do older boards affect firm performance? an empirical ... · overall our results indicate that the...
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Do older boards affect firm performance? An empirical analysis based on Japanese firms
Dr Makoto Nakano
Faculty of Commerce & Management
Hitotsubashi University
Tel: +81 42 580 8745
and
Dr Pascal Nguyen
School of Finance & Economics
University of Technology Sydney
Tel: +61 2 9514 7718
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Do older boards affect firm performance? An empirical analysis based on Japanese firms
Abstract: We analyze the role of board age on firm performance using a large sample of Japanese firms. The results reveal the existence of a significant negative relationship. After controlling for endogeneity using firm size as instrument, the effect of board age is found to be more significant, consistent with the notion that older directors are more likely to retain (relinquish) their positions in strongly (poorly) performing firms. In addition, we show that the performance of younger and high-growth firms is more sensitive to board age, which points to a risk-based explanation. Indeed, it appears that older boards are more reluctant to take risks and particularly to undertake acquisitions. Overall, the results underline the disadvantage of (re)appointing older managers since the latter tend to be more conservative, perhaps because of their shorter decision horizons or greater vested interests. Keywords: board of directors, top management, decision making, risk aversion, performance
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1. Introduction
Research in organizational behavior suggests that top management characteristics affect
corporate decisions and, in particular, the firm’s ability to take risks which may have a critical
impact on its performance. In an influential paper, Vroom and Pahl (1971) establish that older
managers are more likely to avoid risky decisions. Consistent with this notion, Child (1974)
observes that older managers tend to stick to cautious low-growth strategies, while younger
managers are more eager to pursue innovative high-growth policies. Likewise, Wiersema and
Bantel (1992) reveal that executive age has a negative influence on the rate of corporate change.
More recently, Antia et al. (2010) uncover that managers with shorter horizons (who are likely
to be older) prefer investments offering relatively faster payback at the expense of long-term
value creation.
In this paper, our objective is to examine the effect of board age on firm performance. We focus
on the board of directors since our analysis is based on Japanese firms. In Japan’s Commercial
Code, the board of directors is not only responsible for overseeing the company’s business, but
is also in charge of making decisions on the way business is conducted. In addition, the firm’s
most senior executives are usually members of the board (Abegglen and Stalk, 1985; Yoshikawa
and Phan, 2005). Hence, the board represents the effective decision-making unit in the typical
Japanese firm. In that sense, our study of Japanese boards can be viewed as extending the
existing literature on top management decision-making, especially since Japanese CEOs have
less power than their US counterparts (Ahn et al., 2009). One observable characteristic of a
board is the average age of its members. Based on the idea that age is detrimental to risk-taking
and the fact that risk-taking is essential to performance, our hypothesis is that firms with older
boards achieve a lower performance.
Previous studies regarding the role of board characteristics have mainly focused on testing the
negative effect of board size on firm performance (e.g., Yermack, 1996; Eisenberg et al., 1998;
Mak and Kusnadi, 2005). Other studies of board independence and share ownership have found
mixed results (e.g., Himmelberg et al., 1999). More recently, Adams and Ferreira (2009) have
investigated the role of gender diversity on board effectiveness and found a surprisingly strong
effect on firm performance. However, few studies have paid attention to the role of board age
even when that information was available. For instance, Faleye (2007) includes it as a simple
control variable to evaluate the consequence of staggered boards. Yet, closer inspection of his
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results reveals that board age has an impact on firm value which is almost twice as large as the
effect of board size.1 The only study close to ours is from Bonn et al. (2004) who compare the
performance of Australian and Japanese boards. However, their analysis is restricted to a small
sample of large firms and includes few control variables. The issue of board endogeneity is also
neglected, and no further evidence is given to help explain the negative role of board age.
To perform our analysis, we use a large cross-section of Japanese firms listed on the Tokyo
Stock Exchange in 2007. Due to the absence of a time dimension, we cannot control for
unobservable firm (or board) characteristics by examining the differential effect of a change in
board age on firm performance. Bonn et al. (2004) experience the same limitation. However, we
mitigate this problem by including as many controls as possible. We also consider the likelihood
that performance may be determined endogenously with board age. Hence, to the extent that the
most critical firm differences are appropriately accounted for (in particular, performance factors
potentially correlated with board age), our estimates should not be subject to the missing
variable bias that has dogged many corporate governance studies. Consistent with previous
studies (e.g., Fukui and Ushijima, 2007; Adams and Ferreira, 2009), our performance measures
include both the firm’s operating profitability (ROA) and market value (Tobin’s Q).
The OLS regressions show that older boards are associated with significantly lower performance.
In fact, the difference in ROA between the youngest and oldest board age quartiles is as high as
1.7% despite the inclusion of numerous control variables. Nevertheless, this result could simply
be a symptom of weak corporate governance whereby older directors cling to their positions
despite their firm’s poor performance. Alternatively, the impact of board age could be
underestimated if older directors are more likely to retain (relinquish) their positions when their
firm is doing well (poorly).2 To mitigate this endogeneity problem, we use an instrumental
variable (2SLS) framework. Firm size satisfies the conditions of a valid instrument to predict
board age because (1) larger firms have more managerial layers, which implies that employees
1 A one standard deviation increase in board size is associated with a 5.1 percentage point decrease in Tobin’s Q, while a one standard deviation increase in board age is associated with a 9.3 percentage point decrease in Tobin’s Q. 2 More precisely, suppose that 1) Board age has no effect on performance and 2) Performance is subject to random shocks. If some older directors are maintained in their positions as a reward for their firm’s good performance (i.e. the positive outcome to which they have not contributed), this would generate a positive, but spurious, correlation between board age and performance, even though no effect actually exists. Similarly, if some older directors are removed from the board because of the firm’s poor performance (which is due to bad luck) a positive correlation between board age and performance will appear as well.
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need more time to eventually reach board positions and (2) firm size has been shown to have no
effect on firm performance (e.g., Fukui and Ushijima, 2007; Hu and Izumida, 2008).3
The 2SLS results indicate that board age has a somewhat stronger negative influence on firm
performance. However endogeneity tests indicate that the difference with OLS estimates is not
statistically significant. We thus rely on the latter due to their greater efficiency. Partitioning the
sample according to various firm characteristics, we further show that the impact of board age
does not depend on the firm’s size or affiliation to a business group, but is stronger among
younger and high-growth firms, and among firms using more intangible assets. These results
point out that some firms may require different types of managers because of their different
characteristics. More precisely, it appears that the greater determination and ability to take risk
typical of younger managers make them more fit to operate in high-growth or rapidly-changing
environments (Child, 1974; Wiersema and Bantel, 1992).
In an attempt to explain the negative impact on performance, we examine the association
between board age and corporate risk-taking. Our cross-sectional regressions show that firms
with older boards exhibit a significantly lower variability in their operating profits, market
values and stock returns. They are also less likely to undertake acquisitions, which are
considered to be risky investments. This result is consistent with May (1995) who notes that
managers with more capital vested are more likely to diversify in order to reduce their firm’s
idiosyncratic risk. In the case of Japan, age can be a good proxy for a manager’s vested capital
due to the enduring practice of lifetime employment and deferred compensation (Ono, 2010).
