CEO Age, Underinvestment, and Agency Costs - Department of ...

CEO Age, Underinvestment, and Agency Costs *
Matthew A. Serfling
Eller College of Management
University of Arizona
Tucson, AZ 85721-0108
serfling@email.arizona.edu
(218) 244-7965
February 2012
Abstract:
Prior theoretical work provides conflicting predictions with respect to how a CEO’s age impacts
his investment decisions. I document that older CEOs invest less than younger CEOs, and that
this finding is concentrated in firms with larger growth opportunities, suggesting an
underinvestment problem. Further, when older CEOs make investments, they appear to make
investments that reduce firm-specific risk. In addition, firms with older CEOs have lower sales
and income growth and earn lower risk-adjusted stock returns. Counter to efficient contracting
which would predict that older CEOs are provided with more equity-based performance sensitive
compensation to mitigate underinvestment problems, I document that older CEOs actually
receive less of this form of compensation but receive higher levels of cash compensation.
Overall, my findings suggest that the age of a CEO can have an important effect on corporate
financial policy choices, firm performance, and the existence of agency costs within a firm.
* I thank Sandy Klasa, Douglas Fairhurst, Ping Tan, J. Scott Judd, and participants at a doctoral student workshop at
the University of Arizona for their helpful comments.

1. Introduction
Because CEOs are the key decision makers in firms, there is interest in the role that
unique CEO attributes play in explaining variation in corporate decisions across firms. Recent
empirical work shows that CEOs’ corporate leverage decisions are related to their personal
leverage decisions (Cronqvist, Makhija, and Yonker (2011)), that CEO overconfidence affects
investment decisions (Malmendier, Tate, and Yan (2011)), that CEOs’ personal life experiences
impact their attitudes toward risks (Malmendier and Nagel (2011)), and CEOs’ education levels
affect the aggressiveness of their corporate strategies (Bertrand and Schoar (2003)).
Prior theoretical work also predicts that an important CEO characteristic that can affect
corporate decisions is a CEO’s age. Specifically, a CEO’s age can impact investment choices.
Market learning models lead to predictions that younger CEOs invest less than older CEOs
because younger CEOs are more risk-averse since they do not have a previous record of
accomplishments, and as such, if they make a bad investment decision, this could markedly
reduce their future career opportunities (e.g., Scharfstein and Stein (1990); Holmstrom (1999)).
In contrast, managerial signaling models predict that older CEOs invest less aggressively
compared to their younger counterparts, as younger CEOs try to signal superior ability to the
market through more and bolder investments (e.g., Prendergast and Stole (1996)). Further, if
older CEOs are more entrenched or have greater influence over the board, then such CEOs could
get away with enjoying a quiet life, resulting in lower investment levels relative to younger
CEOs (e.g., Bertrand and Mullainathan (2003)).
In this paper, I provide evidence on whether a CEO’s age affects his investment
decisions. Further, I investigate whether in instances in which the effect of a CEO’s age on his
investment decisions leads to decisions that could reduce shareholder value, if consistent with
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efficient contracting, firms take steps to mitigate this problem. I study firms listed on Execucomp
between 1993 and 2010 and document that older CEOs are prone to invest less than younger
CEOs. To verify whether this finding is driven by a CEO’s age having a causal impact on his
investment behavior, I examine changes in firm investment activity subsequent to the arrival of a
new CEO. I find that when younger (older) CEOs are replaced by older (younger) CEOs, this
leads to decreases (increases) in a firm’s investment level. These results are consistent with the
prediction that older (younger) CEOs have less (more) aggressive investment styles.
I also document that the difference in investment between older and younger CEOs is
concentrated in firms with above median level investment opportunities. This suggests that firms
with important investment opportunities that are managed by older CEOs could suffer from large
underinvestment problems. Consistent with this notion, I find that sales growth is 6% lower and
operating income growth is 10% lower in firms that are run by CEOs in the top age tercile.
