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Institute for International Integration Studies IIIS Discussion Paper No.305 / November 2009 Emerging Markets Capital Structure and Financial Integration Brian M. Lucey, QiYu Zhang School of Business, Trinity College Dublin, Ireland IIIS Discussion Paper No. 305 Emerging Markets Capital Structure and Financial Integration Brian M. Lucey, QiYu Zhang∗ School of Business, Trinity College Dublin, Ireland Disclaimer Any opinions expressed here are those of the author(s) and not those of the IIIS. All works posted here are owned and copyrighted by the author(s). Papers may only be downloaded for personal use only. ∗ Corresponding author: E-mail addresses: [email protected] (Q. Zhang), [email protected] (B. M. Lucey) Emerging Markets Capital Structure and Financial Integration Brian M. Lucey, QiYu Zhang∗ School of Business, Trinity College Dublin, Ireland Abstract This paper studies the impact of international financial integration on corporate financing choices, at a country level. Examining publically quoted firms of 24 emerging economies over the 1995-2007 period, we find that greater bond market integration is associated with increased leverage and a longer debt maturity. Measuring credit market integration via bank loan channels we find that increased integration leads to shorter debt maturity. In comparison with other firms, large firms tend to have higher leverage and longer debt maturity, and firms in common law countries have a longer debt maturity as financial integration increases. We find relatively little impact on emerging market firms capital structure choices when we examine equity market integration. Keywords: International financial integration; Emerging market firms; Capital structure 1. Introduction Emerging economies have witnessed waves of international financial integration since the late 1980s. The economic implications of this integration have attracted substantial research. Many papers have addressed the impact of integration on different metrics at country level such as economic growth, cost of capital, and information environment.1 Conceptually, not only country-level metrics such as economic development and legal structure but also financial integration could affect the capital structure of firms in emerging economies. In this paper, we investigate the effects of proxy measures of financial integration, namely, international bond issues, foreign bank loans and a measure of international equity market integration, on the leverage ratio and debt maturity structure of emerging market firms. There are at least two channels by which financial integration could influence corporate financing choices. First, financial integration brings about a global financial environment, where local firms can issue securities or borrow funds through foreign capital markets or intuitions if their home countries have limited capital. The emergence of new financing resources may thus result in a change in capital structure if firms source funds internationally. Second, financial integration prompts international risk diversification and increases competition between domestic and foreign intermediaries. Consequently, the cost of external capital decreases (Bekaert and Harvey, 2000; Henry, 2000; Gianetti et al., 2002). Taking these effects, a general prediction would be an increased use of debt capital by local firms as financial integration proceeds to higher levels. Although financial integration may also affect the equity finance of firms – for example, by reducing the market-level cost of equity capital – this study focuses on the debt side. Our study is closely related to Schmukler and Vesperoni (2006) and Ağca et al. (2007). The former examines 686 public firms in Latin America and East Asia to find that by accessing international equity and bond markets, firms increase their long-term debt and extend their debt maturity. However, financial liberalization decreases the use of long-term debt and debt maturity shifts to shorter term. The latter finds that international financial openness results in higher leverage ∗ 1 Corresponding author: E-mail addresses: [email protected] (Q. Zhang), [email protected] (B. M. Lucey) See, for example, Bae et al. (2006), Bekaert and Harvey (2000), Bekaert et al. (2005). for both emerged and emerging market firms. However, openness results in opposite effects on debt maturity, which is lengthened in developed countries but shortened in developing countries. Additionally, the authors find that financial liberalization has a positive impact on both leverage and debt maturity in developed countries, while has a negative impact on leverage and no significant impact on debt maturity in developing countries. The evidence thus remains somewhat inconclusive. We contribute to the literature from three perspectives. First, we expand the sample size to construct a panel of 6258 emerging market firms during the period of 1995 to 2007. This compares with 7 countries from 1980 to 1998 in Schmukler and Vesperoni (2006), and 16 developing countries from 1994 to 2002 in Ağca et al. (2007). Second, suggested by prior works, we include a wide range of firm- and country-level factors that might predict corporate financing choices. Doing this helps us to control for factors that might otherwise be conflated with integration. For instance, levels of financial integration could merely reflect the development of domestic financial markets (Gianetti, 2002), so it is necessary to control for this when examining integration effects. Third, we attempt to address the documented differential effects of financial integration by introducing interaction terms of integration with both firm and country characteristics. To test interaction effects, we propose two situations under which the effects of accessing foreign bank loans are more pronounced. We argue that in comparison with other firms, large firms and firms from common law countries react differently. On the one hand, large firms are exposed to lower bankruptcy risk, lower monitoring costs and supply more useful information to external investors (Demirgüç-Kunt and Maksimovic, 1999; Fama, 1985). Moreover, large firms are inherently better known domestically and internationally than small firms (Kang and Stulz, 1997). These advantages make it easier for large firms to borrow more debt and particularly long-term debt from foreign banks. On the other hand, the quality of institutions is a crucial factor known to affect the attitude of foreign lenders. Stulz (2005) argues that the positive impact of financial globalization has been surprisingly limited due to the existence of agency problems that arise because leaders of sovereign countries and corporate insiders pursue their own interests at the expense of outside investors. Qian and Strahan (2007) find that foreign banks are sensitive to the legal and institutional environment as their willingness of lending to local firms is reversely related to creditor protection. In this study, we choose common