This negative influence on risk-taking may explain why older boards are associated with lower
profitability and firm value.
Overall our results indicate that the average age of the firm’s decision makers is a significant
determinant of its performance. In fact, the effect is stronger than board structure (size and
independence) and financial incentives (board equity ownership) and more comparable to the
effect of important firm characteristics (such as firm size and age). This finding has important
implications for investors and suggests that they should pay close attention to the characteristics
of the firm’s top managers and make sure that they are renewed or rejuvenated to avoid a
3 In addition, numerous studies have underlined the absence of a size effect in Japanese stock returns (e.g., Kubota and Takehara, 1997; Daniel et al., 2001; Aman and Nguyen, 2008).
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possible drift towards lower risk-taking and decreasing performance, especially when the firm’s
environment is changing and requires taking decisive actions with far-reaching consequences.
The rest of this article proceeds as follows. Section 2 describes the sample and methodology
used in the study. Section 3 presents the results relating board age to firm performance. In
section 4 we investigate the link between board age and risk taking. Section 5 concludes.
2. Data and methodology 2.1 Sample
Our initial sample is represented by all Japanese firms listed on the Tokyo Stock Exchange at
the end of 2007. Financial firms (i.e., banks, insurance, brokerage and asset management
companies) are excluded because of their specific performance and risk indicators. Information
on corporate boards is sourced from Nikkei CGES (Corporate Governance Evaluation System)
provided by the Nikkei newsgroup. Nikkei CGES provides the average age of all board
members, as well as the age of the youngest and oldest director, but not the age of each director.
This data is hand-collected from the annual report and other sources (in particular the firm’s
website). In total, 1771 firms were covered by Nikkei CGES. After eliminating financial
companies and firms with incomplete data, our final sample consists of 1324 firms operating in
31 industries. Additional financial and stock-related information was collected from Nikkei
NEEDS, a database that is extensively used in the analysis of Japanese firms.
2.2 Measurement and determinants of performance
In line with previous studies, firm performance is gauged using two complementary indicators.
To assess the firm’s current profitability and efficiency in the use of its assets, we use an
accounting measure: operating return on assets (ROA). To take into account value created
beyond the current period, we use a forward-looking measure: Tobin’s Q. Following standard
practice (e.g., Mak and Kusnadi, 2005; Fukui and Ushijima, 2007), the Q ratio is proxied by the
market-to-book value of assets. Further, we winsorize the top and bottom 1% to mitigate the
influence of outliers.4
4 An alternative is to take the natural log of Tobin’s Q (see Adams and Ferreira, 2009). We also used that approach and found the results to be similar with slightly more significant coefficients. However, the discussion of the results becomes more convoluted.
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The key explanatory variable is board age, representing the average age of the firm’s directors.
Overall, this variable is expected to have a negative influence on firm performance. While older
managers have a lot of experience that may help improve the firm’s operations, they also tend to
defend the status quo. Hence their reluctance to make risky decisions that could have negative
implications (Carlson and Karlsson, 1970; Vroom and Pahl, 1971). Moreover, older directors
can lack the stamina and energy to deal with innovative concepts and implement new strategies
(Child, 1974). Consistent with this idea, Bonn et al. (2004) show that board age is negatively
correlated with market value for a small sample of Japanese manufacturers, while its effect on
profitability is less significant. However, the effect on Australian firms is found to be
insignificant using both measures. In a different study concerning US firms, Faleye (2007)
incidentally reveals that board age is a very significant determinant of corporate value.
In addition to board age, three board-related factors are likely to affect a firm’s performance:
board size, board independence, and board stock ownership. Board size is typically considered
to have a negative impact on performance. Lipton and Lorsch (1992) and Jensen (1993) argue
that large boards are less cohesive. Due to free-riding problems, board members may become
less involved in strategic decision-making (Judge and Zeithaml, 1992). This has the adverse
consequence of impairing the board’s ability to initiate strategic change (Goodstein et al., 1994).
Likewise, Gladstein (1984) and Forbes and Milliken (1999) argue that large boards are harder to
coordinate given the large number of potential interactions among group members, and can lead
to fractionalization and in-fighting (Ocasio, 1994). As a result, empirical studies have generally
found that firms with larger boards achieve lower market values (Yermack, 1996; Eisenberg et
al., 1998; Mak and Kusnadi, 2005). In the case of Japanese firms, Bonn et al. (2004) and Aman
and Nguyen (2008) show that board size is also negatively correlated with profitability (ROA).
Board independence is also expected to affect firm performance. The received wisdom is that
independent directors add value since they are more likely to challenge management and adopt
shareholder-friendly resolutions. For instance, they are more likely to accept hostile bids or vote
in favor of ousting managers whose results have been disappointing. Indeed, Weisbach (1988)
reveals that CEO dismissal is more sensitive to poor performance when boards are more
independent. In Japan, board members are classified in two categories: executive (inside) and
non-executive (outside) directors. This second category includes officers sent by controlling or
affiliated companies, or the firm’s main bank(s), and independent directors. While corporate
governance rules have further converged towards the Anglo-Saxon model, Japanese boards
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remain largely dominated by insiders. Hence it is unclear what effect independent boards might
have. Bonn et al (2004) find no systematic relation in relation to performance, but Aman and
Nguyen (2008) obtain a negative correlation between the number of inside directors and firm
performance. To fit the Japanese context, board independence is proxied by the proportion of
executive (inside) directors. Implicitly, the assumption is that the lower the proportion of inside
directors, the more independent the board should be.
Board ownership, measured by the percentage of outstanding shares held by all directors, is
considered to decrease agency conflicts by giving directors powerful incentives to create
shareholder value. The results from Yermack (1996) and Bhagat and Bolton (2008) substantiate
the idea that stock ownership by executives and directors is associated with higher firm value.
Hiraki et al. (2003) and Aman and Nguyen (2008) provide evidence that board ownership is also
positively correlated with firm value in Japan.
In addition to board characteristics, we include a number of other control variables. Institutional
ownership is the percentage of shares held by Japanese financial institutions (i.e. commercial
banks, trust banks, brokerage firms, and life insurance companies). Shleifer and Vishny (1986)
argue that institutional investors are more able to monitor management because of their large
size and ownership concentration, which should induce higher firm performance. Recent studies
have highlighted the significant heterogeneity among financial institutions and suggest that
monitoring benefits vary according to the type of institution (Chen et al., 2007). Nonetheless,
Gedajlovic and Shapiro (2002) find that institutional ownership has overall a strongly positive
effect on the profitability of Japanese firms.
Firm size is measured by the natural log of total assets. Because large firms could be more
difficult to manage and more likely to suffer from a lack growth opportunities, studies often
report a negative association between firm size and firm performance. However, Hu and
Izumida (2008) document the opposite result. This inconsistent finding could be due to the
protracted crisis that has plagued Japan since the burst of the asset bubble. In these
circumstances, large firms seem to have benefited from a size advantage in attracting resources
while small firms have experienced reluctance from banks to provide financial support.
We include the ratio of capital expenditures to sales because higher growth tends to be
associated with higher performance. Prowse (1992) provide strong evidence regarding the
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positive influence of capital expenditures on the profitability of Japanese firms. Using a
different proxy, Weinstein and Yafeh (1998) and Gedajlovic and Shapiro (2002) show that sales
growth is associated with significantly higher operating profits.