Further indicating that this underinvestment is costly for shareholders, I find that if an investor
were to go long firms with CEOs in the lowest age tercile and short firms in the highest age
tercile, the investor would earn an annualized abnormal return of 6.0%.
I also examine whether there are differences in the riskiness of the investments made by
older versus younger CEOs. I find that older CEOs invest less in research and development,
which tends to be a riskier form of investment. In addition, these CEOs have a greater propensity
to make risk-reducing diversified acquisitions when they engage in takeovers and to run firms
whose sales are more diversified across a greater number of operating segments. Put together,
these findings are consistent with older CEOs preferring less risky investments. Further, given
that diversification has been shown to be associated with reduced firm value (e.g., Berger and
Ofek (1995); Denis, Denis, and Sarin (1997); Denis, Denis, and Yost (2002)), these results
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suggest that the investment decisions made by older CEOs are not aligned with the interests of
shareholders.
Next, I investigate whether the board of directors of firms that are managed by an older
CEO recognize the possibility of underinvestment problems and take steps to mitigate these
problems using compensation incentives. Efficient contracting predicts that if the board
recognizes the potential for underinvestment, then they will award the CEO with more equity-
based performance sensitive compensation to mitigate underinvestment (e.g., Murphy (1986);
Rajgopal and Shevlin (2002); Aggarwal and Samwick (2006)).
For my sample of firms, I first verify whether a higher level of performance sensitive
equity-based compensation is associated with greater firm-level investment. Specifically, I
examine the relation between firm investment and the delta of a CEO’s annual stock option and
restricted stock grants, the dollar change in the CEO’s wealth resulting from these grants if the
firm’s stock price increases by one percent. I find that for both older and younger CEOs, a higher
delta indeed leads to greater investment.
Subsequently, I investigate whether the delta of the CEO’s annual stock option and
restricted stock grants differs between older and younger CEOs. Inconsistent with the efficient
contracting prediction that firms provide CEOs with more equity-based performance sensitive
compensation when the potential for underinvestment is greatest, I document that firms with
older CEOs provide their CEOs with annual stock option and restricted stock grants that have a
lower delta. Further, I find that older CEOs are provided with a higher annual cash salary,
indicating that these CEOs do not receive less equity-based compensation because their overall
pay level is lower, but instead, the lower level of equity-based compensation is the result of a
substitution of performance-sensitive compensation for compensation that is not sensitive to
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performance for these CEOs. A potential explanation for why older CEOs receive less equity-
based performance sensitive compensation and a greater annual cash salary than younger CEOs
do is that older CEOs may have greater power and influence over the board. As such, these
CEOs could have the ability to award themselves with compensation that is less sensitive to firm
performance. This would be consistent with managerial power theories that predict that since
performance sensitive compensation ties more of the CEO's wealth to the firm's performance,
undiversified, risk-averse CEOs who have enough power over the board to influence the
structure of their compensation will award themselves less performance sensitive compensation
(e.g., Bebchuk, Fried, and Walker (2002); Bebchuk and Fried (2003); Cheng and Indjejikian
(2009)).
Overall, my paper makes several contributions. First, my study increases the
understanding of the role of CEO personal characteristics on corporate financial policies. Prior
work has documented that CEO fixed effects explain a significant portion of the variation in
corporate policies and accounting disclosure (Bertrand and Schoar (2003); Bamber, Jiang, and
Wang (2010)), CEOs have leverage preferences (Cronqvist, Makhija, and Yonker (2011)), CEO
confidence matters in financing decisions (Malmendier, Tate, and Yan (2011)), and CEO
behavioral traits help explain compensation structure (Graham, Harvey, and Puri (2010)). I
provide evidence that the age of the CEO is an important characteristic that can impact financial
policy decisions. Specifically, I document that older CEOs invest less and make less risky
investments than younger CEOs, and that this potentially leads to underinvestment and reduced
performance in firms with large growth opportunities.
