Warner, 1977). (Hughes, Logue, & Sweeney 1975) point out asset diversification as one of the main reasons for multinational firms to exist (Agmon and Lessard 1977) and (Fatemi 1988) argue that international diversification increases debt capacity due to a reduction in bankruptcy costs. Risk is reduced by portfolio effects, since foreign cash flows are not perfectly correlated (Shapiro, 1978).
Internal capital flow in MNCs can impact their capital structure in two different ways, according to (Doukas and Pantzalis, 2003). They argue that the international activity affects debt constraints faced by MNCs due to differences in agency costs and information asymmetries.
First, diversified firms can use internal capital flows to make investments with positive NPVs that otherwise would be overlooked by the lack of external credit. On the other hand, more debt can be used as internal capital markets tend to have fewer problems with asymmetric information.
The exposure to foreign exchange can affect corporate risk. Cash flows generated by foreign operations can be considered as transaction exchange exposure, and can be hedged. For instance, (Fatemi, 1988) suggests that this risk can be eliminated by matching the maturity and currency denomination of cash inflows and outflows. (Burgman 1996) argues that there is also exposure to economic exchange rate, which is harder to measure and hedge. Since markets are integrated, even domestic corporations can be affected by foreign exchange. Foreign exchange can be unfavorable due to pressures on price implied by foreign competitors or supplier’s side; MNCs tend to show much more flexibility by shifting production to low-cost areas. For this reason, MNCs can be less sensitive than DCs to variations in foreign exchange rate.
Political risk- International subsidiaries can suffer interference from local government, such as expropriation and nationalization (Shapiro, 1978). Although this tends to raise capital cost, MNCs are inclined to rely on large amounts of debt in the foreign-subsidiary to minimize this risk. As noted by (Burgman, 1996), the risk of political actions taking place overseas cannot be overlooked, and they also have a distinct nature and are quite tough to diversify; consequently, the riskier the countries where MNC operate the greater their debt ratio tends to be. Government control over capital flow is also part of the political risk faced by MNCs (Fatemi, 1988).
Agency costs- (Jensen and Meckling, 1976) put together the concepts of agency costs and ownership and control. Inside the firm, agency problems can emerge from conflicts of interest between managers, outside equity owners and debt-holders. Managers can make decisions in order to maximize their own utility, consuming perquisites and deviating from the objective of maximizing the value of the firm. As a result, the benefits of debt should be counterbalanced by the emergence of expenditure monitoring and bonding costs. Since MNCs are geographically diversified, agency costs tend to be more pronounced on MNCs than DCs. Information asymmetries and monitoring costs are higher, since it is harder to gather and process information from geographically diversified firms. For this reason, higher agency costs will make MNCs less prone to have debt in their capital structure (Doukas & Pantzalis, 2003). (Myers, 1977) presented a special case of agency cost, the underinvestment problem. He argues that borrowing risky debt will make managers, by acting in the interest of shareholders, choose a suboptimal investment strategy – situations in which positive NPV projects that would maximize firm value are not accepted, because they just transfer value from shareholders to debt-holders. The more similar the investment opportunities are to options, the greater is this problem. Therefore, companies with many available growth options tend to have little or no debt in their capital structure. According to (Kim and Lyn, 1986), MNCs tend to have more growth opportunities, which results in more constraints to debt financing.
The internationalization measure- Based on the trade-off theory, (Fatemi 1988) argued that the impact of international activity is the net effect between additional agency costs and lower bankruptcy costs and found that MNCs have target leverage ratios significantly below those of their domestic counterparts.
To support the reduction in debt financing costs, (Shapiro 1978) says that MNCs can be better off due to portfolio effects originated by the diversification of foreign cash flows, which with less than perfect correlations among company earnings and / or asset values in various countries help to reduce bankruptcy costs. The MNC is more diversified than their DCs counterparts, and therefore, its returns are less correlated with the market and its systematic risk is lower. In addition, MNCs have the ability to arbitrage segmented capital markets, obtaining lower cost of debt (Errunza & Senbe 1981). In the presence of barriers to portfolio capital flows, MNCs have an advantage over DCs due to the ability to internationally diversify, and according to (Agmon and Lessard 1977), investors recognize the extent of multinational diversification.