The ratio of total debt-to-total assets is included because high leverage might constrain
investments and prevent firms from undertaking valuable projects. In addition, Myers (1984)
argues that debt over-hang could lead to underinvestment (i.e. the firm might prefer to pass up
valuable investments since the benefits would go to debtholders). Consistent with this argument,
Fukui and Ushijima (2007) document a significantly negative effect on performance. Similarly
Weinstein and Yafeh (1998) show that the profitability of Japanese firms is negatively related to
their debt-to-sales ratios while Chen et al. (2003) report that the Q ratio of Japanese firms is
inversely related to their financial leverage.
Consistent with existing studies, firm age is proxied by the number of years since the firm’s
listing. For instance, Saito (2008) show that firm age has a negative impact on Tobin’s Q for
family firms. We also use the number of business segments as an indicator of diversification. A
number of studies document a diversification discount because multi-segment firms may use the
profits from one business segment to subsidize other loss-making segments. However, the
discount may be mitigated if the segments are strongly related (Fukui and Ushijima, 2007).
R&D intensity is measured by R&D expenditures to sales. Since they contribute to the build-up
of intangible capital, R&D expenditures should ultimately enhance the firm’s performance.
Although Nguyen et al. (2010) find that most measures of R&D intensity are negatively
correlated with ROA and the market-to-book ratio, this may not necessarily means that high
R&D firms under-perform. In fact, Fukui and Ushijima (2007) document that R&D intensity has
a positive influence on the profitability and valuation of large Japanese manufacturers.
The firm’s systematic risk in included since a higher exposure to the economic environment
should affect the firm’s performance. In a favorable period, such as the one covered by this
study (which predates the recent global financial crisis) high-beta firms are likely to display a
higher performance. Not surprisingly, Saito (2008) finds that high-beta firms have
underperformed during the period 1990-1998 which coincides with the burst of Japan’s
infamous real estate and stock market bubbles. Following standard practice, beta is estimated
using the market model over the period 2003-2007 with 60 monthly stock returns and a
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minimum of 36 monthly observations. The value-weighted Topix is used as a proxy for the
market portfolio. Finally, all regressions include industry fixed effects.5
2.3 Measurement and determinants of risk taking
To evaluate a firm’s risk-taking behavior, we follow Cheng (2008) and use the variability over
time of three performance measures: ROA, Tobin’s Q and stock returns. This variability is
measured by the standard deviation over the 5-year period 2003-2007. A strong rationale for this
approach is provided by the mean-variance framework. In essence, the mean corresponds to the
firm’s expected performance, while the squared deviation measures the risk of not achieving the
expected level of performance. All three volatility measures should be positively and strongly
correlated. For instance, Wei and Zhang (2006) and Irvin and Pontiff (2009) both find that the
idiosyncratic return volatility of US firms is positively related to their earnings volatility.
Further, we analyze acquisitions that firms have effectively completed. The volume of
acquisition detailed in the cash flow statement is scaled by the firm’s total assets. We also
examine whether the firm has been active in the acquisition market using a dummy variable
whose value is one if the firm made acquisitions regardless of their sizes. Acquisitions are
generally considered risky decisions since they involve complex integration problems (Cheng,
2008). In addition, they tend to be associated with negative abnormal returns for the acquiring
firm (while the target firm’s shareholders are always better off). The problem is particularly
sensitive for Japanese firms which are characterized by strong corporate identities and fierce
loyalty from employees as a result of lifetime employment practices.
To explain cross sectional differences in risk-taking we use a similar set of explanatory variables.
Board age, the main explanatory variable, is expected to be negatively associated with risk
taking. As already mentioned, older managers are less likely to embark on major strategic
changes (Wiersema and Bantel, 1992) or pursue innovative growth strategies (Child, 1974). In
particular, the study of Matta and Beamish (2008) indicates that firms whose CEOs have shorter
horizons are less likely to undertake international acquisitions which are considered to be very
risky. At the core of this issue is the fact that older managers are associated with a greater
aversion to risk (Carlson and Karlsson, 1970; Vroom and Pahl, 1971).
5 Due to the large number of firm characteristics already included as control variables, excluding industry effects does not materially change the estimated effect of board age.
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We also include three board-related variables that may affect the firm’s risk taking behavior.
Board size is expected to have a negative influence. According to studies in organizational
behavior, the size of the decision-making group has a negative effect on risk taking (Kogan and
Wallach, 1964). Sah and Stiglitz (1986, 1991) formalize the idea that group decisions reflect a
compromise among the opposite views of each group member. As a result, large groups end up
selecting average projects whose performance also tends to be more stable. In line with this
prediction, Cheng (2008) shows that firms with larger boards tend to exhibit lower performance
variability while Nakano and Nguyen (2011) suggest that the effect is especially negative when
firms have a lack of growth opportunities.
Regarding the effect of board composition, it stands to reason that since they have less to lose
from the firm’s potential failure, outside directors should exhibit a greater risk tolerance relative
to inside (executive) directors. However, the results of Cheng (2008) provide no indication of a
significant relationship between the percentage of independent directors and risk taking. The
effect of managerial stock ownership is found to be equally ambiguous.
On the other hand, firm size is expected to be associated with significantly lower risk since large
firms have more diversification opportunities. For similar reasons, the firm’s scope represented
by the number of business segments should be associated with a lower level of risk. To
complete the model, we include the ratio of capital expenditures to sales because higher growth
tends to be associated with higher risk. We also include total debt over total assets because high
leverage may induce firms to take more risk (Jensen and Meckling, 1976). Finally, the firm’s
payout ratio (proportion of dividends paid out of operating income) is added while beta is
dropped (since it is obviously a risk measure). As in the performance models, all regressions
include industry dummies.
2.4 Sample description
Table 1 provides descriptive statistics for the sample. Panel A shows that the average age of
company boards in Japan is between 58 and 59.6 Younger boards are typically found in the IT
sector. For example, the board of Nexyz Corp. an internet marketing firm with a turnover of
6 This implies that, contrary to widespread perception, Japanese boards are not necessarily older than US boards. In fact, the average director age is 58.9 in the panel of US directors tracked by Adams and Ferreira (2009). However, the average board age in the US is likely to be slightly lower since firms with smaller boards also tend to have younger directors (for example, hi-tech firms).
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about 6 billion yen consists of 7 directors, 6 of whom are less than 40 years of age. At the other
end, Toda a construction firm and Nippon Television Network have particularly old boards with
an average age of 71 years.
Japanese boards consist on average of 10.3 members, which is close to the figure for US firms
indicated by Coles et al. (2008). Bonn et al. (2004) report a much higher number since their
study is based on a smaller sample of large firms and covers a period during which Japanese
firms used to have much larger boards.7 Yet, some boards can consist of 30 directors, as is the
case of Toyota Motors. More than 50% of board members are insiders, and half the Japanese
firms have no outside director.8 In contrast, the boards of US firms are generally formed with a
majority of independent directors (Yermack, 1996; Boone et al., 2007; Coles et al., 2008;
Adams and Ferreira, 2009).