law country as a benchmark which has better shareholder/creditor protection and more efficient law enforcement than other legal origin country, as suggested by La Porta et al. (1998) et seq. Our empirical results indicate some evidence for the importance of financial integration. We find that credit market integration as measured by international bond issues has no significant impact on leverage, but does have a significantly impact on debt maturity, increasing maturity. We find that measured by greater foreign bank loans the effect is higher leverage and a shorter debt maturity for the average firm. Interestingly, in comparison with other firms, foreign creditors are more willing to lend long-term debt to large firms and firms from common law countries. We conduct two sets of tests to check the robustness of these results. Using a reduced sample excluding countries that contribute significantly larger relative numbers of firms, we find that the effect of international bond issues on the leverage ratio becomes positive and significant, while other differences are minor. We also address the issue of causality. This is due to the concern that aggregate levels of capital structure could to some extent reflect the levels of financial integration over time. The results show that, while we need to interpret the results with caution, change of capital structure is at least partially due to integration as opposed to the other direction. The remainder of the paper is structured as follows. Section 2 provides a brief review of works on capital structure and emerging markets, section 3, describes the dataset and methods, section 4 presents the regression results and robustness checks. The concluding remarks are given in the final section. 2 2. Brief review of country-level determinants of capital structure2 This paper belongs to the growing group of empirical studies that emphasize country-level determinants of capital structure. In their early investigation of seven developed countries, Rajan and Zingales (1995) suggest that the effects of institutional features such as tax code, bankruptcy laws, the state of development of bond markets, and patterns of ownership should be addressed in future research. Examining 10 developing countries, Booth et al. (2001) find that there are persistent differences in leverage across countries, and conclude that, in order to determine the capital structure of a firm, knowing the country of origin is usually as important as knowing the size of direct explanatory variables. Generally, there are three relevant perspectives of country-level factors, namely financial institutions, legal institutions, and cultural dimensions. Regarding the effects of financial institutions, Demirgüç-Kunt and Maksimovic (1996) explore the impact of financial market development on firms’ financing choice in 30 industrial and developing economies from 1980 to 1991. They find that in general there is a significant positive relationship between credit market development and leverage and a negative but insignificant relationship between stock market development and leverage. Furthermore, they find that in emerging stock markets, the leverage of large firms are positively related to the level of stock market development and the leverage of small firms does not appear to be significantly affected by stock market development. Using several measures of financial development, Rajan and Zingales (1998) find that a high level of financial market depth reduces the cost of external finance to firms and therefore facilitates the growth of firms that need relatively more external finance. More recently Giannetti (2003) and Mitton (2007) find that firms become less leveraged as stock market develops within the country. Turning to legal institutions, Demirgüç-Kunt and Maksimovic (1999) suggest a positive relationship between the efficiency of a country's legal system and the use of long-term debt and debt maturity, particularly for large firms. Giannetti (2003) finds that a low quality of law enforcement and lack of creditor rights protection in the country are associated with a shorter debt maturity of firms. Jiraporn and Gleason (2005) find a negative relationship between strength of shareholder rights and financial leverage, suggesting that firms in environments where shareholder rights are weak carry more debt. Fan et al. (2008) indicate that firms have less total debt and longer debt maturity if legal systems provide better protection for financial claimants. Another branch of research attempts to incorporate the cultural characteristics of countries into empirical studies of corporate financing. Adler (1997) indicates that culture influences people’s values, which in turn affects their attitudes, and then behavior. Hence, a country’s cultural features might influence the behavior of managers within that particular country and eventually be reflected in firms’ financing choice. Using Hofstede (1980) cultural measures, Gleason et al. (2000) classify 14 European countries into four cultural clusters and find that capital structures do vary across these cultural clusters. The authors also argue that in countries with higher level of individualism and uncertainty avoidance, managers should choose lower debt ratios. Sampling from 22 developing and developed countries, Chui et al. (2002) make use of the cultural measures of Schwartz (1994) and find that firms’ leverage are negatively related to the country’s level of conservatism and mastery. 3. Methods, data and preliminary analysis 3.1. The empirical model 2 Firm-level determinants of capital structure have been widely addressed by a large amount of papers, see, e.g. DeAngelo and Masulis (1980), Rajan and Zingales (1995), Booth et al. (2001), Delcoure (2007), Mitton (2007) and De Jong et al. (2008) for more information. We do not review these works for brevity. 3 This section presents our regression model that estimates the effect of financial integration on capital structure, while controlling for a set of firm- and country-level factors. The estimation method is pooled ordinary least squares (OLS) with standard errors clustered by firms to deal with the serial correlation of residuals for a given firm. The baseline model is presented by the following equation: Yi ,c ,t = α + ∑ α s + ∑ α t + β 1′INFINITI c ,t −1 + β 2′ X i ,c ,t −1 + β 3′ N c ,t −1 + ε i ,c ,t (1) Yi,c,t represents the dependent variables, which measure corporate leverage and debt maturity. The subscripts i, c, and t stand for firm, country, and time respectively. α s and α t stand for industry (IBES codes) and time fixed effects. We do not here explicitly control for country fixed effects because several country-level variables are time invariant. INFINITIc,t-1 denotes the lagged value of proxy measures of international financial integration. Xi,c,t-1 and Nc,t-1 stand for lagged vectors of firm-and country-level control variables respectively.3 All time-variant variables are lagged by one period to allow for the non-contemporaneous effects on financing choices. The use of lagged variables also mitigates endogeneity problems. Our dataset consists of 6258 public firms from 24 emerging economies over the period 1995-2007. The emerging economies in our sample are those categorized by MSCI Emerging Markets Index. We omit China as the country is still at the transitional stage towards a market-oriented economy. Following common practice in capital structure studies, we exclude firms in financial sectors and utilities. For all firm-level variables, we remove outliers that lie below the 1% and above the 99% percentiles. Data on firm-level variables are primarily collected from Worldscope. The information on depository receipts issuance is obtained from Bank of New York Mellon. Data on country-level variables are collected from a variety of sources, such as Morgan Stanley Capital International (MSCI), IMF International Financial Statistics, Financial Structure Database of the World Bank, the statistics of La Porta et al. (1998), and Hofstede’s website. All time-variant variables are annually quoted. 