In addition, I shed further light on how managerial power can result in CEOs influencing
the structure of their own compensation, producing a greater divergence in the interests of CEOs
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and shareholders and imposing substantial costs on shareholders through foregone profitable
investments (e.g., Bebchuk, Fried, and Walker (2002); Bebchuk and Fried (2003); Grinstein and
Hribar (2004); Garvey and Milbourn (2006); Cheng and Indjejikian (2009); Bebchuk, Cremers,
and Peyer (2011)). My evidence suggests that, on average, the influence of older CEOs
potentially results in these individuals receiving a lower amount of equity-based performance
sensitive compensation, and that this plays a part in the underinvestment problem and the
underperformance of firms managed by an older CEO.
I have organized the rest of the paper as follows. I provide an overview of previous
empirical and theoretical research related to the age of the CEO and develop hypotheses and
related empirical predictions in section 2. In section 3, I present the sample selection process and
summary statistics. In section 4, I report the empirical findings. Lastly, in section 5, I conclude
the paper.
2. Related literature, hypothesis development, and empirical predictions
2.1 CEO age and investment activity
Prior theoretical work provides two competing views about how executives’ age could
influence their investment decisions. First, Holmstrom (1999) and Scharfstein and Stein (1990)
develop market learning models, which lead to the prediction that younger CEOs are more risk-
averse and therefore, invest less aggressively than older CEOs. Specifically, these papers argue
that because younger CEOs do not have a previous record of accomplishments as CEO, they may
face greater labor market scrutiny if they make a bad investment decision, which may
significantly reduce future career opportunities. As a result, younger CEOs are reluctant to
pursue an aggressive investment strategy.
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Second, Prendergast and Stole (1996) develop a managerial signaling model, which leads
to the prediction that younger CEOs make more and bolder investments compared to their older
counterparts. Prendergast and Stole (1996) argue that in an effort to signal to the market that they
are of superior ability, younger CEOs pursue a more aggressive investment style. Further, older
CEOs are reluctant to change investment behavior, as this may indicate that their previous
investment decisions were incorrect. Consequently, younger CEOs pursue a more aggressive
investment style compared to the investment style of older CEOs. 1
In addition to these two models, it is possible that there are CEO characteristics beyond
risk-aversion and career concerns that result in older CEOs investing at a level below that of
younger CEOs. Notably, prior work suggests that entrenched CEOs make less risky investments
and also invest less, preferring a quiet life (e.g., Amihud and Lev (1981), Shleifer and Vishny
(1989), and Gompers, Ishii, and Metrick (2003), Bertrand and Mullainathan (2003)). If older
CEOs are more likely to have greater influence within the firm, they could get away with
pursuing a quiet life, resulting in lower investment levels relative to their younger counterparts. 2
There are two contemporaneous papers that also empirically examine the relation
between CEO age and firm-level investment. Yim (2010) provides evidence that younger CEOs
are more likely to make acquisitions. She argues that because there are permanent increases in
CEO compensation following an acquisition, this incentivizes younger CEOs to make more
1 The prediction made by managerial signaling models is related to the horizon problem where older CEOs near
retirement will sacrifice investment in long-term projects that are good for the firm's long-term performance in
exchange for short-term projects that temporarily boost short-term performance (e.g., Smith and Watts (1982);
Dechow and Sloan (1991); Cheng (2004); Antia, Pantzalis, and Park (2010)). However, the horizon problem only
addresses whether CEOs in their final years of tenure reduce investment. It does not address whether the level or
riskiness of investments differs between older and younger CEOs.
2 Other studies documenting potential reasons why the age of the CEO could affect investment behavior include
Levi, Li, and Zhang (2010) who report that younger CEOs are more aggressive during mergers and acquisitions and
attribute their findings to younger CEOs having higher levels of testosterone. Roberts and Rosenberg (2006) voice
concerns of reduced energy levels in the elderly, and Taylor (1975) finds that younger managers are more confident
in their decisions.