------------------------------
Table 1 about here ------------------------------
Panel B reveals that the profitability of Japanese firms is similar to that of US firms with an
average ROA of 5.6%. However, the associated volatility is much lower. This result is
consistent with the cross-country analysis of John et al. (2008) which shows that Japanese firms
achieve the lowest level of cash flow volatility. One reason for this low volatility is that
Japanese firms tend to be relatively diversified (with 3 business segments for the median firm).
Another reason might be their level of risk aversion. According to Hofstede (2001) Japan is
characterized by a high degree of uncertainty avoidance.9 Indeed, the associated score is 92,
compared to 46 for the US and 35 for the UK. Even Germany, which is often compared to Japan,
pulls off a relatively low score of 65. Partially reflecting this high risk aversion, acquisitions
over the sample period have been generally low at about 0.3% of total assets, in line with the
small number of transactions. In fact, only half the firms made acquisitions during the 5 year
period 2003-2007. In most cases, the target size was relatively small.
7 See Uchida (2011). Another reason is that our sample includes the smaller companies listed on the second section of the Stock Exchange, which tend to have significantly smaller boards (between one and two fewer directors compared to firms listed on the first section). 8 A study by the Tokyo Stock Exchange shows that almost 60 percent of listed firms had no outside director in 2006. Source: http://www.tse.or.jp/english/rules/cg/white_paper.pdf 9 Chui and Kwok (2008) explain the relatively high level of life insurance consumption in Japan by this high degree of uncertainty avoidance.
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Panel C shows that institutional investors are well represented in the capital of Japanese firms
with a majority of firms showing an institutional stake greater than 19.2 %. The average R&D
intensity is 1.8% which is slightly lower than national R&D indicators (in which aggregate
R&D expenditures are scaled by aggregate revenues). This figure is also lower than in Nguyen
et al. (2010) since R&D inactive firms are not excluded from our sample. Finally, the indicator
of systematic risk is found to be on average very close to 1.
Table 2 presents the correlation coefficients between board age and various firm characteristics.
The bottom line reveals that larger and older firms tend to have older boards. Boards dominated
by insiders also tend to be older. On the other hand, board age appears to decrease with the level
of board ownership. In line with our hypothesis, older boards are associated with lower
performance and risk (proxied by the volatility of performance). Among the other variables, it is
interesting to note that older firms are generally larger and more diversified (measured by the
number of business segments). Firm age is also associated with lower performance and risk, and
higher financial leverage. Not surprisingly, the two risk proxies exhibit a strong positive
correlation with beta. As in Hu and Izumida (2008) financial leverage appears to be associated
with lower operating and market performance, and higher systematic risk.
------------------------------
Table 2 about here ------------------------------
Larger and older firms tend to have larger boards. For instance, Toyota Motors, Japan’s biggest
manufacturer, happens to have the largest board (with 30 directors). Firms with larger boards
also tend to have older directors. Boards dominated by insiders and boards with low board
ownership are characterized by lower performance and risk. On the other hand, institutional
ownership is associated with higher performance and somewhat higher risk. However, it is not
the case for large firms, although the latter tend to have higher institutional ownership. This
absence of correlation between firm size and performance and the relatively high correlation of
firm size with board age suggest that firm size qualifies as a valid instrument to control for the
endogeneity of board age in the performance equations.10
10 A more fundamental argument to explain why firm size is strongly correlated with board age is that large firms have more management layers, which means that employees need more time to reach the top of the organization and are thus older by the time they are eventually appointed to the board. This relation is formally tested in the next section (see Table 4). The absence of a size effect on the performance of Japanese firms is also noted in a number of papers (e.g., Fukui and Ushijima, 2007; Hu and Izumida, 2008).
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3. The effect of board age on firm performance
Our first approach is to run standard OLS regressions between board age and firm performance.
To address potential endogeneity between these two variables, we then run instrumental variable
(2SLS) regressions in which the exogenous variation in board age is isolated using firm size.
Since endogeneity tests suggest that the difference between OLS and 2SLS estimates is not
systematic, we use the more efficient OLS regressions to examine whether the effect of board
age depends on specific firm characteristics.
3.1 OLS regressions
Table 3 presents the regression results of firm performance on board age and control variables.
For both measures of performance, ROA and Tobin’s Q, the effect of board age is negative and
economically significant. In fact, a one standard deviation increase in board age is associated
with a 0.0018 × 3.62 = 0.65% decrease in ROA and a 0.0255 × 3.62 = 9.2 percentage point
decrease in Tobin’s Q. This order of magnitude is comparable to the one that can be inferred
from Faleye (2007) in a study of US firms for which a one standard deviation increase in board
age is associated with a 0.0198 × 4.72 = 9.3 percentage point decrease in Tobin’s Q.
The next column shows that firms with a board age in the first (lower) quartile exhibit an
average ROA 1.23% higher than firms in the two middle quartiles. The difference in ROA
relative to firms in the last (higher) board age quartile is more substantial at about 1.6%.
Similarly, the difference in Tobin’s Q between firms in the lowest and highest board age
quartiles is almost 0.2 point, which represents more than a third of a standard deviation in that
measure of performance (0.2/0.536 = 37%). Taken together, these results substantiate earlier
evidence by Bonn et al. (2004) obtained for a smaller sample of large Japanese firms.
------------------------------
Table 3 about here ------------------------------
Other widely studied governance variables – board size, board independence, and board share
ownership – have no visible impact on firm performance. Only institutional ownership seems to
result in significantly higher ROA and Tobin’s Q. More precisely, a one standard deviation
increase in institutional ownership is associated with a 0.0992 × 14.50 = 1.44% higher ROA. In
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line with other studies, the effect of firm age and financial leverage on both ROA and Tobin’s Q
is significantly negative, while capital expenditures and R&D investments have a positive and
statistically significant effect on Tobin’s Q, but not on ROA.
Overall, the results indicate that board age is one of the most important determinants of firm
performance. In particular, its influence outstrips the effect of board size and independence
which have received greater attention from academics and regulators. Nevertheless, the
endogenous determination of board characteristics (in particular age) and firm performance
could drive the observed relationship without implying any causal effect. To address this
endogeneity problem, we use instrumental variable (2SLS) regressions.
3.2 Instrumental variable regressions
Far from being a miracle solution, this approach usually runs into the thorny problem of
identifying valid instruments, which should be exogenous, strongly correlated with the
endogenous regressor (board age) and uncorrelated with the unexplained variation in
performance (the error term in the performance equations). The challenge associated with
finding valid instruments explains why firm fixed-effect regressions have been proposed as an
alternative solution to endogeneity problems in governance studies (Himmelberg et al., 1999;
Chi, 2005). However, fixed-effects may not be appropriate in the case of board age because
changes in board age are likely to involve other confounding changes in board characteristics
and because board age tends to remain relatively stable over time which makes the identification
of any effect on firm performance particularly difficult.
Fortunately, one variable appears to display the properties of a valid instrument for board age.
First, we have already noted the significant positive correlation between the size of a firm and
the age of its board. This empirical relation is also supported by a solid theoretical argument.