Non-linear specification- (Mansi and Reeb 2002) suggest that MNCs use less debt despite benefiting from lower cost of debt, by assuming a non-monotonic relation between firm international activity and both cost of debt and leverage. According to (Mansi and Reeb 2002) the benefits of international activity are more pronounced in early stages of international activity. Many of the benefits of diversification and the ability to arbitrage segmented capital markets can be obtained with expansions to a few countries. However, exchange risks and agency costs tend to be lower for low levels of international activity, although when MNCs are present in a greater number of markets, such costs tend to become higher.
Conclusion- Brazilian MNCs use more debt due to international activity that increases both short and long-term leverage. A difference in the usage of foreign currency debt by MNCs is expected, as they can have access to cheaper debt (Faulkender & Petersen, 2006; Miller & Puthenpurackal, 2002; Mittoo & Zhang, 2008), or alternatively, motivated by credit scarcity in the company’s home country and also used as a manner to match assets and liabilities in order to control foreign exchange risk.
9. Presentation and Examination of Results
9.1. Descriptive Statistics
Table 1 contains descriptive statistics of the dependent and independent variables used in the panel regression model.
Descriptive Statistics for India
|Sum Sq. Dev.||7157635.00||523.98||1282143.00||725389.30||2128.90||131352.50||2055.01||20.99|
Table 1 provides some surprising results. Characteristics of MNCs differ significantly on all variables. In relation to capital structure, MNCs have an annual average of €44.14 million shareholders-liquidity ratio. For the period covering 2000-2007, Complete Coffee Limited recorded the highest shareholders-liquidity ratio of €959 million. On the other hand, Jain (Europe) Limited recorded the lowest capital structure ratio of -€8.36 million.
In regards to the independent variables, MNCs size as measured by their total assets is on average more than €4.50 billion. The average value of the multinationals ROA is negative 1.61, indicating low profitability of MNCs. This means that MNCs are generating fewer profits with their available assets. Business risk, as proxied by the standard deviation of profit margin, is significantly high indicating that MNCs are having difficulty in turning over their products at a profit. High business risk is associated with a high probability of default.
In summary, the results are generally similar to many US studies. Particularly important is our finding that MNCs entering emerging markets have significantly different capital structure ratios.
9.2. Correlation Analysis
Before the model is evaluated and tested by regression methods, it is essential to first identify if a relationship exists between capital structure and the individual explanatory variables, and subsequently to test the significance of such relationship. Paired coefficients of correlation (r) between MNC capital structure and each explanatory variable were therefore calculated for the model. The results are shown in Table 2.
|1. Firm Size (SIZE)||-0.0898|
|2. Profitability (PROFIT)||0.0610|
|3. Business Risk (BRISK)||-0.0083|
|1. GDP growth volatility (GDPGRW)||-0.0024|
|2. Inflation rate (INF)||-0.0209|
|3. Foreign exchange rate (FOREX)||-0.0077|
|4. Stock market liquidity (SMLIQUID)||0.0252|
Dependent Variable: Capital Structure (CAPSTRUC); n = 488
The results show that PROFIT, GDPGRW, INF and SMLIQUID correlated positively with CAPSTRUC. On the other hand, SIZE, BRISK and FOREX correlation coefficients indicate an inverse relationship with capital structure. Ignoring the sign, the correlation coefficients also indicate a very weak association of MNCs capital structure with respect to firm size (0.0898), profitability (0.0610), business risk (0.0083), GDP growth rate (0.0024), inflation rate (0.0209), foreign exchange rate (0.0077) and stock market liquidity (0.0252). Any relationship that has a correlation r-value that is (0.19), and below is negligible or not considered to be a reliable predictor (Fisher 2004) The corresponding correlation patterns of capital structure with firm-specific information and macroeconomic variables are shown in Figure 2.
9.3. Panel Regression
The results of the panel data regression using fixed effects or least squares dummy variable (LSDV) regression model are summarized in Table 3 given below:
Panel Regression Results
|Durbin-Watson stat||1.8702||Probability (F-statistic)||0.0000|
1/ Dependent Variable: CAPSTRUC
2/ Critical Values: t-statistics (10%=1.6450; 5%=1.9600; 1%=2.5760)
F-statistic (5% = 1.39; 1% = 1.60)
Durbin-Watson: 5% (dL=1.6540, dU=1.8850); 1% (dL=1.5610, dU=1.7910)
The residual plot of the pooled regression is given in Figure 3. Notice the absence of a pattern in the plot, which indicates that a linear regression function is appropriate. In general, the plot shows that the points seemed to fall in a narrow horizontal band, indicating constancy of error variances.
