Capital structure choice in the emerging markets

(2)

Based on the significance and interpretations of these interactive variables we can test the following hypotheses:

H4.1: Profitable firms in case of economic growth prefer less debt in their capital structure

H4.2: Larger firms with increasing grow opportunities prefer more debt in their capital structure

H4.3: Firms with higher tangible assets ratio prefer more debt in their capital structure in less corruptive environment

The model is estimated with the Fixed-Effects estimation, which is equivalent to the (1) Least Squares Dummy Variables specification of the model. This model is applicable as there is an unobserved heterogeneity in each firm's capital structure due to missing variables. As all the macroeconomic and institutional variables vary with time, this allows us to estimate their effects.

4. Data description and statistics

The sample consists of total 1,763 non-financial and non-utility firms from capital IQ from 6 developing countries for a period from 2007 to 2014. The countries are Chile, Egypt, Malaysia, Poland, Thailand, and Turkey. The number of firms for each country are 122, 110, 461, 310, 488, and 272 in the respective order. All the firms employ IFRS accounting standard. There are missing values for some year-firm observations. So, there are 11,041 efficient observations. The number of efficient firm-year observations for each country are 797, 563, 3 139, 2 013, 3 056, and 1 745 in the respective order. The Macro Institutional data is attached for each year firm observation Macroeconomic variables are obtained from World Bank Development Indicators. The institutional variables are obtained from World Bank Doing Business and Transparency international. The individual firm data was winsorized at 1% and 99% levels to remove the effects of outliers.

The Table 1 summarize the statistics of variables:

Table 1, Source: Own calculations

The Table 2 on the next page shows the correlations matrix between all dependent and independent variables. As expected, there is a high correlation within all different definitions of leverage. For the explanatory variables the highest correlations are between profit and growth (41.4%), credit quality and corruption (35.5%), credit quality and inflation (35.1%), GDP and Inflation (33.6%). Other correlations are quite small, so there should be no problem of multicollinearity.

Table 2. The Correlations Matrix, source: Own calculations

5. Regression Analysis Results

Table 3. Model (1) Estimations on Total Leverage, Source: Own calculations

Table 4. Model (1) Estimations on Long Term Leverage, Source: Own calculations

The tables above report the model estimation results for all definitions of leverage. The standard errors reported are robust to heteroskedasticity, as the Modified Wald test for groupwise heteroskedasticity detected it. Overall, the book definitions of leverage show less significant correlations especially with macroeconomic and institutional variables than market value definitions.

Regarding the first hypothesis: the conventional firm factors seem to have a significant impact on firm's leverage. The F-test for a group significance of variables indicate p-value close to 0 for every definition of leverage. The most consistent factors are size and tangibility, which are significant at 1% level for every definition of leverage. What is surprising, tangibility has a negative impact on the leverage, and this is different from the evidence from the developed markets. This is consistent with San Martin and Saona (2017), who also report negative relationship between tangibility and leverage. Size has a positive sign for every estimate, as expected. The profitability has a consistent negative sign and it is significant at 1% for every definition of leverage, except for BLLev, for which it is significant at 10%. Growth is significant at 1% level for MTLev with a negative sign, and it is also significant at 5% level for BLLev. Overall, we do not reject the first hypothesis.

GDP is significant only at 1% level in MTLev and has a positive sign. For book total leverage it is significant at 10% and has a negative sign. So, the hypothesis is rejected for every definition of leverage, except for a BLLev. Overall, this finding is inconsistent and further work is required in this direction.

Inflation is significant at 5% level for BTLev and MTLev with a positive sign. So, we do not reject our hypothesis for those definitions of leverage, while reject for the effect on long term leverages.

Only market definitions of leverage are significantly affected by quality of institutions. Better anticorruption environment and better quality of credit institutions show the positive relationship with MTLev and MLLev and are significant at 1%. The institutional quality of tax system seems to be significant at 5% only for MTLev and has an expected negative sign. Overall, the test of group significance gives positive result for institutional variables at any reasonable significance levels for market definitions of leverage. So, we may conclude that H3.1 and H3.3 are not rejected for MTLev and MLLev, while H3.2 is not rejected for MTLev.

6. Interactive variables analysis

Table 5. Model (2) Estimations on Total Leverage, Source: Own calculations

Table 6. Model (2) Estimations on Long-Term Leverage, Source: Own calculations

*, **, *** denotes the level of significance of 10%; 5% and 1% respectively.

The addition of interactive variables does not change the model in a serious way, but signs of some coefficients change. What is interesting, interactive variable affected the significance of corruption for the book definitions of leverage, making it significant at 5% level for BTLev and BLLev. For MTLev profitability and growth became insignificant.

To explore the effect of interactive variables, I will calculate the coefficients of one variable at the different percentiles of another variable. The analysis is done for all the significant interactive variables.

First, I start with , which significant for all models

Table 7. Interactive variable analysis for tangibility and corruption, Source: Own calculations

It seems that higher corruption perception index interactive variable consistently overcomes the older estimate of tangibility on leverage starting from 75^th percentile of corruption perception index. While the tangibility starting from 25^th percentile reinforces the positive effect of corruption on leverage. So, overall, the hypothesis H4.3 is confirmed to be true.

Next, the similar table for interactive variable

Table 8. Interactive variable analysis for size and growth, Source: Own calculations

It seems like the effect of the interactive variable on size overcomes the older estimate only at 95^th percentile of growth. While for growth opportunities the interactive effect of size magnifies the estimate since 50^th percentile. Thus, the hypothesis H4.2 is rejected because the direction of effect is inverse from expected. Finally, we inspect the table for interactive variable . I will consider only effect of GDP on profitability, as it the inverse relationship does not make sense.

Table 9. Interactive variable analysis for GDP growth and profitability, Source: Own calculations

The effect of interactive GDP variable reinforces the negative effect of profitability consistently since 75^th percentile. This suggests that during economic growth firms might repay their debt more actively, and this lowers the leverage. Thus, the hypothesis H4.1 is not rejected.

Conclusion

This paper contributes to existing research on capital structure in developing markets. The evidence suggests that based on a sample containing 1,736 firms for a period 2007-2014 from 6 countries with emerging economies, the conventional capital factors seem to capture some capital structure decisions. The included countries are Chile, Egypt, Malaysia, Poland, Thailand, and Turkey. The significance and signs of profitability, size, and growth coincide with the evidence from developed countries, while tangibility is negatively significant.

Inflation with GDP are positively significant at explaining the market values of total and long-term leverages. The effects of better credit institutional quality and better anticorruption environment are significantly positively related to market value of total and long-term leverages, while the negative significant effect of better tax system institutional quality is captured only at total market leverage.

The interesting part of the work is the analysis of interactive variables. The results are that profitable firms have less leverage during the expansion of economy, the negative impact of tangibility is less pronounced in the better anticorruption environment, and firms with high growth opportunities prefer less leverage with increase in size.

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