Because large firms involve more intermediate layers of management, employees need more
time to reach the top of the organization. As a result, directors who have progressed from within
the firm’s rank and file are typically older in larger firms. The second condition for a valid
instrument (known as the exclusion restriction) is also likely to be satisfied since the effect of
firm size on firm performance is statistically insignificant.11 In addition, several studies have
11 The effect is insignificant for both ROA and Tobin’s Q (see Table 3). This ensures that firm size can be excluded from the second-stage regression (the exclusion restriction being satisfied).
15
established that performance is unrelated to firm size in Japan (Fukui and Ushijima, 2007; Hu
and Izumida, 2008). Black et al (2006) follow a similar reasoning to justify the use of firm size
as instrument for corporate governance in Korea.12
Table 4 presents the results of two-stage least squares (2SLS) regressions. The first stage
confirms that firm size is a strong determinant of board age. The instrument is highly significant
with a robust F-value of 26.11 which easily exceeds the critical value of 8.96 suggested by
Stock et al. (2002). The partial R2 which measures the fraction of the variation in board age
explained by the instrument net of its effect through the other exogenous variables is also
relatively high at about 2.86%. Hence, our analysis is unlikely to involve (and depend on) the
use of a weak instrument.
------------------------------
Table 4 about here ------------------------------
The first-stage regression also shows that older firms are associated with significantly older
boards.13 On the other hand, firms with a high exposure to the business environment (proxied by
high systematic risk) tend to have younger boards. It is also interesting to note that weak
external control (proxied by a high proportion of inside directors) is associated with a higher
board age, while strong monitoring by institutional investors (proxied by a high percentage
ownership from financial institutions) is associated with a lower board age.
In the second stage, the coefficients on predicted board age are found to be negative for both
performance measures. In comparison with the OLS coefficients, the 2SLS estimates are 3 times
larger for ROA and about 2.4 times larger for Tobin’s Q. The difference is consistent with the
argument that older directors are more likely to retain their positions in well performing firms
since there is less need to reshuffle management when performance is good. In contrast,
underperforming firms should feel greater pressure to change their top managers which is likely
to result in the promotion of a younger generation. In support of this argument, Faleye (2007)
12 Firm size is a strong predictor of better governance in Korea due to the existence of a threshold in the application of tighter governance legislation. Furthermore, as in the case of Japan, firm size does not seem to affect firm value. Both properties ensure that firm size is a valid instrument to control for the endogeneity of corporate governance in regressions involving firm value. 13 Firm age is another strong predictor of board age. However, it cannot be used as instrument since its impact on firm performance is far from negligible (in other words, the exclusion restriction is not fulfilled).
16
provides evidence that the turnover rate of US top executives is negatively related to the firm’s
performance. Kang and Shivdasani (1995) report a similar result for Japan.
It follows that when the endogenous (positive) relation between board age and firm performance
is properly accounted for, the influence of board age is found to be more negative than what is
suggested by simple OLS analysis. Likewise, the positive effect that younger managers may
have on firm performance is partially concealed by the fact that they are more likely to be
promoted to the board of poorly performing companies and less likely to be appointed to the
board of outperforming firms. Nonetheless, the Durbin-Wu-Hausman endogeneity test indicates
that the difference between the OLS and 2SLS estimates is not systematic (the hypothesis that
there is no systematic difference between the two estimates cannot be rejected). As a result, it
seems reasonable to rely on the OLS estimates because of their greater efficiency.14
3.3 Effect of firm characteristics
In this section, we investigate whether the negative impact of board age depends on the firm’s
characteristics. Our approach is to interact the board age variable with a dummy variable
representing each characteristic. For instance, to examine whether the performance of younger
firms is more sensitive to the age of their boards, we define D as the indicator that the firm is
younger than the median firm, and include both D and D × board age in the OLS regressions.
We then compare the coefficients on board age and D × board age.
Table 5 reports these pairs of estimates for both performance measures (in columns) and for
several firm characteristics (in rows). The coefficients on the other variables are not reported in
order to save space. Starting with firm size, the results indicate that the coefficient on the
interaction variable is insignificant for both ROA and Tobin’s Q, which suggests that the
performance of large and small firms is equally sensitive to the age of their boards. Similarly,
the effect of board age does not seem to depend on the firm’s affiliation to a business group.
On the other hand, the firm’s age and growth rate appear to strongly condition the impact of
board age on performance. More precisely, board age has no effect on ROA and Tobin’s Q for
older and low-growth firms (using sales growth and Tobin’s Q as growth proxies). But, the
effect of board age is negative and highly significant for young and high-growth firms. There is
14 2SLS regressions induce a loss of efficiency since only the exogenous variation in board age (i.e. the variation in the endogenous regressor collinear to the instrument) is used for estimating the effect on firm performance.
17
also evidence of a stronger negative board-age effect among high-R&D firms, among firms
using more intangible assets, and among firms operating in high-risk industries (especially when
performance is measured by Tobin’s Q). These results are consistent with the argument that
older managers are less effective in environments characterized by rapid change and a high level
of uncertainty. One explanation suggested by Child (1974) is that they may lack the stamina and
energy to implement new ideas that younger managers are more likely to have.
------------------------------
Table 5 about here ------------------------------
Finally, it appears that board age has a stronger negative effect on ROA (at the 5% level) and
Tobin’s Q (at the 1% level) for firms with high cash balances. A stronger negative effect on
ROA is also detected among firms classified as having more stable shareholdings. These results
suggest that the performance of firms typically associated with greater agency problems (as
indicated by larger cash holdings and stable ownership) is more sensitive to board age.
4. The effect of board age on corporate risk taking
To understand why older boards can have such a negative impact on firm performance, it is
necessary to evaluate their decisions on a range of issues and contrast them with the decisions
that would have been taken by younger directors. Since most of these decisions are not
observable, we rely on examining whether older boards are more reluctant to take risk. Given
the strong theoretical link between risk taking and performance, consistent avoidance of risk
might help explain why firms choose to pass up valuable but risky investment opportunities,
which eventually harms their performance.
Table 6 presents the results of OLS regressions of various risk taking proxies on board age and
firm characteristics.15 The examination of the three measures of performance volatility provides
strong support for the view that older directors prefer to take less risk. This finding is consistent
with the argument that older decision makers tend to choose the safer alternatives (Carlson and
Karlsson, 1970; Vroom and Pahl, 1971). May (1995) explains that older managers are more
reluctant to take risk because of the higher amount of their capital vested in the firm. In the case
15 We recognize that this OLS analysis has inherent limitations. However, instrumental variable regressions cannot be used since the literature offers no assistance in the identification of potential instruments.
18
of Japan, the widespread practice of lifetime employment and deferred compensation among
large companies confirmed by Ono (2010) suggests that executive age is strongly correlated
with the amount of human and financial capital tied to the firm. This could explain the greater
reluctance of older board members to put that patiently-accumulated capital at risk.
------------------------------
Table 6 about here ------------------------------
In terms of economic significance, a one standard deviation increase in board age is associated
with a 0.25% decrease in the volatility of ROA which does not appear to be huge, but is still
significant relative to the standard deviation of the volatility of ROA which is only about 1.7%.
In addition, board age is consistently one of the main determinants of performance volatility.