Furthermore, there is an unusually long spike in the behavior of capital structure between 170 and 230 observations. This could be a reflection of the significantly high values of shareholders liquidity ratio recorded for a number of MNCs included in this investigation (i.e., Associated Coffee Merchants (International) Limited, NIIT Technologies Limited, Virgin Radio Limited, etc.).
Actual and Fitted Values and Residuals
The results of the panel data regression analysis denote that firm size in terms of the log of MNC total asset has a significant effect on capital structure. Since the calculated t-value of 2.3908 is greater than the critical t-value of 2.5760 at = 0.01. Thus, the elasticity of CAPSTRUC with respect to SIZE is -47.2383, suggesting that if firm size measured goes up by 1 percent, on average, MNCs capital structure in terms of shareholder-liquidity ratio goes down by about €47.2383 million.
The regression equation shows that capital structure in terms of shareholder liquidity ratio increases by €0.0970 million for every 1 percentage point increase in profits as measured by the return on assets (ROA). The computed t-value of 1.0963, which is less than the critical t-values at 10 percent, 5 percent and 1 percent level of significance, indicates that PROFIT does not significantly affect CAPSTRUC.
Business risk, which is measured in terms of the standard deviation of profit margin, has no significant effect on CAPSTRUC given a computed t-value of 0.2942 in absolute term, which is less than its critical t-statistics value of 1.9600 at 5 percent level of significance. The partial slope coefficient of -42.8095 measures the responsiveness of capital structure with respect to MNCs business risk. Specifically, this number states that, holding the other independent variables constant, if BRISK increases by 1 percent, on the average, CAPSTRUC declines by about €42.8095 million.
GDP growth rate volatility significantly affects CAPSTRUC of MNCs entering the emerging market of India as shown by the absolute computed t-value of 2.0182, which exceeds the critical t-value of 1.96 at 5 percent level of significance. The estimated GDPGRW coefficient is -12.4776, meaning that if GDPGRW volatility goes up by 1 percentage point; CAPSTRUC also goes down by about €12.4776 million, ceteris paribus. As the panel data regression shows, GDPGRW is negatively related to GDP growth rate volatility, which is in accord with a priori information.
The regression equation also reveals that CAPSTRUC decreases by €1.1999 million for every 1-percentage point increase in INF. The computed t-value of 1.9499 in absolute term, which is greater than the tabular t-value at 5 percent level, indicates that inflation rate significantly affect MNCs capital structure, which is not surprising. A rise in inflation reduces the purchasing power of MNCs and thus should have a negative impact on their financing decisions.
Regarding the effect of exchange rate on capital structure of MNCs, the result showed the depreciation of the Indian local currency against the U.S. dollar would decrease shareholder liquidity ratio by about €3.3970 million. FOREX has no significant effect on CAPSTRUC as evidenced by the calculated absolute t-value of 0.9818 with probability value of 0.3268.
Stock market liquidity measured in terms of the log of market capitalization significantly affects CAPSTRUC as shown by the t-value of 1.7121 which far exceeds the critical t-value of 1.6450 at 10 percent level of significance. As the panel regression shows, SMLIQUID is positively related to CAPSTRUC, which is not in accord with the study’s theoretical expectation. This could be explained by the fact that stock markets promotes alternative financing options for MNCs to have greater flexibility in raising funds to boost business activity.
Based on the t-ratio results, the hypothesis that the individual effect on MNC capital structure of the above firm-specific information and macroeconomic indicators are not significant is rejected in so far as firm size, GDP growth volatility, rate of inflation, and stock market liquidity are concerned. In the case of profitability, business risk and foreign exchange rate, their t-values indicate that the hypothesis of having no significant effect has to be accepted.
The estimated equation also exhibited “goodness of fit” with an adjusted R2 of 0.4713. This means that 47.13 percent of the total variation in MNC capital structure is accounted for or explained by the regression line or equation fitted on the given data. The combined effect on capital structure of the above-mentioned firm specific information and macroeconomic indicators are significant based on the consideration that the computed F-statistics of 34.6749 is far greater than the critical F-statistic of 1.60 at 0.01. This implies also that the model is significant. Thus, the firm specific information and macroeconomic factors selected collectively influenced the MNC capital structure overtime.
To detect the problem of serial correlation, the Durbin-Watson Statistics was utilized. The computed d-statistic of 1.8702 indicates the absence of both positive and negative autocorrelation on the basis that it is closer to the limiting value of two (2) for absence of autocorrelation. The absence of autocorrelation makes the estimates efficient.