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acquisitions earlier in their careers. Li, Low, and Makhija (2011) examine the differences in
investment behavior between older and younger CEOs by examining plant-level investment
decisions and show that older CEOs have a less aggressive investment style compared to
younger CEOs.
2.2 The relation between investment activity and performance sensitive compensation
Several theoretical studies predict that linking CEO compensation to performance can
lead to CEOs making optimal investment decisions. Some of these studies include Lambert
(1986) in the context of CEOs dismissing positive net present value projects due to perceived
risks, Campbell, Chan, and Marino (1989) in the context of information asymmetry about CEO
ability and project value, Smith and Watts (1982) in the context of growth opportunities,
Hirshleifer and Suh (1992) in the context of risk-aversion, risky projects, and monitoring, and
Bizjak, Brickley, and Coles (1993) in the context of asymmetric information and stock price
manipulation. In addition, Aggarwal and Samwick (2006) show that investment activity is
increasing in the use of performance sensitive compensation.
Empirically, Smith and Watts (1982), Gaver and Gaver (1993), and Baber, Janakiraman,
and Kang (1996) report that firms with more investment opportunities compensate CEOs with
more performance sensitive compensation. Guay (1999) finds that when firms have more
investment opportunities, CEOs will not only have more performance sensitive compensation,
but they will also have more compensation that is sensitive to equity risk. Consistent with this,
Ryan and Wiggins (2001), Rajgopal and Shevlin (2002), and Coles, Daniel, and Naveen (2006)
document that CEOs whose wealth is more sensitive to stock returns and stock return volatility
make more risky investments. Moreover, Barker and Mueller (2002) show that R&D spending is
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greater in firms whose CEO has more wealth invested in the firm, and Datta, Iskandar-Datta, and
Raman (2001) report that CEOs with more equity-based compensation make better acquisition
decisions. Lastly, Cheng (2004) finds that CEOs with greater option compensation are less likely
to cut long-term R&D expenditures in their final years in office as a means to inflate short-term
earnings.
In addition to work directly examining the relation between CEO age and compensation
policies, there are studies that examine the relation between CEO tenure and compensation
policies. Cremers and Palia (2010) document that there is a positive relation between CEO tenure
and both pay level and pay-performance sensitivity, which is consistent with a career concerns
hypothesis that CEOs in the later stages of their careers require more explicit incentives to
replace implicit labor market incentives. However, Zheng (2010) shows that equity-based
compensation increases during the early years of tenure for outside CEOs, supporting a learning
hypothesis that says boards use equity-based compensation as a tool to learn about the CEO’s
abilities. 3
3 These results in Cremers and Palia (2010) and Zheng (2010) differ primarily due to the different definitions of
equity incentives that the authors use to construct a measure of equity-based pay-performance sensitivity. In the first
study, the authors use new equity awards and all outstanding equity awards to calculate the CEO’s equity-based payperformance
sensitivity, whereas in the latter study, the author only uses new equity awards. In the first study, the
positive relation between tenure and equity-based pay-performance sensitivity could be driven by the accumulation
of outstanding awards over time. This approach would be appropriate if the researcher wants to capture the CEOs
total equity incentives. However, similar to the latter study, I am interested in how firms manage CEO equity
incentives to incentivize the CEO to make optimal investment decisions. Since Core and Guay (1999) show that
firms will use annual equity grants to manage the level of CEO equity incentives when incentives deviate from the
optimal level, only using new equity grants to calculate equity-based pay-performance sensitivity is most
appropriate for my study.
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2.3 Hypotheses and empirical predictions
The discussion in the prior sections about the effect of CEO age on a firm’s investment
level leads to two competing hypotheses.
Hypothesis 1: The aggressiveness of a CEO’s investment behavior is increasing in his
age.
Hypothesis 2: The aggressiveness of a CEO’s investment behavior is decreasing in
his age.
These hypotheses generate the empirical prediction that the level of firm investment is positively
(negatively) associated with the CEO’s age.