3.2. Firm-level variables Table 1 presents the mean and median values of firm-level variables for each country over the sample period. Table 2 provides the details and summary statistics of integration and country-level variables. Corporate leverage is measured by the ratio of total debt (Worldscope item 03255) over total asset (Worldscope item 02999). Debt maturity is measured by the ratio of long-term debt (Worldscope item 03251) over total debt. The three highest mean leverage ratios are observed in Indonesia (33.3%), Thailand (30.8%) and India (29.2%), while the three lowest are observed in Morocco (7.5%), Jordan (12.8%) and Colombia (13.2%). The three highest debt maturity ratios are observed in India (61%), Mexico (59.3%) and Egypt (54.8), while the three lowest are observed in Morocco (27.9%), Turkey (31.4%) and Taiwan (34.5%). Consistent with well-established theoretical and empirical literature, we include several firm-level factors that affect corporate leverage and debt maturity. These are as follows ; SIZE: Firm size is defined as the natural logarithm of sales (Worldscope item 01001). TAN: Asset tangibility is defined as the ratio of net property, plant and equipment (Worldscope item 02501) over total asset. GROWTH: Growth opportunity is defined as market value of total asset, which is total asset minus total equity (Worldscope item 03995) plus market value of equity (Worldscope item 08001) over total asset. PROFIT: Profitability is defined as operating income (Worldscope item 01250) over total asset. NDTS: Non-debt tax shield is defined as 3 The subscript t-1 does not apply to time-invariant variables. 4 depreciation, depletion and amortization (Worldscope item 02201) over total asset. TAX: Effective tax rate is defined as income tax (Worldscope item 01451) over pretax income (Worldscope item 01401). Finally, we construct a one-year lagged dummy variable indicating whether the firm has issued depository receipts (DR) in a given year and this variable gives us 2863 firm-year observations. [Table 1 about here] 3.3. International financial integration and other country-level factors We adopt two de facto measures to gauge the level of financial integration for a given year. They are outstanding international bond issues and outstanding loans from non-resident banks (both as a percentage of GDP). The data come from the Financial Development and Structure Database produced by World Bank. Quantity-based de facto measures try to underline how much a country is integrated with international capital markets in practice (Kose et al., 2006), as opposed to regulatory reductions in cross-border capital flows. The latter is often called de jure measures. There are at least two potential drawbacks to use de jure approach. First, even though governments may open their financial markets at policy level, it is likely that it will take some time before international investors will begin to fully access the newly opened market. Pinpointing a ‘real’ liberalization/integration date is notoriously tricky. Using a reduced-form model, Bekaert et al. (2002) specify a set of liberalization dates for 20 emerging markets and they find these endogenous dates are usually later than official dates. Second, although one can estimate an integration date, it is still necessary to capture the time-varying nature of integration which is widely believed to be the case (Bekaert and Harvey, 1995). Given these concerns, we believe that de facto measures are superior to de jure measures. Besides the variables of interest, we also include stock market linkages and economic openness. It is likely that high levels of financial integration maybe also feature high levels of international stock price comovement and economic openness of the country. These parallel economic phenomena would contain other information but less important in this study. We use the trace test statistics generated from a Johansen cointegration test to measure stock market linkage. Higher statistics are expected to imply higher levels of price comovement in the long run. Details on estimation are given in Appendix A. We use the ratio of total exports and imports over GDP to measure economic openness. We do not have strong predictions regarding the effects of these two variables per se, although we expect that stock market and trade will be correlated with financing behavior through other channels. We include a set of country characteristics as control variables. First, we include two variables to take into account the impact of macroeconomic conditions, represented by GDP per capita and inflation rate. Second, with respect to financial market development, we use two widely used measures: the ratio of stock market capitalization over GDP and the ratio of domestic credit provided to the private sector over GDP. With respect to the legal institution, we construct a common law dummy variable that equals one if the country’s legal system is based on British common law, otherwise zero. We also include a variable that gauge the efficiency of legal system. Third, we incorporate two of Hofstede’s cultural dimensions, namely, individualism and uncertainty avoidance, the effects of which on capital structure are predicted by Gleason et al (2000)4. The correlations among all variables are tabulated in Appendix B. 4 Other measures of culture from Hofstede include masculinity and power distance as well as long-term orientation. For the first two there is no a-priori reason to include these. The second is conceivably very useful but this measure is available only for a small subset of countries, very few of which are included in the dataset here. 5 [Table 2 about here] 4. Regression results Table 3 presents the regression results. Columns (1) and (2) show the impacts of financial integration on corporate leverage and debt maturity while controlling for firm- and country-level factors. Columns (3) and (4) report the results with interaction terms so as to examine whether financial integration