As can be seen from Table 4, the estimated slope coefficient for all MNCs included in this study are not statistically significant, as the probability values of their estimated t-statistics are extremely large. These imply that their mean capital structure measured in terms of the shareholder liquidity ratio is about the same. Simply, the mean capital structures are not statistically significantly different.
Fixed Effect Results
|Variable||Coefficient||Std. Error||t-Statistic||Prob.||Variable||Coefficient||Std. Error||t-Statistic||Prob.|
However, the intercept values of the sixty one (61) MNCs are statistically different, being 17.4475 for NIEF Plastic SA, -884.7441 (=17.4475-902.1916) for TTD International SAS, -129.8402 (=17.4475-147.2877) for Huron Graffenstaden SAS and so on. These differences in the intercepts may be due to unique features of each MNC company, such as size, profitability, business risk, and financial leverage.
10. Discussion of Results
According to a priori information, the corporate size effect on capital structure is ambiguous (Rajan and Zingales, 1995). In this study, it showed a negative coefficient on CAPSTRUC contradicting the findings of (Agrawal & Nagarajan 1990) and in turn the trade-off theory perspective. This may be explained by the fact that corporate size might be regarded as a proxy for the availability of financial information. Since outside investors are likely to prefer equities over debt, the availability of detailed financial information (larger companies have to provide more information on their financial choices) might be regarded as an obstacle to a higher leverage. Thus, corporate size could also be negatively related to firms’ capital structure (i.e., financial leverage). The finding on the size factor is in line with the Asymmetric Information Signaling Framework which hypothesizes that firm size negatively affects financial leverage in terms of short term debt, (Hall et. al. 2004).
Although PROFIT is insignificant, it contradicted the predicted sign of having a negative influence on CAPSTRUC (Myers 1984; Kogut 1985; Barlett and Ghoshal 1989). The profitability represents a key variable in financing choices. There is no doubt, the higher the profitability is, all else being equal, the lower the external financing needs are, hence the debt and the equity. The shareholder value maximizing corporations must do everything possible in order to increase the shareholders wealth. Hence, the greater the dividends are, the greater the need for external finance will be. From a pecking order theory perspective, under informational asymmetries, the privileged external finance source will be the debt. On the other hand, the dividends payment leads to lower agency costs of equity. Firms can therefore raise more equity, leading to a lower leverage ratio.
However, according to trade-off theory, highly profitable corporations are concerned with fiscal optimization purposes. In order to benefit from debt tax advantages, highly profitable companies would thus increase their leverage. A positive sign of this variable will then suggests fiscal optimization strategies. (Frank and Goyal 2003) argued that according to the trade off theory the profitable firms should be more highly levered to offset corporate taxes.
The risk associated to a high volatility of the income leads, under both the pecking order theory and the trade-off theory, to lower leverage ratios. According to (Burgman 1996), higher income volatility (i.e., higher risk) is associated with a higher probability of default, a negative relationship between leverage and risk should be observed.
The significant negative relationship of CAPSTRUC on GDPGRW and INF is underscored by understanding that uncertain macroeconomic environment makes it more difficult for MNCs to borrow in their host countries. (McClure et al. 1999) found that companies’ capital structures are still significantly different by nationality for the G7 countries (Canada, France, Italy, US, Germany, Japan, UK), and suggested that macroeconomic factors, including economic growth and inflation, may be important considerations in capital structure decisions and cause of difference. (Booth et al. 2001) examined the capital structure of 10 developing countries to assess country’s effect on firm’s capital structure decision and found that there are significant differences in the ways in which the decision is affected by country factors such as GDP growth rate, inflation rate, and the development of capital market. The countries examined were India, Pakistan, Thailand, Malaysia, Turkey, Zimbabwe, Mexico, Brazil, Jordon, and Korea. Baker and (Jeffrey 2002), found that low leverage firms are those that raised funds when their market valuations were high, as measured by the market-to-book ratio, while high leverage firms are those that raised funds when their market valuations were low.
The rapid expansion in international trade and the adoption of freely floating exchange rate regime by many emerging markets heralded a new era of increased exchange rate risk and volatility. Not surprisingly, the economic exposure of MNCs to exchange rate risks has increased. In the aggregate sense, the MNCs should respond to the excess movement and increasing volatility of exchange rates.
