Panel Estimation Results for Model 2: Firm Performance and Non-Strict Independence
The dependent variable is Firm Performance, as measured by ROA, ROE, or NEGPROF. ROA is computed as net income plus interest expenses net of tax effects (EBIAT) divided by the book value of assets of the previous period; ROE is computed as EBIAT divided by the book value of equity of the previous period; NEGPROF is a dummy variable equal to 1 when the firm’s EBIAT is negative and 0 otherwise. NSID is the ratio of the number of non-strict independent directors to the total number of directors on the board; TopOwn is the percentage ownership of the largest shareholder; Firm Size is the natural logarithm of the book value of total assets; Growth Opportunities is proxied by the one-period lagged market-to-book asset ratio; Leverage is the ratio of long-term debt to total assets; Firm Age is the natural logarithm of the number of years since the firm’s date of incorporation; CEO Duality is a dummy variable that takes on the value of 1 when the CEO is also the Chair of the board and 0 otherwise; ED Proportion is the proportion of executive directors on the board; Board Size is the natural logarithm of the number of directors on the board; EDown is the percentage of outstanding shares owned by executive directors; NEDown is the percentage of outstanding shares owned by non-executive directors; Busyness is the ratio of the number of busy non-executive directors to board size; Interlock is measured as the ratio of the number of interlocked executive directors to board size. The test statistics of the various specification tests are also reported. BPLM test is the Breusch-Pagan Lagrange Multiplier used to test for random effects; Wald’s test is used to examine whether there are fixed effects; Hausman test is used to determine whether the random effects model suffers from biased and inconsistent estimates; LR Test is analogous to the BPLM test but for logistic regressions. The results of these tests indicate that the Fixed Effects model is appropriate for regression models 1 to 4, while random effects logistic model is appropriate for models 5 and 6. The coefficient estimates for columns 5 and 6 are the average marginal effects. Figures in parentheses are cluster-robust standard errors. *** denotes significance at the 1% level; ** denotes significance at the 5% level; and * denotes significance at the 10% level.
Dependent Variable ROA ROE NEGPROF
Variable 1 2 3 4 5 6
NSID −0.0944 −0.219 −0.0717 −0.4242 −0.0371 0.0568
(0.0927) (0.1877) (0.1810) (0.3317) (0.0234) (0.0470)
TopOwn 0.1635 0.1467 0.4309 0.3834 0.0001 0.0201
(0.1487) (0.1549) (0.2883) (0.3027) (0.0114) (0.0146)
NSID x TopOwn 0.0023 0.0065 −K. KKMO**
(0.0026) (0.0045) (0.0009)
Firm Size −N. ]^OK* −N. ]Nba* −2.836 −2.7493 −K. bb^a*** −K. ONNN***
(1.4092) (1.4025) (2.2583) (2.2743) (0.1707) (0.1800)
Growth Opportunities 4.5661 4.6041 2.3591 2.4667 K. ]KNa** K. ]NMb**
(3.1654) (3.1731) (2.5049) (2.5074) (0.2016) (0.2077)
Leverage 0.182 0.1792 0.0837 0.0758 −0.015 −0.0162
(0.1106) (0.1153) (0.2122) (0.2130) (0.0171) (0.0174)
Firm Age 3.2071 3.2005 −15.0367 −15.0553 −0.0962 −0.0669
(9.7112) (9.6398) (20.5289) (20.1571) (0.3998) (0.4178)
CEO Duality −3.3913 −3.2974 −7.9454 −7.6796 0.7273 0.7496
(4.0087) (4.0341) (6.3229) (6.3860) (0.5276) (0.5457)
ED Proportion 0.0441 0.047 0.0171 0.0253 −K. Ka^^** −K. Ka`_**
(0.1195) (0.1219) (0.2243) (0.2309) (0.0167) (0.0173)
Board Size −10.7214 −10.5675 −23.9911 −23.5555 −1.5832 −1.8164
(15.4649) (15.5866) (20.3034) (20.5480) (1.2866) (1.3494)
ED Ownership 0.205 0.2042 0.2166 0.2145 0.0144 0.0161
(0.1715) (0.1698) (0.2517) (0.2477) (0.0133) (0.0140)
NED Ownership 0.3167 0.3275 K. ^_O^* K. `KOO** −0.0066 −0.0154
(0.2099) (0.2081) (0.3104) (0.3040) (0.0274) (0.0287)
Busyness 0.0968 0.1017 0.1719 0.1859 −0.0353 −0.0386
(0.1499) (0.1499) (0.2569) (0.2589) (0.0247) (0.0259)
Interlock −0.1499 −0.1491 −0.0441 −0.0418 −0.0414 −0.0431
(0.1347) (0.1353) (0.3375) (0.3385) (0.0445) (0.0463)
Industry None None None None Yes Yes
Years Yes Yes Yes Yes Yes Yes
Wald’s Test 2.56*** 2.56*** 2.09*** 2.10*** 0.63 0.71
BPLM Test 41.96*** 42.02*** 43.14*** 43.26*** - -
