ESTADOS DE RESULTADOS Estado de Resultados

The quantitative approach uses two definitions of leverage, (total liabilities-to- total assets (TLTA) and long term debt-to-total assets (LTDTA), as dependent variables. In testing the determinants of leverage, three numerical independent variables were used to proxy for profitability, size and risk. To capture the impact of ownership structure (family, institutional and government) and relationship banking, four additional dummy variables were included in the regression. Table 5.5 outlines descriptive statistics, the correlation matrix of the two dependent variables (LTDTA and TLTA) and the seven (7) independent variables.

Table 5.5: Descriptive statistics and correlation matrix

LTDTA TLTA ROA SIZE BETA GOV FAM INS RB

Panel A: Descriptive statistics

Mean 0.14 0.37 0.03 18.78 0.88 0.49 0.28 0.4 0.42 Sum 123.37 317.84 21.62 16154.33 756.98 420 240 340 360 Median 0.07 0.37 0.02 19.98 0.89 0.00 0.00 0.00 0.00 Maximum 0.65 0.8 0.2 23.42 2.8 1.00 1.00 1.00 1.00 Minimum 0.00 0.00 -0.09 13.47 -1.63 0.00 0.00 0.00 0.00 Sum Sq. Dev. 41.17 154.61 1.33 310373.06 826.41 420 240 340 360 Std. Dev. 0.17 0.21 0.03 2.84 0.43 0.5 0.45 0.49 0.49 Skewness 1.19 0.1 2.17 -0.37 0.17 0.05 0.99 0.43 0.33 Kurtosis 3.47 1.87 11.62 1.6 5.11 1.00 1.97 1.18 1.11 Jarque-Bera 212.37 47.29 3333.61 90.02 163.89 143.33 177.08 144.54 143.76 Probability 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Observations 860 860 860 860 860 860 860 860 860 Cross sections 43 43 43 43 43 43 43 43 43

Panel B: Correlation coefficients

LTD/TA 1.00 0.64 -0.28 -0.08 0.30 0.12 0.24 0.29 0.25 TL/TA 0.64 1.00 -0.34 -0.32 0.24 0.00 0.30 0.38 0.34 ROA -0.28 -0.34 1.00 0.08 -0.24 0.19 -0.22 -0.02 0.04 Size -0.08 -0.32 0.08 1.00 -0.08 0.12 -0.18 -0.21 -0.07 Beta 0.30 0.24 -0.24 -0.08 1.00 -0.02 0.11 0.05 0.03 Govt. 0.12 0.00 0.19 0.12 -0.02 1.00 -0.09 -0.03 0.40 Fam. 0.24 0.30 -0.22 -0.18 0.11 -0.09 1.00 0.03 0.21 Inst. 0.29 0.38 -0.02 -0.21 0.05 -0.03 0.03 1.00 0.09 RB 0.25 0.34 0.04 -0.07 0.03 0.40 0.21 0.09 1.00

The findings indicate that profitability and size are negatively correlated with the two dependent variables. With the exception of size, all the variables have positive skewness. However, the Jarque-Bera tests show that the variables are not normally distributed. The mean values are all positive, with a mean leverage of 14% and 37% for LTDTA and TLTA, respectively. Al-Ajmi et al. (2009) report slower leverage ratios of 10% and 28% for LTDTA and TLTA respectively. The minimum leverage is 0% for both measures, but the maximum leverage for the sample is 65% and 80% for LTDTA and TLTA, respectively. Overall, the correlations among independent variables are not too high and do not cause multicollinearity concerns.

Table 5.6: Regression results with total liabilities to total assets (TLTA) as dependent variable

Pooled OLS

Pooled OLS with White-Corrected Standard Errors

Random Effect Fixed Effect

Constant 0.51*** 0.51*** 0.09 0.49*** (0.00) (0.00) (0.21) (0.00) Profitability -1.85*** -1.85*** -0.33*** -1.78*** (0.00) (0.00) (0.00) (0.00) Size -0.01*** -0.01*** 0.01*** -0.01*** (0.00) (0.00) (0.01) (0.00) Risk 0.06*** 0.06*** 0.01** 0.08*** (0.00) (0.00) (0.04) (0.00) Family Ownership 0.05*** 0.05*** 0.10** 0.05*** (0.00) (0.00) (0.05) (0.00) Government Ownership -0.01 -0.01 -0.04 -0.01 (0.46) (0.42) (0.42) (0.45) Institutional Ownership 0.13*** 0.13*** 0.15*** 0.13*** (0.00) (0.00) (0.00) (0.00) Relationship Banking 0.12*** 0.12*** 0.13*** 0.12*** (0.00) (0.00) (0.01) (0.00) Adj. R-Square 0.43 0.43 F-Statistics 93.37 93.37 Prob.(F-Stats) (0.00) (0.00) Chi-Squared Statistics 19.54 Chi-Squared (df) 3 Prob. (0.00)