The more sensitive a firm’s cash flows and earnings are to foreign exchange rates, the lower the expected level of debt. (Burgman 1996) analyzed the relation between foreign exchange risk and corporate financing decisions and reported that foreign exchange risk significantly affects financing decisions. Further, exchange rate movements affect both the cash flows of a firm’s operations and discount rates employed to value the cash flows (Bartov et al 1996). MNCs have high exposure to foreign exchange risk which implies that MNCs would have relatively lower leverage. However, foreign exchange risk is a risk commonly hedged by firms. Although MNCs may have greater exposure to foreign exchange risk, the risk may be hedged.
Stock markets may affect economic activity through the creation of liquidity. Many profitable investments require a long-term commitment of capital, but investors are often reluctant to relinquish control of their savings for long periods. Liquid markets make investment less risky and more attractive, because they allow savers to acquire an asset or equity and to sell it quickly if they need access to their savings or want to alter their portfolios. At the same time, companies enjoy permanent access to capital raised through equity issues. By facilitating longer-term, more profitable investments, liquid markets improve the allocation of capital and enhance prospects for long-term business growth. Further, by making investment less risky and more profitable, stock market liquidity can also lead to more investment. (Stenbacka and Tombak 2002) argued that capital structure depend on more basic ingredient such as the nature of the capital markets, the characteristics of investment opportunities available to the firm, and the internal funds.
This thesis attempts to investigate the capital structure of multinational corporations entering the emerging BRIC market of Brazil, Russia, India and China based on firm-specific information and macroeconomic determinants.
Characteristics of MNCs differ significantly in terms of firm-specific information. The correlation results show that profitability, GDP growth volatility, inflation rate and stock market liquidity correlated positively with capital structure. On the other hand, firm size, business risk and foreign exchange rate risk correlation coefficients indicate an inverse relationship with capital structure. However, the correlation coefficients also indicate a very weak association.
The results of the panel data regression analysis denote that firm size, GDP growth volatility, rate of inflation, and stock market liquidity significantly affects MNC capital structure as measured by the shareholder liquidity ratio. In the case of profitability, business risk and foreign exchange rate, the hypothesis of having no significant effect has to be accepted.
12. Limitations of the Study and Areas for Future Research
This paper presented an empirical analysis of MNCs capital structure entering the emerging BRIC countries market. Multinational companies from France, Germany, Italy, Switzerland and UK were used as the sample firms. In this paper, the annual balance sheet data for individual firms were employed covering the period 2000 to 2007 to ensure that there is sufficient data available for analysis.
I feel a study covering MNC data from the United States, would be useful in providing a comparison to the European MNCs data used in this paper. The US is a major trading and strategic partner of India and it would be interesting to see if this close relationship affects the capital structure decisions. Unfortunately, it was not possible to source MNC financial data from the resources at my disposal. Bureau Van Dijk publishes databases that provide this source data but it was cost prohibitive for me to access these.
Furthermore, the firm-specific information variables are chosen between those identified by the capital structure theoretical framework and validated by previous empirical studies: firm size, profitability and business risk. In this investigation, the firm-specific explanatory factors are also complemented by macroeconomic indicators to form the complete set of analysis on MNC capital structure entering emerging markets: GDP growth, inflation rate, foreign exchange rate and stock market liquidity.
A number of bilateral and multilateral agreements with regard to trade and financial policies across countries that have already been actuated and the effects of these initiatives could directly or indirectly be related with firm specific activities and macroeconomic conditions and hence MNC capital structure, therefore, there is the need to examine in greater detail financial market integration among countries. Measuring financial market integration (also volatility spillovers) may provide very important policy implications. For instance, MNCs should be aware to what degree each market is integrated with the rest of the markets in the region, or the world, as it provides information on the direction of flows of international capital across countries.
Moreover, in order to capture a better long-run equilibrium relationship between MNC capital structure and each of the firm-specific information and macroeconomic variables, one should use more advanced econometric techniques like the estimation of a Vector Error Correction Model (VECM). If there is at least one co integrating relationship among the variables, then the causal relationship among these variables can be determined by using the Granger-causality test within the vector error-correction model (VECM) framework. In addition to indicating the direction of causality amongst variables, the VECM also allows us to distinguish between short-run and long-run Granger-causality.
12.1. Key Challenges and Lessons Learnt
The main challenge in carrying out the study was to gather and process the input data for the analysis from a reliable database source. The scope of my research was limited to European data due to limited access to international databases. US data could have been collated in a piece-meal fashion from other databases, but in a large majority of cases data was not consistently provided. This also applies to some European source data, and several multinationals had to be dropped due to insufficient data availability.