has differential effects across different firms. [Table 3 about here] 4.1. Firm-level determinants We find that leverage is positively related to firm size, asset tangibility and depositary receipts programs, while negatively related to growth opportunity, profitability, non-debt tax shields and effective tax rate. The results indicate that firms with larger size and more tangible assets are able to borrow more debt as they have a lower probability of bankruptcy, lower costs in the event of bankruptcy and provide more collateral to lenders relative to other firms. Firms use less leverage when they are more profitable and have better growth opportunity. This agrees with the theoretical arguments of information asymmetries between insiders and outsiders of firms (Myers and Majluf, 1984) and the underinvestment problems (Myers, 1977) respectively. The negative association between non-debt tax shields and leverage suggests that the use of debt finance as tax shields becomes less necessary when firms have other alternatives. We also find the negative relation between tax rate and the use of interest bearing debt while the opposite is more often expected. Booth et al. (2001) note that tax rate is likely to have negative effect on debt ratios because it might serve as an alternative measure of profitability rather than a proxy for debt tax-shields values. Furthermore, we find that firms with depository receipts programs can borrow more debt and thereby have higher leverage ratios. A similar finding is reported in Ağca et al. (2007). The possible reason for this is that global-financed firms are more exposed to the scrutiny of investors. Therefore, the information asymmetry problem is less severe for these firms, and they can obtain more external debt. For debt maturity, the estimates of firm-level factors are largely consistent with previous studies such as Demirgüç-Kunt and Maksimovic (1999), Ağca et al. (2007), and Fan et al. (2008). The ratio of long-term debt to total debt is higher for firms with larger size, greater asset tangibility, higher profits, brighter growth opportunities and depository receipts programs. The advantages of these firms to borrow more long-term debt can be summarized as follows. Larger firms have lower monitoring cost for creditors; firms with greater asset tangibility can readily provide collateral; more profitable firms are less possible to defraud the long-term interest payments; firms with depository receipts program have easier access to developed financial markets which provide more long-term debt. 4.2. Country-level determinants With respect to country-level factors, leverage is positively related to GDP per capita. The result suggests that better economic conditions are conducive for firms to borrow. This result is consistent with the findings of Ağca et al. (2007) and Fan et al. (2008). Stock market development is negatively related to leverage, which is also documented in other studies (Demirgüç-Kunt and Maksimovic, 1996; Giannetti, 2003; Mitton, 2007). We find that better investor protection and a more efficient legal system is associated with higher leverage. This implies that a better legal environment helps local firms to borrow by alleviating the information symmetry costs between insiders and outsiders of 6 firms. Another implication is that emerging market firms generally stay at the bottom of the financing pecking order and increase the use of debt when the external financing becomes feasible. Consistent with the predictions of Gleason et al. (2000), higher levels of individualism and uncertainty avoidance of a country leads to higher corporate leverage. Against theoretical predictions, we find a positive relation between inflation and leverage, and a negative relation between credit market development and leverage. Debt maturity is found to be positively related to GDP per capita, which suggests that more longterm debt is used in wealthy countries. Inflation is negatively associated with maturity, implying that uncertain macroeconomic prospects make it difficult for firms to borrow long-term debt. We also find that debt maturity is longer when investor rights are better protected and the legal system is more efficient, which echoes the finding that debt maturity is longer when laws are better enforced in Demirgüç-Kunt and Maksimovic (1999) and Giannetti (2003). This also supports the argument of Diamond (2004) that lenders use short-term debt in weak legal systems with expensive/ineffective contract enforcement. We find that credit market development is negatively related to debt maturity at 5% level, indicating that better-developed credit markets provide more debt finance to local firms but at shorter maturities. It is likely due to the weak institutions in emerging economies so that creditors lend short term to monitor borrowers’ behavior. The coefficient of stock market capitalization/GDP is positive at 1% level. Advanced stock market may provide more information that is useful to creditors. This makes it less risky to lend to a public firms and increases the ability of firms to obtain long-term credit (Demirgüç-Kunt and Maksimovic, 1999). 4.3. The direct effect of international financial integration Turning to the variable of primary interest, we do not find a significant relation between international bond issues and firm leverage with a low t-statistics of 0.94, shown in column 1 of Table 3. However, we find a significant and positive association between foreign bank loans and leverage at 5% level. The economic magnitude of the effect is reasonably high as well. A one standard deviation increase in the use of foreign loans as a percentage of GDP will lead to a 0.62% increase in the leverage ratio of emerging market firms. To better understand this magnitude, we consider two pole sample countries, one of which has high average proportion of foreign loans to GDP and the other has a low proportion. Thailand has the largest value of 25.3% and India has the smallest value of 4.6%. The estimate in column 1 implies that if India’s weight of foreign loans became identical to that of Thailand, its average leverage ratio would roughly rise by 1.76%. This equals to a 6% increase in the average leverage of 29.2% for India.. This point is consistent with Giannetti and Ongena (2007), where the authors find that foreign bank lending has increased corporate leverage in Eastern European economies. Another possible interpretation is that if financial integration brings more efficient and advanced financial intermediaries (e.g. banks) into less developed economies, then the increased competition between domestic and foreign intermediaries reduce the cost of financial services to the firms of countries with less developed financial systems (Gianetti et al., 2002). Hence, firms adjust their financing policies to use more reduced-cost debt relative to equity and thereby their leverage goes up. Column 2 of Table 3 reports the result of the debt maturity regression. The two financial integration variables are both statistically