The results in Table 5.6 outline the regression results for the dependent variable (TLTA) and the seven independent variables. The results show the pooled sample, the pooled sample using the White-corrected standard errors and adjustments for fixed and random period effects. The adjusted R-squared indicates that approximately 43% of the variation in leverage is explained by the variables in the equation. In addition, the F-statistics show that the overall regression is significant at the 1% level, as the P-values are less than 1%.

Table 5.7: Regression Results with Total liabilities to total assets (LTDTA) as dependent variable

Pooled OLS

Pooled OLS with White- Corrected Standard Errors

Random Effect Fixed Effect

Constant -0.02 -0.02 -0.02 -0.05 (0.53) (0.50) (0.53) (0.16) Profitability -1.24*** -1.24*** -1.24*** -1.13*** (0.00) (0.00) (0.00) (0.00) Size 0.00 0.00 0.00 0.00 (0.31) (0.32) (0.31) (0.29) Risk 0.08*** 0.08*** 0.08** 0.11*** (0.00) (0.00) (0.00) (0.00) Family Ownership 0.05*** 0.05*** 0.05** 0.05*** (0.00) (0.00) (0.00) (0.00) Government Ownership 0.04 0.04 0.04 0.04*** (0.00) (0.00) (0.00) (0.00) Institutional Ownership 0.09*** 0.09*** 0.09*** 0.09*** (0.00) (0.00) (0.00) (0.00) Relationship Banking 0.05*** 0.05*** 0.05*** 0.05*** (0.00) (0.00) (0.00) (0.00) Adj. R-Square 0.28 0.28 F-Statistics 49.87 49.87 Prob.(F-Stats) (0.00) (0.00) Chi-Squared Statistics 23.40 Chi-Squared (df) 3 Prob. (0.00)

Table 5.7 shows the regression results for the dependent variable (LTDTA) and several independent variables. The results show the pooled sample, the pooled sample using the White-corrected standard errors, and adjustments for fixed and random period effects. The adjusted R-squared indicates that approximately 28% of the variation in leverage is explained by the variables in the equation. However, the model with the dependent variable (TLTA) has a stronger explanatory power than the one with LTDTA. In addition, the F- statistics show that the overall regression is significant at the 1% level, as the p- values are less than 1%.

Table 5.8: Summary of regression results

Hypotheses Regression Result

(TL_TA)

Regression Result (LTD_TA)

Hypothesis 1. ROA has a negative

impact on leverage

Accept Accept

Hypothesis 2. Natural logarithm of total

assets has a positive impact on leverage

Reject Reject

Hypothesis 3. Beta has a negative

impact on leverage

Reject Reject

Hypothesis 4. Family ownership has a

positive impact on leverage

Accept Accept

Hypothesis 5. Institutional ownership

has a negative impact on leverage

Reject Reject

Hypothesis 6. Government ownership

has a positive impact on leverage.

Reject Accept

5.2.1 Profitability

The results shown in Tables 5.6 and 5.7 show that profitability is negatively related to leverage. The results in Table 5.6 show that a 1% increase in profitability results in a 1.85% decrease in leverage (TLTA) for the pooled OLS. When leverage is defined as LTDTA, as shown in Table 5.7, the decrease in leverage for a 1% increase in profitability is 1.24%. The results for the random and fixed effect were also negative and significant for both measures of leverage. These findings show that firms with higher profitability have lower leverage, and these results are significant at the 1% level. The results also show that Saudi manufacturing firms reduce their leverage as they become

more profitable. In addition, the reduction in the leverage is more pronounced when leverage is defined as total liability-to-total assets (TLTA), rather than long-term debt-to-total assets (LTDTA). This finding is consistent with Hypothesis 1, which states that ROA has a negative impact on leverage. It is also consistent with the pecking-order theory, which argues that profitable firms depend more on internal financing in order to reduce information asymmetries and avoid costly external financing (Myers and Majluf, 1984). The theory predicts a negative relationship between profitability and leverage.