The learning also included time management, organization and efficient processing of data and reading materials. The key technical lessons learned include, the use of correlation analysis and the application of panel data analysis using fixed effects approach. The researcher recognizes the immense value that these advanced approaches in estimating the relationship and effects of firm-specific information and macroeconomic variables on MNC capital structure. This study contributes to the existing and emerging literature of MNC capital structure entering emerging markets in several dimensions by testing both the effects of firm specific information and macroeconomic indicators on MNC capital structure in a single study and contributes to the on-going debate on whether firm-specific factors or macro-level economic events are more important determinants of MNC capital structure.
Agrawal, A. and Nagarajan, N. (1990). Corporate capital structure, agency costs and ownership control: The case of all-equity firms. Journal of Finance, 45(4), p1325-31.
Antoniou, A., Guney, Y. and Paudyal, K. (2002). The Determinants of Corporate Debt Maturity Structure. Annual Meeting of the European Financial Management Association 2003, Helsinki. Unpublished Manuscript. 45pp.
Baker, M., Greenwood, R. and Wurgler, J. (2002). The Maturity of Debt Issues and Predictable Variation in Bond Returns. Harvard Business School Working Paper, Helsinki. Unpublished Manuscript. 42pp.
Barclay, J., Marx, M. and Smith Jr., W. (2003). The Joint Determination of Leverage and Maturity. Journal of Corporate Finance, 9(1), p149-167.
Barclay, J. and Smith Jr., W. (1995). The Maturity Structure of Corporate Debt. Journal of Finance, 50(2), p609-631.
——— (1996). On Financial Architecture: Leverage, Maturity, and Priority. Journal of Applied Corporate Finance, 8, p4-17.
Barlett, A. and Ghoshal, S. (1989). Managing across borders: The transnationals solution, Cambridge, MA, Harvard Business School Press.
Bartov, E., Bodnar, G. and Kaul, A. (1996). Exchange rate variability and the riskiness of US multinational firms: Evidence from the breakdown of Bretton Woods. Journal of Financial Economics 42, p105-32.
Berger, G. andOfek, E. (1995). Diversification’s effect on firm value. Journal of Financial Economics, 37, p 39-65
Bhaumik, S., and Gelb, S. (2003). Determinants of MNC’s Mode of Entry into an Emerging Market:Some Evidence from Egypt and South Africa. Available: http://www.london.edu/assets/documents/PDF/determinants_of_mnc.pdf.
Booth, L., Aivazian, V., Demirgüç-Kunt, A. and Maksimovic, V. (1999). Capital Structures in Developing Countries. Unpublished Manuscript. 53pp.
Bradley, M., Jarrell, G. and Kim, E. (1984). On the Existence of an Optimal Capital Structure: Theory and Evidence. Journal of Finance,39, p857-78.
Burgman, T. (1996). An empirical examination of multinational corporate capital structure, Journal of International Business Studie, 27(3), p553-57.
Cesário Mateus, C. and Terra, P. (2005). Capital Structure and Debt Maturity: Evidence from Emerging Markets. Working Paper. Available: http://www.fma.org/SLC/Papers/Capital_ Structure_and_Debt_Maturity_Evidence_from_Emerging_Markets.pdf
Cassar, G. and Scott, B. (2003). Capital structure and financing of SMEs: Australian evidence. Accounting and Finance Journal, 43(2), p123–147.
Cheng, S. and Shiu, C. (2007). Investor protection and capital structure: International evidence. Journal of Multinational Financial Management, 17(1), p30-44.
Choi, J. and Prasad, A. (1995). Exchange Risk Sensitivity and its Determinants: A Firm and Industry Analysis of US Multinationals. Financial Management, 24(3), p77-88.
Desai, M., Foley, F. and Hines, J. (2003). A Multinational Perspective on Capital Structure Choice and Internal Capital Markets. The Journal of Finance, 59(6), p2451 – 2487.
Deesomsak, R., Paudyal, K. and Pescetto, G. (2004). The determinants of capital structure: evidence from the Asia Pacific region. Journal of Multinational Financial Management, 14(4-5), p387-405.
Demirgüç-Kunt, A. and Maksimovic, V. (1996). Stock Market Development and Financing Choices of Firms. The World Bank Economic Review, 10(2), p341-369.