significant at 1% level. We find that debt maturity is longer, that is, more long-term debt vis-à-vis short-term debt is used when the degree of bond market integration increases. This suggests that the involvement in international debt financing of emerging economies is associated with the easier access to more long-term debt and thus longer debt maturity for domestic firms. The impact is sizable − one standard deviation increase in the issue of international bond as percentage of GDP will lead to an increase of 1.56% in the debt maturity ratio of firms. This result echoes the findings of Schmukler and Vesperoni (2006), where the authors use 7 firms’ access to international bond market as a de jour measure, and include very limited number of country-level variables. On the other hand, the use of foreign banks is negatively related to debt maturity. Specifically, one standard deviation increase in the use of foreign loans as a percentage of GDP will lead to a 2.22% decrease of debt maturity ratio. Although emerging market firms are able to borrow more funds due to integration, it seems that they only obtain these extra funds from creditors at short maturity. This is similar to the findings of Ağca et al. (2007), where they find that greater credit market openness has led to higher leverage but shorter debt maturity in emerging economies. The authors argue that the institutional weakness of emerging economies, such as poor investor protection and accounting opacity, have prevented local firms from making full use of financial integration. A relevant question is therefore to be: what types of firms are more conducive to borrowing long-term debt as international financial integration deepens? We study this issue by examining the interaction effects of foreign bank loans on local firms’ leverage and debt maturity, which will be elaborated on in section 4.2. The F-tests in column 1 and 2 indicate that all variables are jointly significant. The R-squares are at levels (17.8% and 14.8%) comparable to other studies. Hence, our empirical models have some ability to explain the variation of capital structure choices in emerging economies. The results reflect the economic benefits brought by financial integration, such as expanded financing options, and decreased cost of debt. These channels may individually or collectively work on the financing behavior of firms, and it would be interesting to pinpoint the working channels in future research. On top of the impact of financial integration, we also demonstrate the effects of some well-known factors. These factors include firm characteristics, macroeconomic conditions, financial market development, and legal institutions. This is an important by-product of the paper. 4.4. The interactive effects of international financial integration The foregoing analyses have provided evidence on the direct relationship between capital structure and financial integration. In this section, interaction terms are added to the baseline to examine whether integration has differential effects across firms and countries. In order to interact with firm size, we categorize sample firms into two size groups based on their average net sales over the sample period. Large firms and small firms are those with average sales at the top 25% and bottom 25% percentile respectively. To obtain econometrically sound specifications, we also include size group dummy variables in the regressions. For legal origin, we simply interact foreign bank loans with common law dummy variable. We conduct mean equality tests between each possible pair of terms and the null hypothesis of equal mean is rejected for all tests at 1% level. The third column in Table 3 indicates that the positive impact of bank loan integration on leverage found in the baseline regression does not generally exist when we control for size. However, the positive impact is pronounced for large firms, with a significant positive coefficient 0.405 at 1% level. Small firms show no significant effect. This result demonstrates our earlier conjecture that large borrowers have advantages in obtaining more external funds when financial integration rises to higher levels. However, we do not find evidence to support the argument that firms from common law countries should borrow more funds when financial integration deepens. Here, the interaction effect of integration with common law dummy variable only reaches at 0.071 with t-value 0.72. The possible reason for this is twofold. First, the number of qualified countries limits the freedom to test country effects. In our sample, there are only 6 common law countries out of 24. Second, the overlap of different institutional characteristics is likely to restrict our focus on any single perspective of interest. Through legal channels, common law systems may also influence other institutional dimensions. Common law countries tend to have better-developed financial intermediaries and markets than civil law countries (La Porta et al., 1997; Beck et al., 2003), and corporate governance is more transparent in common law countries (Bushman et al., 2004). The interactions of these parallel 8 institutional features with financial integration might work on corporate financing in contradictory ways. For instance, the positive effect of foreign bank loans is likely to be attenuated for firms from common law countries because these firms tilt towards using domestic financial intermediaries that have already developed to relatively high levels. The fourth column in Table 3 shows that the general effect of foreign bank loans remains strongly negative (-0.670) at the 1% level, which is the same as the baseline result. Again, this effect varies significantly with firm size. A positive coefficient 0.618 and significance at 1% level is observed for the interaction term with the large firm dummy variable, while small firms seem less sensitive to integration. Large borrowers have obtained more long-term debt due to financial integration. The result reflects the fact that creditors, especially foreign creditors, find it less risky to lend long-term to large firms. We also observe a positive interaction effect (0.301) with the common law dummy variable at 10% level. This suggests that, at a moderate level, the better investor protection granted by common law countries facilitates the ability of local firms to borrow more long-term debt. Overall, firms with large size and originating in common law countries have better enjoyed the benefits of credit market integration to lengthen their debt maturity structures. Recalling the study of Ağca et al. (2007), the authors find a negative effect of credit market openness on debt maturity in developing countries and the effect remains after distinguishing between financially constrained and unconstrained firms. Therefore the authors conclude that the observed negative effect is due to the institutional weaknesses in emerging economies. Attempting to address the same phenomenon, we explicitly explore the alleged differential effects and confirm Ağca et al’s finding. 