The influence of the other variables is in line with existing results. For instance, larger boards
are associated with lower risk taking (Nakano and Nguyen, 2011) which is also the case for US
firms (Cheng, 2008). This may explain why firms with larger boards are characterized by lower
performance (Yermack, 1996; Eisenberg et al., 1998; Mak and Kusnadi, 2005). On the other
hand, board independence and ownership have no apparent effect on risk taking which may why
their impact on firm performance is unclear (Himmelberg et al., 1999; Bhagat and Bolton, 2008).
The effect of institutional ownership is consistently positive, thus offering support for the view
of Shleifer and Vishny (1986) that large institutions are better able to monitor management, and
in particular, mitigate its propensity to avoid risk.16 Firm size has a significantly negative effect
on performance volatility, consistent with the greater diversification opportunities available to
larger firms. However, the number of business segments does not appear to produce any
reduction in risk (not already captured by the other control variables such as firm size). In line
with Chan et al. (2000) for US firms and Nguyen et al. (2010) for Japanese firms, R&D intensity
is associated with higher risk, whereas the dividend payout rate is associated with lower risk.
Taken together, the above results indicate that some governance-related variables such as board
age and institutional ownership have as much power to explain the cross section of corporate
risk taking as traditional variables like firm size, and have a much stronger effect than other
characteristics such as the number of business segments or the intensity of capital expenditures.
16 This may also explain why institutional ownership has a strong positive effect on firm performance (see the results in the previous section).
19
However, performance volatility is an indicator of risk based on past observations. As a result, it
could reflect decisions made by previous directors rather than decisions made by the current
board. In that sense, a better proxy may be to use the decision to acquire another firm or part of
its assets. Because acquisitions involve a fair degree of risk, this ex-ante decision is similar to
the portfolio decision an investor can make (i.e., selecting the proportion of risky and risk-free
assets in the portfolio). Accordingly, we use the amount of assets acquired relative to the firm’s
total assets. We also use a dummy indicating that the firm made an acquisition regardless of the
amount involved. Because the acquisition involve a positive amount, we use Tobit regressions
with a lower bound at zero, while the acquisition dummy is fitted using a Probit model.
The results are presented in the last two columns of Table 5. The Probit model clearly indicates
that older boards are less likely to undertake acquisitions (significant at the 5% level) and that
the amount purchased is also likely to be smaller (significant at the 5% level). Consistent with
the view that acquisitions are risky decisions (Matta and Beamish, 2008) larger acquisitions are
more likely to be undertaken by younger boards. Considering the fact that acquisitions have the
potential to induce substantial change, this result is consistent with Wiersema and Bantel (1992)
who argue that innovative strategies are more likely to be initiated by younger managers.
Regarding the role of the other variables, it can be observed that firms with large boards are less
likely to make risky acquisitions, in line with the findings of Cheng (2008), while higher
institutional ownership is associated with a higher probability of undertaking acquisitions,
consistent with Shleifer and Vishny (1986). Interestingly, firms with a higher proportion of
executive directors are more likely to make acquisitions. However, the amount involved is
typically smaller. It is also worthwhile to note that larger and more diversified firms as indicated
by a larger number of business segments are significantly more likely to carry out acquisitions.
5. Conclusion
While older directors benefit from greater experience, their age may have less favorable effects
on firm performance. This consequence has long been suspected in the management literature.
For instance, Child (1974) highlights the lower ability of older managers to carry out innovative
strategies. Subsequent studies have confirmed that management age is associated with a lower
20
rate of strategic change (Wiersema and Bantel, 1992). Likewise, Matta and Beamish (2008)
point out the negative impact on international acquisitions using the related notion of decision
horizon, which normally decreases with the age of the manager.
In essence, the problem appears to stem from the higher degree of risk aversion associated with
older decision makers (Carlson and Karlsson, 1970; Vroom and Pahl, 1971). However, agency
conflicts can exacerbate the problem as well. Because of their age, older managers have less
time to wait for a high-risk project to eventually pay off. In addition, they cannot benefit from
enhancing their reputation if they are not involved in future decisions. This explains why they
prefer investments that deliver a quicker payback instead of aiming for the optimal outcome
which may entail a much higher level of risk (Antia et al., 2010). Another agency problem is
related to their vested capital in the firm. Since older managers tend to have more capital tied to
the firm, it is likely that they will prefer to take less risk (May, 1995). In the case of Japanese
firms where managers typically receive little stock-related compensation, but are entitled to
deferred compensation linked to the length of their employment in the company (Ono, 2010),
the incentive for successful managers who have made it to the top is to cash out by ensuring the
firm’s long-term solvency and by staying on the board as long as possible.
The results we have documented in this paper support the hypothesis that board age has a
negative effect on firm performance. Moreover this effect appears to be stronger after
controlling for endogeneity. Some studies already hint at the negative effect of board age on
firm performance. For instance, the results contained in Faleye (2007) suggest that board age is
negatively associated with Tobin’s Q in the US. Likewise, Bonn et al. (2004) report that board
age is negatively related to the profitability of Japanese firms. However, the significance of
board age is not truly emphasized, and their study is biased towards large firms and does not
include some necessary control variables.
Our first contribution is to present precise estimates of the impact of board age by examining a
large sample of Japanese firms and drawing on a quality dataset from the Nikkei newsgroup.
Furthermore, we adopt an instrumental variable framework to isolate the exogenous variation in
board age. An interesting feature of our analysis is to provide an intuitive explanation for the
difference between the different estimates. Indeed, the reason why the unfavorable effect of
board age may be partially concealed is because management turnover is a decreasing function
of firm performance. This result has been empirically verified in many studies (e.g., Kang and
21
Shivdasani, 1995). Hence, managers who happen to be successful are more likely to retain their
seats on the board. This can generate a positive endogenous relation between board age and
performance that is entirely due to chance. It follows that the OLS regressions probably
underestimate the actual effect of board age on firm performance.
Nonetheless, we find that the effect of board age is more significant than the influence of other
governance variables, such as board size and independence and board equity ownership, which
have received greater attention in the literature. This finding certainly pleads for greater focus on
top management characteristics in future governance research. Our second contribution is to
show that firm performance is particularly sensitive to board age for young and high-growth
firms, R&D-intensive firms, and firms using more intangible assets. This result strengthens the
view that board age is detrimental to firm performance because older decision-makers are less
eager to embrace change and implement innovative strategies. Given the critical impact that
bold decisions can have in a rapidly-changing environment, the typically prudent and measured
decisions made by older boards appear to harm their firm’s ability to compete effectively against
more audacious rivals, resulting in lower performance.
Our last contribution is to provide evidence suggesting that risk taking could be a key factor
underpinning the negative influence of board age on firm performance. For instance, we show
that older boards are less likely to undertake acquisitions, and particularly large ones, which
involve substantial risks. The significantly lower risk associated with older boards may stem
from a personal aversion to risk which is known to increase with age. But it could also be due to
the fact that older directors have less time ahead of them to turn out positive results. In addition,
risk taking may impair the value of their vested capital. Hence the incentives to select low-risk
projects that ultimately undermine firm performance.