——— (1999). Institutions, Financial Markets, and Firm Debt Maturity. Journal of Financial Economics 54, p295-336.
Doukas, A. and Pantzalis, C. (2003). Geographic diversification and agency costs of debt of multinational firms. Journal of Corporate Finance, 9, p59-92.
Eiteman, D., Stonehill, A. and Moffett, M. (2001). Multinational Business Finance, 9th edition, Addison-Wesley, USA.
Errunza, V., and Senbet, L. (1984). International corporate diversification, market valuation and size-adjusted evidence. Journal of Finance, 34, p727-745.
Easterwood, C. and Kadapakkam, P. (1994). Agency Conflicts, Issue Costs, and Debt Maturity. Quarterly Journal of Business and Economics, 33(3), p69-80.
Evrensel, A. and Kutan, A. (2007). Are multinationals afraid of social violence in emerging markets?: Evidence from the Indonesian provinces. Journal of Economic Studies, 34(1), p59 – 73.
Fama, E.F. and French, K.R. (2002). Testing Trade-Off and Pecking Order Predictions about Dividends and Debt. Review of Financial Studies, 15, p1-34.
Fan, H., Titman, S. and Twite, G. (2003). An International Comparison of Capital Structure and Debt Maturity Choices. Unpublished Manuscript. 60pp.
Fisher, Colin (2004). Researching and Writing a Dissertation for Business Students. Harlow, England: Prentice Hall.
Frank, Z. and Goyal, K. (2003). Testing the pecking order theory of capital structure. Journal of Financial Economics, 67(2), p217.
George, R. and Kabir, R. (2005). Corporate diversification and firm performance: Does the organizational form of the firm matter? Paper for presentation at the 2005 FMA Annual Meeting, Chicago.
Gottesman, A. and Roberts, S. (2003). Maturity and Corporate Loan Pricing. Annual Meeting of the European Financial Management Association 2003, Helsinki. Unpublished Manuscript. 39pp.
Grosse, R. (2003). The Challenges of Globalization for Emerging Market Firms. Latin American Business Review, 4(4), p1–21.
Guedes, J. and Opler, T. (1996). The Determinants of the Maturity of Corporate Debt Issues. Journal of Finance 51(1), p1809-1833.
Hall, C., Hutchinson, J. and Michaelas, Nicos (2004). Determinants of the Capital Structures of European SMEs. Journal of Business Finance & Accounting, 31(5 & 6), p711-728.
Harris, M. and Raviv, A. (1991). The theory of capital structure. Journal of Finance, 49, p297-355.
Heyman, D., Deloof, M. and Ooghe, H. (2003). The Debt Maturity Structure of Small Firms in a Banking Oriented Environment. Universiteit Gent Working Paper, Ghent. Unpublished Manuscript. 26pp.
Jensen, C. (1986). Agency costs of free cash flow, corporate finance and take-overs. American Economic Review, 76, p323-39.
Jensen, M. (2006). Firm-Level Responses to Politics: Political Institutions and the Operations of U.S. Multinationals Paper Presented at the International Political Economy Society Conference.
Jodice, D. (1985). Political Risk Assessment: An Annotated Biography. Greenwood Press,Westport.
Johanson, J. and Vahlne, E. (1977). The Internationalization Process of the Firm-A Model of Knowledge Development and Increasing Foreign Market Commitments. Journal of International Business Studies, 8(1), p23-32.
Johanson, J. and Wiedersheim-Paul, F. (1975). The Internationalization of the Firm: Four Swedish Cases. Journal of Management Studies, 12(3), p305-322.
Johnson, A. (1997). An Empirical Analysis of the Determinants of Corporate Debt Ownership Structure. Journal of Financial and Quantitative Analysis, 32(1), p47-69.
Kaldor, E. (2005). The Costs of Foreignness in an Emerging Market: Profitability among Hungary’s Commercial Banks. Paper presented at the annual meeting of the American Sociological Association, Marriott Hotel, Loews Philadelphia Hotel, Philadelphia, PA Online <PDF> Retrieved 2008-07-18 from http://www. allacademic.com/ meta/p21868_index.html.
Kogut, B. and Singh, H. (1988). The Effect of National Culture on the Choice of Entry Mode. Journal of International Business Studies, 19(3), p411-432.
Kogut, B. (1985). The Multinational Corporations in 1980’s, MIT Press, Cambridge, MA.
