Table 4

Results of multinomial logit regression

ModelDivMethodDiv0Div50DivYear
VariableCashStockCash–stock
SDG5.479***5.208**0.203(4.708)***4.283*(0.425)
6.9115.9170.0056.9983.1690.012
SIZE(0.839)***(0.034)(0.431)*0.450***0.058(0.308)
17.5340.0262.9128.1460.0350.636
LEV0.0180.2083.737(0.043)(1.735)(0.151)
1x10−40.0112.0200.0010.3660.002
TobinQ(0.425)*(0.333)(0.086)0.2930.1410.150
3.0880.4990.1162.1200.3510.484
ROA19.593***(2.409)16.336*(8.658)1.4111.994
6.6390.1203.0292.2280.0170.027
ROE1.7027.330**12.384**(6.771)**4.8597.153
0.1944.6296.0045.0950.3680.725
AGE0.078***0.042**0.079***(0.070)***(0.006)(0.046)
16.8843.86413.31915.7060.0991.923
BGD(0.026)***0.009(0.032)**0.014**(0.037)*0.014
6.9940.6704.8763.0143.0390.543
–2Log-likelihood (Intercept)748.423  366.519113.14980.845
–2Log-likelihood (Final)522.696***  269.753***94.425**67.414**
Pseudo R20.302  0.2640.1650.166
Observations300  300300300

Note(s): Significant at *p < 0.10; **p < 0.05; ***p < 0.01; Negative numbers are presented in parentheses; the italicised value below is the Wald value

Source(s): Authors' own work

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