4.5. Robustness checks In our sample, Korea, Malaysia, and Taiwan contribute a large amount of firms, with 1013, 921, and 1225 respectively. The number of firms from these three countries exceeds 50 percent of the total amount. With the concern that our results are driven by country effects, we run all model specifications that exclude the firms from the above three countries. The results for the reduced size sample (Panel A of Table 4) indicate that most of our baseline results are not driven by potential sample selection bias. Regarding the impact of financial integration, some changes in significance deserve to be noted here. First, we find a positive impact of international bond issues at 5% level, while positive but not significant in the baseline model. The finding suggests that the issues of international debt securities increase the portion of debt in firms’ capital structure. Second, we find that the interaction between foreign bank loans and common law countries are positive, but not significant as in the previous result. This change is not surprising as the reduction of sample country would further decrease the cross-country difference in institutional features. The effects of other firm and country control variables are largely consistent to the previous findings. We then examine whether the relationship found in the previous specifications is subject to the reverse causality bias. In other words, we care about whether the aggregate levels of leverage ratio and debt maturity also lead to certain levels of international financial integration over time. This could happen as the increased demand for capital by firms results in the increased international bond issues and foreign bank loans at country level. The possibility would cause an endogeneity problem. Although the use of lagged variables has to some extent mitigated this potential problem, we think it necessary to formally investigate the issue as a robustness check. The standard way to address the issue is to estimate the regression model with instrument variables. It would be difficult to identify a proper instrument for financial integration for our case, however, and as such we apply an alternative approach. Our method is to test the causal effect of financial integration on capital structure. As mentioned in the beginning of the paper, one of the major channels through which integration should affect financing is the expanded financing resources. Thus, integration should have weaker impact on 9 firms which have access to international capital sources in other ways. If reverse causality exists, this attenuated effect should not be predicted. We use the depository receipts dummy variable to proxy for firms’ access to foreign capital in a certain year. The objective is to show that financial integration would not affect these firms/years. Panel B of Table 4 reports the regression results that estimate the interaction terms between integration and DR dummy. In all four specifications, the coefficient on the interaction term is either insignificant or significant with opposite sign to the main effect of integration. Thus, the association between financial integration and capital structure is stronger for domestically financed firms/years than globally financed firms/years. The unequal effect suggests that the financial integration at country level causes the increase in the use of debt at firm level. [Table 4 about here] 5. Conclusions In this paper, we test how international financial integration affects corporate leverage and debt maturity structure, while controlling for a variety of determining factors. With extra capital sources and reduced-cost capital, financial integration does affect firms’ financing behavior. The increased use of international debt securities leads to higher leverage and longer debt maturity. The increased foreign bank loans also provide local firms more debt capital sources. In this sense, policy makers in emerging economies should encourage the financial integration with the world system because this effort will help local firms to raise external funds for their investment projects and contributes to the growth of these firms in the long run. On the other hand, the alleged benefits of financial integration seem not to be a ‘free lunch’. We advocate the efforts of authorities on international financial integration, and call for high quality institutions to secure the benefits of integration. This can be seen from the influence of foreign bank loans, which represents creditors’ attitude towards lending to local firms. Specifically, large firms and firms from common law countries benefit more form the expansion of financing options by borrowing at longer maturities. This presents a clear picture of how financial integration generates beneficial financial outcomes. With lower monitoring costs, more transparent information, and better investor protection, foreign creditors are more willing to enter long-term contract with emerging market firms. According to Doidge et al. (2007), it will be costly for firms to improve their governance to protect investors if their countries lack good institutional infrastructure and governance. Hence, for policy makers of emerging economies, the first priority is to improve the quality of institutions and governance at country level to secure the benefits of international financial integration. 10 Appendix A. Johansen cointegration tests To proxy stock market integration, we make use of the trace statistics estimated from the cointegration test originally proposed by Johansen and Juselius (1988, 1990). A large group of studies have applied this method to study the linkage and integration between markets (Yang et al., 2003; Phylaktis and Ravazzolo, 2005; Lucey and Voronkova, 2007). The test can be easily implemented as long as the investigated series have a unit root and are I(1) processes. The investigated series are weekly closing price of MSCI stock market index for emerging markets. For the world benchmark index, we take the MSCI World Index. All indices are expressed in US dollar. The period ranges from January 1994 to December 2007. All stock market indices in our sample are found to be I(1). The model specification is presented as follows: ΔX t = Γ1 ΔX t −1 + ... + Γk −1 ΔX t −k +1 + ΠX t − k + μ + ε t (2) where Xt denotes a vector of stock price indices (p) under consideration (p = 2 in this study), μ is a constant. The lag length (k) is chosen based on the Akaike information criterion (AIC) applied on the undifferenced VAR models. In our case, the model is estimated between each of 24 emerging markets and the world benchmark. We use CATS 2.0 (in RATS 6.3) software package for estimation and obtain the trace test statistics. Higher statistics indicate higher level of integration, while lower statistics suggest the opposite. For the purpose of obtaining continuous values for each year, recursive Johansen cointegration tests (Hansen and Johansen 1993, 1999) are conducted by simply constructing