Kumar, K. and McLeod, G. (1981). Multinationals from Developing Countries. Lexington, Mass.: Lexington Books/DC Heath and Company.
Kwok, Y. and Reeb, M. (2000). Internationalization and Firm Risk: An Upstream-Downstream Hypothesis. Journal of International Business Studies, 31(4).
Lecraw, D. (1977). Direct Investment by Firms from Less Developed Countries. Oxford Economic Papers, 29(3), 442-457.
Lee, K. and Kwok Y. (1988). Multinational corporations vs. domestic corporations: International environmental factors and determinants of capital structure. Journal of International Business Studies, 19, p195-217.
Lewellen, W. (1971). A pure financial rationale for the conglomerate merger. Journal of Finance 26, p521-537.
Luo, Y. (2002). Determinants of Entry in an Emerging Economy: A Multilevel Approach. Journal of Management Studies, 38(3), p443 – 472.
Lyandres, E. and Zhdanov, A. (2003). Underinvestment or Overinvestment? The Effect of Debt Maturity on Investment. William E. Simon Graduate School of Business Administration Working Paper. Unpublished Manuscript. 45pp.
Madj, S. and Meyers, C. (1987). Tax asymmetries and corporate income tax reform. In Fedstein M. (ed.), Effects of taxation on capital accumulation. University of Chicago Press, Chicago, Ill.
Michaelas, N., Chittenden, F. and Poutziouris, F. (1999). Financial policy and capital structure choice in U.K. SMEs: Empirical evidence from company panel data. Small Business Economics, 12, p113-30.
Mitchell, K. (1991). The Call, Sinking Fund, and Term-to-Maturity Features of Corporate Bonds: an Empirical Investigation. Journal of Financial and Quantitative Analysis, 26(2), p201-222.
——— (1993). The Debt Maturity Choice: an Empirical Investigation. Journal of Financial Research, 16(4), p309-320.
Modigliani, F. and Miller, H. (1958). The cost of capital, corporation finance and the theory of investment. American Economic Review, 53, p261-97.
Morris, R. (1992). Factors Affecting the Maturity Structure of Corporate Debt. University of Colorado at Denver. Unpublished Manuscript.
Myers, C. (2001). Capital Structure. Journal of Economic Perspectives, 15(2), p81-102.
Ozkan, A. (2000). An Empirical Analysis of Corporate Debt Maturity Structure. European Financial Management, 6(2), p197-212.
Porter, E. (1987). From competitive advantage to corporate strategy. Harvard Business Review, 65, p43-59.
Rajan, G. and Zingales, L. (1995). What do we know about capital structure? Some evidence from international data. Journal of Finance 50(5), p1421-60.
Reeb, D., Kwok, C. and Baek, H. (1998). Systematic risk of the multinational corporation. Journal of International Business Studies, 29(2), p263-279.
Rejie George, R. and Kabir, R. (2005). Corporate Diversification and Firm Performance: Does the Organizational Form of the Firm Matter? Paper for presentation at the 2005 FMA Annual Meeting, Chicago.
Shapiro, C. (1996). Multinational Financial Management, 5th edition. Prentice-Hall, New Jersey.
Scherr, C. and Hulburt, M. (2001). The Debt Maturity Structure of Small Firms. Financial Management, p85-111.
Schiantarelli, F. and Sembenelli, A. (1997). The Maturity Structure of Debt: Determinants and Effects on Firms’ Performance. World Bank Policy Research Working Paper, WPS 1699. Unpublished Manuscript. 39pp.
Smith, A. and Valderrama, D. (2008). The Composition of Capital Inflows When Emerging Market Firms Face Financing Constraints Federal Reserve Bank of San Francisco http://www.frbsf.org/publications/economics/papers/2007/wp07-13bk.pdf
Stenbacka, Rune and Tombak, Mihkel (2002). Investment, Capital Structure, and Complementarities Between Debt and New Equity. Management Science, 48(2), p257-272.
Stohs, M. and Mauer, C. (1996). The Determinants of Corporate Debt Maturity Structure. Journal of Business, 69(3), p279-312.
Paulo R.S. Terra, (2005) Determinants of Corporate Debt Maturity in Latin America, Social Science Research Network
Ting, L. and Schive, C. (1981). Direct Investment and Technology Transfer. Multinationals from Developing Countries. Lexington, Mass., DC Health and Company, 101-114.
Wells, T. (1983). Third World Multinationals: The Rise of Foreign Investment from Developing Countries, MIT Press.