22
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Table 1. Descriptive statistics Variables Mean Std Dev 25th pct Median 75th pct Min Max Panel A - Board characteristics Board age 58.63 3.62 56 59 61 40 71Board size 10.32 4.10 7 10 12 3 31Insider ratio 0.560 0.180 0.430 0.540 0.667 0.086 1.000Board ownership (%) 3.797 7.727 0.146 0.476 3.195 0.001 59.661
Panel B - Firm performance and risk proxies ROA 0.056 0.042 0.030 0.048 0.074 -0.141 0.446Tobin’s Q 1.323 0.536 1.010 1.170 1.428 0.744 4.071Vol(ROA) 0.018 0.017 0.008 0.013 0.024 0.000 0.176Vol(Tobin’s Q) 0.229 0.270 0.081 0.145 0.262 0.020 1.711Vol(stock return) 0.094 0.038 0.068 0.087 0.110 0.016 0.295Acquisitions/total assets (%) 0.328 1.471 0 0 0.157 0 3.229Acquisition dummy 0.504 0.500 0 1 1 0 1 Panel C - Other firm characteristics Ln(total assets) 11.69 1.38 10.74 11.45 12.42 7.40 17.05Firm age 34.48 19.23 15 43 55 1 57Number of segments 2.79 1.60 1 3 4 1 14Institutional ownership (%) 22.36 14.50 10.27 19.21 32.25 0.78 70.32Capex/sales 0.048 0.063 0.019 0.037 0.060 0.000 0.864Total debt/total assets 0.215 0.176 0.058 0.185 0.331 0.000 0.821R&D/sales 0.018 0.027 0 0.008 0.028 0.000 0.227Payout ratio 0.154 0.183 0.075 0.133 0.195 -0.489 1.076Beta 0.980 0.504 0.635 0.914 1.281 -0.145 3.229 The sample consists of 1,324 nonfinancial firms listed on the Tokyo Stock Exchange in 2007. Board information (age, size, insider ratio, ownership) is from Nikkei CGES. ROA is operating income over total assets. Tobin’s Q is proxied by the market to book value of assets. Volatility of ROA and Tobin’s Q is calculated over the 5-year period 2003-2007. Volatility of stock return is calculated over the related 60-month period. Acquisitions are averaged over the period 2003-2007. The dummy indicates that the firm has made at least one acquisition. Firm age is the number of years since the firm’s listing. Institutional ownership is restricted to Japanese financial institutions. R&D is assumed to be zero if data is missing. Payout is dividends over operating income. Beta is estimated over 60 months using the Topix (Tokyo price index) as the market portfolio.
27
Table 2. Correlation between board age and firm characteristics
Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Firm size 1
Firm age 2 0.320*
N of segments 3 0.294* 0.245*
ROA 4 -0.058 -0.345* -0.113*
Tobin’s Q 5 0.003 -0.255* 0.001 0.704*
Vol(ROA) 6 -0.199* -0.177* -0.117* 0.211* 0.398*
Vol(Tobin’s Q) 7 -0.216* -0.204* -0.103* 0.344* 0.617* 0.464*
Beta 8 -0.016 0.060 0.094* -0.037 0.208* 0.269* 0.303*
Debt/TA 9 0.257* 0.222* 0.261* -0.294* -0.089* -0.031 -0.156* 0.237*
RD/sales 10 0.084* 0.181* -0.043 0.169* 0.171* 0.114* 0.086* -0.053 -0.238*
Ln(Board size) 11 0.488* 0.190* 0.154* -0.082* -0.080* -0.193* -0.166* -0.139* 0.102* 0.005
Insider ratio 12 0.264* 0.162* 0.054 -0.054 -0.083* -0.063 -0.125* 0.023 0.077* 0.021 -0.070
Board ownership 13 -0.528* -0.576* -0.215* 0.183* 0.073* 0.133* 0.126* -0.102* -0.207* -0.122* -0.261* -0.188*
Instit. ownership 14 0.507* 0.015 0.079* 0.350* 0.329* 0.049 0.097* 0.057 -0.124* 0.251* 0.159* 0.095* -0.199*
Board age 15 0.299* 0.465* 0.145* -0.271* -0.291* -0.243* -0.306* -0.094* 0.021 0.162* 0.200* 0.223* -0.339* 0.045
* indicates significance at the 1% level
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Table 3. OLS regressions of firm performance on board age and control variables Measure of firm performance ROA Tobin’s Q ROA Tobin’s Q Board age -0.0018*** -0.0255*** (-4.02) (-4.57) 1st quartile of board age 0.0123*** 0.1695*** (3.96) (4.02) 4th quartile of board age -0.0037* -0.0293 (-1.87) (-1.19) Ln(board size) -0.0015 -0.0509 -0.0011 -0.0444 (-0.41) (-1.05) (-0.31) (-0.91) Insider ratio 0.0097 -0.0231 0.0100 -0.0229 (1.49) (-0.24) (1.51) (-0.23) Board ownership 0.0005 -0.0015 0.0003 -0.0044 (0.63) (-0.13) (0.37) (-0.40) Institutional ownership 0.0992*** 1.0275*** 0.1009*** 1.0571*** (8.77) (7.47) (9.18) (7.80) Ln(total assets) -0.0023 -0.0228 -0.0031* -0.0364 (-1.40) (-1.03) (-1.94) (-1.66) Ln(Capex/sales) 0.0032 0.0722** 0.0031 0.0713** (1.63) (2.51) (1.61) (2.47) Total debt/total assets -0.0453*** -0.2403** -0.0434*** -0.2076** (-6.07) (-2.60) (-5.96) (-2.24) Ln(firm age) -0.0113*** -0.1036*** -0.0118*** -0.1124*** (-6.26) (-4.64) (-6.18) (-5.05) Ln(N of segments) -0.0003 0.0299 -0.0002 0.0309 (-0.19) (1.18) (-0.11) (1.21) Ln(1+R&D/sales) 0.0036 0.0959** 0.0042 0.1047** (1.30) (2.40) (1.52) (2.57) Beta -0.0003 0.2356*** -0.0002 0.2385*** (-0.11) (6.92) (-0.06) (6.85) F value 17.20*** 11.10*** 16.92*** 10.75***R2 0.3787 0.3329 0.3759 0.3269 The sample consists of 1,324 nonfinancial firms listed on the Tokyo Stock Exchange in 2007. ROA is operating income over total assets. Tobin’s Q is proxied by the market to book value of assets. Board age is from Nikkei CGES. Firm age is the number of years since the firm’s listing. R&D is set to zero if data is missing. The regressions include 31 industry dummies (not reported). The t-ratios in parentheses are adjusted for heteroskedasticity. ***, **,* indicate significance at the 1%, 5% and 10% level.
