We explore endogeneity explicitly by means of two different models, which we call the dynamic performance model and the integrated mechanism model, respectively.
The dynamic performance model rests on the idea that reverse causa-tion between performance and board mechanisms (i.e., performance drives board design) can be partially captured by including lagged performance as a determinant of current performance in (2.2). The required assump-tion is that lagged performance and the other explanatory variables are predetermined relative to current performance (Arellano, 2003, p. 144)20. Since this assumption allows for feedback from past performance to cur-rent board design, it means that if the dynamic model produces diffecur-rent coefficient estimates for board mechanisms than the basic model in table 2.3, these board mechanisms are at least partially driven by performance.
19To explore whether panel data estimation is required in our setting, we used OLS to estimate (2.2) on the pooled (i.e., un-demeaned) sample. This approach ignores both indi-vidual effects and time effects by assuming that the error term is identical across all firms and time periods. We found several noticeable differences. First, unlike our panel data model, pooled OLS reproduces the classic result in the ownership structure literature of a positive and quadratic relationship between insider holdings and corporate performance.
Also, outside ownership concentration is inversely related to performance in a significant way. Second, the negative exported CEO effect becomes significant, and the significant coefficient of the network effect is higher. Third, only employee directors is significant among the decisiveness mechanisms, but its sign is reversed and its significance weaker.
As expected, the importance of the control variables increases considerably, and the R2is below one fifth. Overall, these findings show that unless we can ignore the panel structure in our data set, the pooled model is seriously misspecified. ANOVA analysis of the the pooled OLS regression shows that the pooled model is indeed misspecified. 54% of the sum of squares in the estimated OLS error term is driven by fixed firm effects, 4% is due to time effects, and 40% is random. The remaining 2% is driven by joint individual and time effects.
20Thus, lagged performance may be correlated with the lagged error term, but not with the contemporaneous and future error terms. This assumption is the so-called sequential moment condition, which can be expressed as E(vit|Xi1, . . . , Xit, ci) =0 when t=1, . . . , T, where lagged performance is one of the explanatory variables.
We specify the dynamic model as:
Qit=θ+αQi,t−1+β(Board mechanisms)it+γ(Controls)it+ci+vit (2.4) where α is the coefficient of lagged performance and β and γ are the coeffi-cient vectors of the board mechanisms and control variables, respectively.
The standardized coefficient estimates of the dynamic performance model are shown in the first column of results in table 2.4. The results strongly support the findings in the static model in table 2.3, whose esti-mates are repeated in the second column of results in table 2.4. The only difference is that the linear insider holding term becomes more signifi-cant both statistically and economically and that its non-linear component becomes statistically significant. These results show that endogeneity in terms of reverse causation from past performance matters because past performance influences the effect of current board mechanisms on current performance. However, this reverse causation from past performance is still moderate, as every result from the base-case model survives.
Table 2.4
The second model consists of six equations and is called the integrated mechanisms model in table 2.4. The first equation is the static model from table 2.3. Each of the five other equations have a board mechanism as de-pendent variable, which is Directors’ holdings, Independence, Network, Gender, and Size, respectively.
The estimates of the integrated mechanisms model reflect two-way contemporaneous causation between performance and four of the five mechanisms. The performance equation shows that boards with high di-rectors’ holdings, networked directors, low gender diversity, and small size produce higher performance. As shown by the directors’ holdings, network, gender, and size equations, respectively, better performance feeds back to these four board mechanisms by producing lower insider hold-ings, more networked directors, less gender diversity, and reduced board size. Thus, directors with high equity stakes improve the firm’s expected performance (from the performance equation), but tend to sell off or be re-placed by non-owning directors as performance improves (from the direc-tors’ holdings equation). Busy directors improve performance (the perfor-mance equation), but are also attracted to well-performing firms (the net-work equation). Lower gender diversity reduces performance (the perfor-mance equation), but high-performing firms tend to establish boards with
less gender diversity (the gender equation). Finally, smaller boards im-prove performance (the performance equation), but firms with high per-formance tend to reduce board size (the size equation). Overall, these find-ings show that improved performance makes boards less aligned, better informed, and more homogenous.
Turning to the internal relationship between board design mechanisms within each of the three groups (i.e., the alignment, information, and de-cisiveness mechanisms), five of the six significant coefficients are positive.
This means mechanisms are mostly complements rather than substitutes.
Firms with high inside ownership concentration also have high outside ownership concentration (alignment), boards with well networked direc-tors also export their CEO to other boards (information), and boards with low gender diversity are smaller and have less age diversity (decisive-ness). The relative economic significance of the mechanisms resembles what we found for the determinants of performance: Ownership and net-work mechanisms are not only the strongest drivers of performance, but also of other board mechanisms.
Four of the five mechanisms modeled in the table are significantly related to either firm size, firm risk, or both, which are the two board-exogenous determinants in our model. Large firms have better networked directors, and risky firms have lower insider holdings, better networked directors, and lower gender diversity. The negative association between risk and gender mix is consistent with Adams and Ferreira (2004), who find that firms more often choose male directors when uncertainty in-creases. This is in line with the argument that higher uncertainty makes firms rely more on the trust inherent in homogenous teams than on the more noisy performance-based incentives in heterogenous teams (?).
The finding that several board design mechanisms are internally re-lated and driven by board-external variables supports the equilibrium hy-pothesis of Demsetz and Lehn (1985) that governance mechanisms are op-timally adjusted to each other. On the other hand, the equilibrium hypoth-esis is apparently falsified by our finding that several mechanisms are also significant variables in a performance equation that controls for endogene-ity. We hesitate to conclude this way, since the institutional environment of our sample firms does not allow owners to freely design the board.
Mandatory employee directors in firms with more than 200 employees is the most obvious example of such a restriction.
Summarizing, the empirical tests in this section have shown that own-ers on the board as well as multiple directorships are positively related to
performance. This is consistent with the hypotheses that directors with high ownership stakes have stronger monitoring and advice incentives than other directors and that well-connected directors create extra value through the network they bring along to the boardroom. In contrast, more diversity produced by larger board size, more gender mix, and more em-ployee directors is always negatively associated with performance. This suggests heterogenous boards are less effective decision makers than ho-mogeneous boards. All these relationships are statistically significant, and the economic significance is stronger for insider ownership and networked directors than for the decisiveness mechanisms.
Several board design mechanisms are endogenous, both relative to performance and to each other. For instance, directors with wide net-works produce high performance and gravitate towards well-performing firms, and networking declines when gender diversity increases. More-over, board mechanisms are much more often complements than substi-tutes.
In terms of policy implications, these findings provide no economic argument for mandating more independence or more diversity, such as requiring by law or recommending by code that a minimum fraction of directors be independent, employees, or of a particular gender. The fact that the relationship to performance for employee directors and gender diversity is statistically and economically significant may reflect that these mechanisms are not at their optimal level, whereas the insignificance for the independence mechanism reflects that it is. This means that if any-thing, the regulatory implication is the opposite of what has been argued in the public: Regulators should mandate more director owners and more networked directors, less employee directors, less gender diversity, and smaller boards. The independence mechanism needs no regulatory assis-tance.