LR Test - - - - 81.20*** 84.02***
Hausman Test 64.25*** 64.77*** 24.52** 25.73** - -
Chi2 1.54* 1.51* 1.44 1.69** 53.90*** 51.33***
Appropriate Model Fixed Effects Fixed Effects Fixed Effects Fixed Effects Random Effects Random Effects
Observations 669 669 669 669 669 669
Table 8 reports the results of estimating Equation 2. Columns (1) and (2) present the results using ROA as the measure of firm performance; Columns 3 and 4 present the results when ROE is used to measure firm performance; and Columns 5 and 6 report the results when NEGPROF is the measure of firm performance.
For our models using ROA and ROE as the firm performance measures, results of the Wald’s test and the Breusch-Pagan Lagrange Multiplier (BPLM) test indicate the presence of firm-specific effects on firm performance. Furthermore, results of the Hausman specification test indicate that the fixed effects estimator is more appropriate for our data than the random effects estimator. On the other hand, for our models using NEGPROF as the firm performance measure, results of the likelihood ratio tests indicate that the random effects estimator is more appropriate for the data than the pooled logit estimator while the Wald’s test indicates that fixed effects logistic regression is no better than the pooled logit estimator. Results of the Hausman test further confirm that the random effects estimator is more appropriate than the fixed effects estimator when using NEGPROF as the firm performance measure.
Overall, regardless of the performance measure used, we find that the presence of non- strict independent directors does not significantly affect firm performance. The finding of an insignificant relationship supports the optimal board independence theory, which posits that firms may appoint non-strict independent directors merely to satisfy the recommended levels of board independence, even if such directors may not be more or less impactful than their strictly independent counterparts when it comes to enhancing firm performance. Moreover, this implies that the appointment of non-strict independent directors does not mask any uncontrolled agency problems within the firm that reduce firm value, similar to the findings of Crespi-Cladera and Pascual-Fuster (2014).
Results from Model 1 show that firms with a higher degree of ownership concentration are more likely to have a non-strict independent director on the board; however, in Model 2, we find that the interaction term between top ownership and the proportion of non-strict independent directors (NSID x TopOwn) is negative and significant when NEGPROF is used as the firm performance measure. These results indicate that although firms with a higher degree of ownership concentration are more likely to have a non-strict independent director on the board, more non-strict independent directors also imply that firms with higher degrees of ownership concentration are less likely to suffer from negative profits. This evidence is consistent with the prediction of the optimal board independence theory.
We also find some significant evidence that larger firms have lower firm performance when ROA is used as the firm performance measure. It may be the case that larger firms are more susceptible to bureaucratic problems and are less technically efficient when adopting changes in their organizational structure (Yang & Chen, 2009). However, when using NEGPROF as the performance measure, we find significant evidence that larger firms are less likely to have negative profits. This may be attributed to the more competitive and powerful nature of larger firms relative to smaller firms (Dogan, 2013). We also find some evidence that higher growth opportunities result in a higher likelihood of the firm experiencing negative profit.
For our corporate governance variables, we find some significant evidence that non- executive directors’ share ownership improves firm performance. These results indicate that the ownership stake of non-executives on the board may motivate management to make sure that the firm performs well (Vu, Phan, & Le, 2017).
Model 3 Results and Discussion