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Table 4. 2SLS regressions of firm performance on board age and control variables First stage Second stage: effect on performance Board age ROA Tobin’s Q Predicted board age -0.0054 ** -0.0603 * (-2.32) (-1.89) Ln(total assets) 0.6564 *** (5.11) Ln(board size) 0.3735 -0.0002 -0.0379 (1.14) (-0.04) (-0.69) Insider ratio 2.1727 *** 0.0174 * 0.0525 (3.66) (1.87) (0.39) Board ownership -0.0439 0.0003 -0.0030 (-0.66) (0.41) (-0.25) Institutional ownership -3.1603 *** 0.0880 *** 0.9176 *** (-3.98) (9.79) (6.93) Ln(capex/sales) 0.1412 0.0037 * 0.0771 ** (0.85) (1.87) (2.48) Total debt/total assets -1.9033 *** -0.0521 *** -0.3064 *** (-3.01) (-6.35) (-2.71) Ln(firm age) 1.2418 *** -0.0069 * -0.0604 (7.85) (-1.88) (-1.39) Ln(N of segments) 0.1375 0.0001 0.0347 (0.82) (0.07) (1.25) Ln(1+R&D/sales) -0.3067 0.0025 0.0852 ** (-1.22) (0.94) (2.11) Beta -0.6665 *** -0.0026 0.2124 *** (-3.24) (-0.84) (5.51) F value (model) 14.72 *** R2 0.3304 F test (instrument) 26.11 *** Partial R2 0.0286 Durbin-Wu-Hausman test 2.04 1.08 p value 0.1535 0.2978
The sample consists of 1,324 nonfinancial firms listed on the Tokyo Stock Exchange in 2007. ROA is operating income over total assets. Tobin’s Q is the market to book value of assets. Board age is from Nikkei CGES. Firm age is the number of years since the firm’s listing. R&D is assumed to be zero if data is missing. All regressions include 31 industry dummies (not reported). The t-ratios in parentheses are adjusted for heteroskedasticity. The Durbin-Wu-Hausman test evaluates the endogeneity of board age. ***, **,* indicate significance at the 1%, 5% and 10% level. Use both Wu-Hausman Chi2 and Durbin-Wu-Hausman F stats
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Table 5. Interaction of board age and firm characteristics in performance regressions
ROA regression Tobin's Q regression Coeff on
Board age Coeff on
Board age × D Coeff on
Board age Coeff on
Board age × D
Definition of D (indicator of firm characteristic relative to sample median)
Large firms (by assets) -0.0019 *** 0.0001 -0.0241 *** -0.0029 (-3.48) (0.09) (-3.23) (-0.26)
Business group affiliation -0.0020 *** 0.0010 -0.0279 *** 0.0170 (-3.90) (1.28) (-4.57) (1.63)
Young firms -0.0008 -0.0015 ** -0.0098 * -0.0227 ***(-1.57) (-2.05) (-1.65) (-2.60)
High sales growth -0.0003 -0.0024 *** -0.0091 -0.0256 ***(-0.52) (-3.19) (-1.47) (-2.82)
High Tobin's Q 0.0000 -0.0027 *** 0.0064 ** -0.0498 ***(-0.14) (-4.24) (2.17) (-7.03)
High intangible/total assets -0.0013 *** -0.0009 -0.0130 ** -0.0233 **
(-2.91) (-1.20) (-2.42) (-2.50) High R&D/sales -0.0007 -0.0012 * -0.0081 -0.0259 **
(-1.20) (-1.68) (-1.25) (-2.22) High industry volatility -0.0010 ** -0.0014 * -0.0121 * -0.0219 **
(-2.01) (-1.82) (-1.82) (-2.38)
High cash/total assets -0.0007 * -0.0017 ** -0.0095 * -0.0238 ***(-1.76) (-2.50) (-1.89) (-2.75)
High stable shareholdings -0.0010 ** -0.0018 ** -0.0185 *** -0.0154 (-2.09) (-2.23) (-2.69) (-1.49)
The two regressions are ROA or Tobin’s Q = β (Board age) + γ (Board age × D) + η D + ψ (control variables). The coefficient on Board age is β. The coefficient on Board age × D is γ. For each regression, only β and γ are reported next to each other. The control variables are the same as in Table 3. The dummy variable D indicates the firm’s position relative to the median firm. For example, large firm = 1 if the firm’s total asset is above the median. The exception is business group affiliation which indicates that the firm is affiliated with one of Japan’s major business groups. The t-ratios in parentheses are adjusted for heteroskedasticity. ***, **,* indicate significance at the 1%, 5% and 10% level.
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Table 6. OLS regressions of risk-taking proxies on board age and control variables
Volatility of performance Acquisition activity Vol(ROA) Vol(Q) Vol(Return) volume/assets volume > 0
Board age -0.0007 *** -0.0169 *** -0.1304 *** -0.0005 ** -0.0235 ** (-3.93) (-5.50) (-4.52) (-2.22) (-2.04) Ln(board size) -0.0022 -0.0437 * -1.2275 *** -0.0065 *** 0.0047 (-1.59) (-1.91) (-4.60) (-2.91) (0.04) Insider ratio 0.0020 -0.0354 -0.7928 -0.0089 ** 0.4674 ** (0.69) (-0.74) (-1.53) (-2.11) (2.20) Board ownership -0.0001 -0.0035 -0.0813 -0.0001 0.0550 ** (-0.17) (-0.57) (-1.35) (-0.31) (2.27) Institutional ownership 0.0166 *** 0.3987 *** 1.7807 ** 0.0104 * 0.6427 ** (4.23) (5.57) (2.56) (1.76) (2.05) Ln(total assets) -0.0025 *** -0.0356 *** -0.8002 *** 0.0018 ** 0.0766 * (-4.11) (-3.07) (-7.15) (2.11) (1.82) Ln(Capex/sales) -0.0017 * 0.0346 ** -0.6956 *** 0.0006 0.0235 (-1.80) (2.07) (-4.14) (0.51) (0.42) Total debt/total assets 0.0070 ** -0.0971 * 7.9386 *** -0.0036 -0.3108 (2.11) (-1.86) (12.36) (-0.74) (-1.30) Ln(firm age) -0.0017 ** -0.0290 ** 0.1294 -0.0006 -0.0241 (-2.48) (-2.10) (0.97) (-0.54) (-0.43) Ln(N of segments) -0.0005 0.0148 0.1868 0.0040 *** 0.2451 *** (-0.57) (1.08) (1.13) (3.21) (4.00) Ln(1+R&D/sales) 0.0037 ** 0.0537 ** 0.2763 0.0028 * 0.0860 (2.30) (2.29) (1.17) (1.65) (1.59) Payout ratio -0.0056 ** -0.1055 *** -2.8057 *** -0.0074 * -0.2245 (-2.02) (-2.97) (-5.40) (-1.93) (-1.17) F value 11.16 *** 8.55 *** 22.16 *** R2 0.2426 0.2751 0.4092 Likelihood | Wald test 122.69 *** 56.85 *** Pseudo R2 0.0493 0.032
The sample consists of 1,324 nonfinancial firms listed on the Tokyo Stock Exchange in 2007. ROA is operating income over total assets. Tobin’s Q is proxied by the market to book value of assets. Volatility of ROA and Tobin’s Q is calculated over the 5-year period 2003-2007. Volatility of stock return is calculated over the associated 60-month period. Acquisitions are averaged over the period 2003-2007. The associated dummy indicates that the firm has made at least one acquisition. Firm age is the number of years since the firm’s listing. Institutional ownership is by Japanese financial institutions. R&D is set to zero if data is missing. Payout is dividends over operating income. The acquisition/asset equation is fitted using a Tobit regression with a lower bound at zero. The equation involving the acquisition dummy is fitted using a probit regression. All regressions include 31 industry dummies (not reported). ***, **, * indicate significance at the 1%, 5% and 10% level.