recursive time window of the sample. The time-window strategies are applied as follows: for each bivariate set, the period January 1994 to December 1994 are used as the base estimation period and one-week data is added each time until the end of sample is reached. The annual value is the average statistics of the year. Appendix B. Correlation matrix This table provides Pearson correlation coefficients among all variables. Panel A reports the correlation coefficients for capital structure and other firm-level variables and Panel B reports the correlation coefficients for capital structure and country-level variables. [Table 5 about here] 11 Table 1. Descriptive statistics of firm-level variables Country Argentina Number of firms 61 LEV MAT SIZE TANG GROWTH PROFIT NDTS TAX DR 0.241 0.445 12.224 0.470 0.992 0.056 0.052 0.238 0.120 (0.224) (0.500) (12.001) (0.491) (0.895) (0.054) (0.046) (0.240) (0.000) Brazil 286 0.259 0.447 13.071 0.404 1.050 0.053 0.044 0.262 0.057 (0.241) (0.484) (13.052) (0.397) (0.859) (0.057) (0.037) (0.254) (0.000) Chile 124 0.213 0.534 17.733 0.494 1.202 0.059 0.042 0.168 0.045 (0.214) (0.604) (17.985) (0.493) (1.033) (0.059) (0.036) (0.156) (0.000) Colombia 32 0.132 0.504 19.179 0.488 0.910 0.040 0.033 0.245 0.000 (0.092) (0.559) (19.062) (0.487) (0.787) (0.040) (0.029) (0.240) (0.000) Czech 43 0.192 0.456 15.270 0.534 0.983 0.023 0.063 0.284 0.016 (0.183) (0.426) (15.178) (0.545) (0.848) (0.027) (0.059) (0.284) (0.000) Egypt 34 0.256 0.548 13.605 0.462 1.594 0.106 0.037 0.085 0.059 (0.247) (0.653) (13.444) (0.464) (1.263) (0.100) (0.031) (0.041) (0.000) Hungary 30 0.168 0.406 16.938 0.443 1.178 0.041 0.053 0.111 0.110 (0.158) (0.362) (16.764) (0.442) (0.987) (0.035) (0.049) (0.090) (0.000) India 606 0.292 0.610 15.414 0.392 1.703 0.085 0.036 0.211 0.084 (0.297)) (0.664) (15.406) (0.381) (1.282) (0.084) (0.032) (0.213) (0.000) Indonesia 244 0.333 0.416 19.604 0.419 1.309 0.057 0.043 0.274 0.009 (0.315) (0.399) (19.780) (0.401) (1.075) (0.058) (0.038) (0.286) (0.000) Israel 174 0.211 0.497 12.870 0.221 1.612 0.018 0.039 0.189 0.022 (0.157) (0.542) (12.722) (0.136) (1.314) (0.038) (0.033) (0.157) (0.000) Jordan 18 0.128 0.518 11.107 0.350 1.811 0.093 0.041 0.123 0.043 (0.122) (0.678) (11.435) (0.307) (1.585) (0.081) (0.039) (0.073) (0.000) Korea 1013 0.287 0.394 18.741 0.354 1.120 0.047 0.037 0.234 0.011 (0.277) (0.375) (18.712) (0.346) (0.935) (0.050) (0.031) (0.247) (0.000) Malaysia 921 0.238 0.355 11.958 0.394 1.281 0.040 0.032 0.231 0.003 (0.210) (0.287) (11.837) (0.386) (1.032) (0.043) (0.027) (0.227) (0.000) Mexico 135 0.233 0.593 15.145 0.479 1.169 0.071 0.040 0.287 0.185 (0.222) (0.703) (15.249) (0.531) (1.035) (0.076) (0.034) (0.275) (0.000) Morocco 14 0.075 0.279 14.970 0.309 2.087 0.119 0.061 0.296 0.000 (0.033) (0.024) (14.818) (0.256) (1.620) (0.155) (0.059) (0.297) (0.000) Pakistan 79 0.280 0.390 15.153 0.418 1.356 0.110 0.041 0.315 0.026 (0.257) (0.356) (15.059) (0.405) (1.109) (0.104) (0.038) (0.327) (0.000) Peru 73 0.248 0.421 12.137 0.457 0.845 0.066 0.049 0.279 0.035 (0.230) (0.440) (11.945) (0.451) (0.665) (0.064) (0.041) (0.287) (0.000) Philippines 130 0.233 0.419 14.252 0.444 1.271 0.026 0.216 0.041 0.031 (0.197) (0.431) (14.413) (0.443) (0.988) (0.021) (0.220) (0.035) (0.000) Poland 199 0.166 0.486 12.532 0.361 1.577 0.043 0.049 0.255 0.028 (0.125) (0.470) (12.473) (0.357) (1.304) (0.054) (0.044) (0.215) (0.000) Russia 70 0.223 0.522 16.844 0.551 1.518 0.093 0.051 0.319 0.248 (0.199) (0.571) (16.822) (0.572) (1.251) (0.096) (0.045) (0.276) (0.000) South 429 0.159 0.508 13.444 0.290 1.529 0.083 0.040 0.261 0.052 Africa (0.121) (0.552) (13.505) (0.230) (1.270) (0.093) (0.035) (0.281) (0.000) Taiwan 1225 0.235 0.345 14.951 0.332 1.387 0.053 0.035 0.180 0.021 (0.222) (0.300) (14.846) (0.315) (1.154) (0.047) (0.030) (0.165) (0.000) Thailand 129 0.308 0.376 14.350 0.424 1.283 0.060 0.041 0.178 0.011 (0.303) (0.332) (14.311) (0.428) (1.133) (0.059) (0.035) (0.200) (0.000) Turkey 189 0.205 0.314 11.573 0.353 1.574 0.080 0.052 0.232 0.034 (0.166) (0.235) (11.441) (0.341) (1.284) (0.077) (0.046) (0.224) (0.000) This table presents mean and median (in parentheses) of firm-level variables from 24 countries. LEV: Corporate leverage is the ratio of total debt over total asset, MAT: Debt maturity is the ratio of long-term debt over total debt, SIZE: Firm size is the natural logarithm of net sales in local currency, TANG: Asset tangibility is the ratio of gross fixed assets over total assets, GROWTH: Growth opportunity is the ratio of total asset minus book equity plus market capitalization all over total assets, PROFIT: Profitability is the ratio of operating income over total assets, NDTS: Non-debt tax shields is the ratio of depreciation, depletion and amortization over total assets, TAX: Tax rate is the ratio of income tax over pretax income. DR: DR dummy equals 1 if the firm issue a depository receipts in the last year and otherwise zero. Table 2. Description and summary statistics of country-level variables Variables Description International bond issues The ratio of international bond issues (outstanding) over the country’s GDP, annual data through 1995-2007 (Source: World Bank, Financial Structure Database). Referenced in the database as intldebt The ratio of loans from non-resident banks (outstanding) over the country’s GDP, annual data through 1996-2007 (Source: World Bank, Financial Structure Database). Referenced in the database as nrbloan Trace test statistics from recursive Johansen cointegration test. Take average of weekly estimates to obtain annual data through 1995-2007. Higher statistics indicate the higher levels of integration. See Appendix for estimation details. The ratio of import and export over the country’s GDP, annual data through 19952007. The International trade data is from IMF International Financial Statistics. The natural logarithm of GDP per capita at PPP, annual data through 1995-2007 (GDP per capita at PPP is from IMF International Financial Statistics). The annual return of CPI for each country through 1995-2007 (CPI data is from IMF International Financial Statistics. CPI of Taiwan is from the Directorate General of Budget, Accounting and Statistics (DGBAS), Taiwan. The ratio of stock market capitalization over the country’s GDP, annual data through 1995-2007 (Source: World Bank, Financial Structure Database). The ratio of domestic private credit by deposit money banks and other financial institutions over the country’s GDP, annual data through 1995-2007. (Source: World Bank, World Development Indicators; For Taiwan: we use total domestic credit from Taiwan Banking Survey over GDP). A dummy variable that equals to 1 if the country adopts British common law system and 0 otherwise. (Source: La Porta et al., 1998, 1999). The second component of the World Index focuses on the legal structure and security of property rights of country. The index includes elements such as judicial independence, impartial courts and legal enforcement of contracts. The data are averaged across 1990 to 2006. Higher