5.2.2 Size

The results of the impact of size on leverage depend on the measure of leverage used. The results in Table 5.6 show that, for the pooled OLS and the White-corrected standard errors, leverage (TLTA) for larger firms is lower by approximately 1% than for smaller firms, and these results are significant at the 1% level. Even though the results for the fixed effect are consistent with those of the pooled OLS, the findings of the random effect shows that size has a positive impact on leverage. However, when using the firms’ long-term debt-to- total asset (LTDTA) as the dependent variable, the results in Table 5.7 show that the relationship between size and leverage is insignificant. Therefore, the proposition of large firms carrying more debt is refuted. Hence, the findings are not consistent with Hypothesis 2, which states that the natural logarithm of total assets has a positive impact on leverage.

5.2.3 Risk

The results shown in Tables 5.6 and 5.7 demonstrate a positive and significant relationship between risk and leverage. The results for the pooled sample and the pooled sample with White-corrected standard errors show that leverage (TLTA) is 6% and 8% higher for riskier firms when leverage is defined as total liability-to-total assets (TLTA) and long term debt-to-total assets (LTDTA), respectively. These results are significant at the 1% level and show that Saudi manufacturing firms with significant business risks are more likely to carry more debt. The results are inconsistent with Hypothesis 3, which states that Beta has a negative impact on leverage.

5.2.3 Family Ownership

Dummy variables were used to assess the effect of family ownership on leverage. Firms that have a family ownership structure that is greater than 10% are considered to be family-owned. The results for family ownership in Tables 5.6 and 5.7 show that firms that have large family ownership blocks carry more debt than those that do not. The results for the pooled sample and the sample with White-corrected standard errors show that firms with family ownership carry 5% more debt on both measures of leverage than those that do not have family ownership, and these results are significant at the 1% level.

Hence, for the sample of Saudi manufacturing firms, the result is consistent with Hypothesis 4, which states that family ownership has a positive impact on leverage. It is also consistent with the findings by Stulz (1988), who states that family-owned firms would carry more debt in order to avoid the dilution of their equity stake and control.

5.2.4 Institutional Ownership

Dummy variables were used to assess the effect of institutional ownership on leverage. Firms that have an institutional ownership structure that is greater than 10% are considered to be institutionally owned. The results for institutional ownership in Tables 5.6 and 5.7 for the pooled sample and the sample with White-corrected standard errors show that firms with family ownership carry 13% more debt, when leverage is measured as total liabilities-to-total assets, and 9% more debt, when leverage is measured as long-term debt-to-total assets; these results are highly significant at the 1% level. Hence, the results for the sample of Saudi manufacturing firms do not support Hypothesis 5, which states that institutional ownership has a negative impact on leverage.

5.2.5 Government Ownership

Dummy variables were used to assess the effect of government ownership on leverage. Firms that have a government ownership structure that is greater than 10% were considered to be government owned.

The results shown in Table 5.6 indicate that, when leverage is measured as total liability-to-total assets, government ownership has no impact on firms’ leverage decisions. However, when leverage is measured as long-term debt-to- total assets, as shown in Table 5.7, government ownership has a positive and significant influence on leverage. Firms with government ownership are, on average, 4% more levered than those without government ownership, and the result is significant at 1%. This finding is consistent with Hypothesis 6, which states that government ownership has a positive impact on leverage. According to Booth et al., (2001), the effect of government ownership on capital structure largely depends on the jurisdiction in which the SOEs operate. However, in the context of Saudi Arabia, government financial institutions, such as the SIDF, provide loans to manufacturing firms regardless of their ownership structure. The mixed results imply that the impact of government ownership on leverage depends on the definition of leverage.

5.2.6 Relationship Banking

A dummy variable was used to represent relationship banking. This variable takes the value 1, if members of the firm’s board are also in the board of the banks, and takes a vale of zero in all other cases. The results in Table 5.6 show that, when leverage is defined as total liabilities-to-total assets (TLTA), firms that have a positive relationship with their banks have higher leverage than those that do not have such a relationship with their banks. The results for the pooled sample and the pooled sample with White-corrected standard errors show that firms with a good relationship with their banks are, on average, 12% more leveraged than their counterparts, and the results are significant at the 1% level. Moreover, when leverage is defined as long term debt-to-total asset (LTDTA), the results in Table 5.7 show that firms with strong relationship with their banks are, on average, 5% more leveraged than their counterparts, and the results are significant at the 1% level.

The findings support Hypothesis 7, which states that relationship banking has a positive impact on leverage. In addition, several studies have shown that

commercial banking concentration can significantly affect the capital structure decisions of a firm (Petersen and Rajan, 1995; Cetorelli and Strahan, 2006; and Gonzalez and Gonzalez, 2008). The Saudi Arabian banking sector is highly concentrated (SAMA, 2013). Thus, it can be argued that relationship banking resumes importance in this context.