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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
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∗
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. The abbreviations for firm
characteristics are defined in section 3.2. The abbreviations for country characteristics are defined as follows: international bond issues (INBOND), foreign bank
loans (FLOAN), stock market linkage (STKLINK), economic openness (OPEN), GDP per capita (GDP), inflation rate (INFL), stock market development
(STKDEV), credit market development (CRTDEV), common law dummy (COMMON), efficiency of legal system (LEGSYS), individualism (IND), uncertainty
avoidance (UAI). Bold characters indicate significant at 1% level.
16
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18
Institute for International Integration Studies
The Sutherland Centre, Trinity College Dublin, Dublin 2, Ireland