scores mean more effective legal system. (Source: Economic Freedom of the World, 2008 Annual Report, available at: http://www.fraserinstitute.ca). One of Hofstedes’s cultural dimensions, static data. (Source: http://www.geert-hofstedee.com/hofstede_dimensions.php.) One of Hofstedes’s cultural dimensions, static data. (Source: see above). Foreign bank loans Stock market linkage Economic openness GDP per capita Inflation rate Stock market development Credit market development British common law Efficiency of legal system Individualism Uncertainty avoidance Basic statistics Mean 0.097 Median 0.078 Std. Dev. 0.100 Obs. 297 0.125 0.111 0.077 288 7.403 6.752 3.072 312 0.733 0.602 0.429 311 8.114 8.199 0.969 312 0.093 0.049 0.171 312 0.556 0.353 0.506 312 0.554 0.379 0.419 312 0.250 0.000 0.442 24 5.563 5.978 1.176 24 36.174 37.000 18.686 23 71.652 76.000 17.327 23 Table 3. Determinants of corporate leverage and debt maturity Explanatory variables International financial integration International bond issues Foreign bank loans Foreign bank loans*large firms Foreign bank loans*small firms Foreign bank loans*common law Stock market linkage Firm-level variables Firm size Asset tangibility Growth opportunity Profitability Depository receipt Non-debt tax shields Effective tax rate Large firms Small firms Country-level variables Economic openness GDP per capita Inflation rate Stock market development Credit market development Common law Efficiency of legal system Individualism Uncertainty avoidance Dependent variable Total debt / total asset (1) Long-term debt / total debt (2) 0.037 [0.94] 0.105** [2.05] 0.203*** [3.66] -0.369*** [-5.45] 0.002** [2.12] 0.003** [2.29] 0.009*** [8.22] 0.190*** [15.20] -0.009*** [-3.80] -0.533*** [-23.31] 0.019** [2.48] -0.387*** [-4.59] -0.032*** [-3.96] 0.019*** [12.73] 0.347*** [22.39] 0.018*** [4.39] 0.088*** [2.89] 0.073*** [6.36] -0.071*** [-6.93] 0.022* [1.84] 0.135*** [5.33] -0.024*** [-3.03] -0.010 [-0.84] 0.036*** [3.84] 0.015*** [3.34] -0.002*** [-7.74] -0.002*** [-5.05] -0.072*** [-6.72] -0.001 [-0.06] -0.115*** [-3.12] 0.063*** [6.25] -0.147*** [-9.27] 0.077*** [6.15] 0.047*** [8.17] Total debt / total asset (3) Long-term debt / total debt (4) 0.050 [1.27] -0.025 [-0.34] 0.405*** [4.56] 0.076 [0.68] 0.071 [0.72] 0.0004 [0.44] 0.227*** [4.03] -0.670*** [-5.36] 0.618*** [4.46] -0.204 [-1.36] 0.301* [1.89] 0.002* [1.81] 0.189*** [15.20] -0.010*** [-4.52] -0.513*** [-22.57] 0.028*** [3.69] -0.383*** [-4.59] -0.027*** [-3.28] -0.036*** [-3.35] -0.036** [-2.37] 0.345*** [22.26] 0.016*** [4.15] 0.109*** [3.60] 0.083*** [7.56] -0.079*** [0.72] 0.018 [1.57] 0.128*** [5.09] -0.042*** [-5.24] 0.017 [1.39] 0.030** [2.09] 0.013*** [2.81] -0.002*** [-8.36] -0.002*** [-4.85] -0.075*** [-6.58] -0.009 [-0.94] -0.104*** [-2.85] 0.050*** [4.81] -0.115*** [-7.35] 0.047** [2.26] 0.043*** [7.32] -0.003 [-0.16] -0.040* [-1.85] Constant 0.014 [0.18] -0.230*** [-2.62] 0.215*** [3.10] 0.185** [2.34] F-test 54.81*** 67.93*** 49.65*** 59.07*** Adjusted R-square 0.178 0.148 0.173 0.146 Number of Observation 27639 33190 28055 33721 The table reports the pooled OLS regression results for the two ratios: total debt/total asset and long-term debt/total debt. Columns (1) to (2) report the coefficient estimates of baseline specification. Columns (3) to (4) report the coefficient estimates with interaction terms. We use clustered standard errors by firms. For brevity, we do not report the estimates of industry and year dummy variables. The values of t-statistics are reported in brackets. ***, **, * stand for significance at the 1, 5 and 10 percent levels, respectively. Table 4. Determinants of corporate leverage and debt maturity – robustness checks Explanatory variables Dependent variable Total debt / total asset (1) Long-term debt / total debt (2) Total debt / total asset (3) Long-term debt / total debt (4) Panel A: Reduced sample International bond issues Foreign bank loans Foreign bank loans*large firms Foreign bank loans*small firms Foreign bank loans*common law Constant F-test Adjusted R-square Number of Observation 0.093** [2.00] 0.261*** [3.48] 0.220*** [3.25] -0.606*** [-5.96] 0.078* [1.67] 0.067 [0.52] 0.359*** [2.91] 0.170 [0.93] 0.081 [0.55] 0.204*** [2.91] -0.904*** [-4.29] 0.599*** [3.06] -0.229 [-0.84] 0.209 [0.89] 0.041 [0.49] 27.70*** 0.208 13033 -0.256** [-2.51] 33.95*** 0.152 15523 0.121 [1.46] 25.25*** 0.206 13169 0.195** [2.13] 30.32 0.148 15726 0.032 [0.79] 0.106** [2.07] 0.016* [1.66] 0.041 [0.51] 0.225*** [3.92] -0.373*** [-5.50] 0.086*** [5.86] -0.166 [-1.33] 0.040 [1.02] 0.115** [2.25] 0.044*** [3.66] 0.204*** [3.68] -0.367*** [-5.39] 0.078*** [4.69] -0.300*** [-2.90] -0.065 [-0.42] Panel B: Causality check International bond issues Foreign bank loans Depository receipts International bond issues* depository receipts Foreign bank loans* depository receipts Constant 0.016 [0.21] -0.234*** [-2.68] 0.012 [0.16] -0.231*** [-2.63] F-test 53.31*** 66.59*** 53.53 66.13*** Adjusted R-square 0.177 0.148 0.178 0.148 Number of Observation 27639 33190 27639 33190 The table reports the pooled OLS regression results that exclude the firms from Korea, Malaysia, and Taiwan. Columns (1) to (2) report the coefficient estimates of baseline specification. Columns (3) to (4) report the coefficient estimates with interaction terms. We use clustered standard errors by firms. For brevity, we do not report the estimates of control variables. The values of t-statistics are reported in brackets. ***, **, * stand for significance at the 1, 5 and 10 percent levels, respectively. Table 5. Pairwise correlations Firm characteristics [2] [3] [4] [5] [6] [7] [8] [9] LEV [1] 0.150 0.159 0.252 -0.181 -0.239 0.042 -0.048 0.029 MAT [2] 1.000 0.089 0.218 0.041 0.072 0.100 -0.009 0.125 SIZE [3] 1.000 0.054 -0.069 0.103 0.013 0.066 0.089 TANG [4] 1.000 -0.175 -0.054 0.359 -0.001 0.075 GROWTH [5] 1.000 0.278 0.018 -0.076 0.011 PROFIT [6] 1.000 -0.024 0.135 0.045 NDTS [7] 1.000 0.003 0.070 TAX [8] 1.000 0.016 DR [9] Country characteristics 1.000 [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] LEV [1] 0.150 -0.020 0.072 0.039 -0.039 -0.076 0.008 -0.118 -0.015 -0.018 -0.049 -0.111 -0.014 MAT [2] 1.000 -0.069 -0.104 -0.011 -0.164 -0.157 0.010 -0.086 -0.160 0.084 -0.051 0.171 -0.034 INBOND [3] 1.000 0.458 -0.148 0.448 0.069 -0.013 0.128 0.003 0.058 -0.008 -0.036 -0.101 FLOAN [4] 1.000 -0.026 0.470 -0.037 0.156 0.178 0.199 0.084 -0.036 -0.258 -0.185 STKLINK [5] 1.000 -0.220 0.095 0.081 -0.220 -0.028 -0.200 -0.077 -0.098 0.284 OPEN [6] 0.270 -0.236 0.619 0.550 0.308 0.468 -0.322 -0.449 GDP [7] 1.000 -0.199 0.254 0.649 -0.471 0.497 -0.359 0.560 INFL [8] 1.000 -0.271 -0.392 -0.125 -0.314 0.145 0.165 STKDEV [9] 1.000 0.607 0.428 0.470 0.026 -0.495 CRTDEV [10] 1.000 -0.004 0.599 -0.427 -0.062 COMMON [11] 1.000 0.208 0.453 -0.753 LEGSYS [12] 1.000 0.050 -0.109 IND [13] 1.000 -0.172 UAI [14] 1.000 1.000 This table reports the simple correlations among capital structure variables and explanatory variables from 1995 to 2007. 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