In firms with important growth opportunities, the extent to which a CEO has an
aggressive investment style has a larger effect on his firm’s investment practices, given that
when a firm has an abundance of investment opportunities, it is easier for a CEO with an
aggressive investment style to invest heavily. Consequently, if the age of a CEO has a positive
effect on the amount of investment he undertakes and this is driven by younger CEOs having a
higher level of risk aversion, then in firms with large growth opportunities older CEOs should
invest even more relative to younger CEOs. Likewise, if a CEO’s age has a negative effect on his
investment activity and this occurs because younger CEOs make more and bolder investments to
signal their ability or due to older CEOs preferring a quiet life, then in firms with larger
investment opportunity sets, the finding that older CEOs invest less should be more pronounced.
Thus, the study’s two main hypotheses also lead to the empirical prediction that the effect of
CEO age on a firm’s investment level is more pronounced in firms with higher growth
opportunities.
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In addition, since firm diversification reduces firm-specific risk (e.g., Mansi and Reeb
(2002); Duchin (2010)), the age cohort with the smaller risk tolerance and lower propensity to
invest aggressively will prefer that the firm have a more diversified asset base. Thus, Hypotheses
1 and 2 also predict that younger (older) CEOs are more likely to make diversifying acquisitions
when they do make acquisitions and more likely to have business operations in more operating
segments.
Different forms of investment are associated with varying levels of risk. R&D
expenditures are a riskier form of investment than are capital expenditures due to their higher
degree of uncertainty regarding future benefits (e.g., Kothari, Laguerre, and Leone (2002); Coles,
Daniel, and Naveen (2006); Cassell, Huang, Sanchez, and Stuart (2012)). Consequently,
Hypotheses 1 and 2 also predict that younger (older) CEOs prefer to invest in less R&D intensive
projects.
Efficient contracting predicts that firms use equity-based performance sensitive
compensation as a tool to align the incentives of CEOs with those of shareholders and mitigate
the potential for underinvestment. This leads to the study’s third hypothesis.
Hypothesis 3: If either younger or older CEOs underinvest, then firms try to mitigate this
problem by providing the underinvesting group of CEOs with additional
equity-based performance sensitive compensation.
The hypothesis results in the prediction that if younger (older) CEOs underinvest then there will
be a positive relation between the amount of equity-based performance sensitive compensation
that the CEO receives and whether he is a younger (older) versus an older (younger) CEO.
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3. Sample description and variable construction
3.1 Sample selection
I begin my sample selection process by identifying all CEOs on Execucomp of firms with
fiscal year-ends between 1993 and 2010. I use Execucomp to collect data on CEO age, tenure,
and compensation measures. I discard observations if the CEO owns more than 10% of the
shares outstanding because incentive compensation for and the investment decisions of these
CEOs are unlikely to be similar to those of other CEOs with less stock ownership. 4 Moreover, I
eliminate CEO-year observations in which the CEO only serves a partial year. I exclude utilities
(SIC 4900-4999), financials (SIC 6000-6999), and quasi-public firms (SIC codes greater than
9900). I collect accounting variables from Compustat, return data from CRSP, and data on M&A
transactions from Thomson’s SDC Spectrum Mergers and Acquisitions database. After merging
the databases and deleting observations with missing values of variables needed for the main
regressions, the sample has 13,769 CEO-year observations.
3.2 Measuring investment activity and investment opportunities
The primary variables of interest include the firm's investment activity, the firm's
investment opportunity set, the CEO's age, and the CEO's equity-based performance sensitive
compensation. To measure the firm's investment activity, I calculate investment as the sum of
capital expenditures and R&D expenditures divided by total assets at the beginning of the year. 5
To proxy for the firm's investment opportunity set, I use two different industry proxies.
Using industry proxies for growth opportunities instead of firm-level measures mitigates the
4
As a robustness check, I rerun all my tests excluding the restriction that CEOs own less than 10% of the firm. All
of my results continue to hold.
5
As a robustness check, I also calculate investment activity as the sum of the firm's capital expenditures, R&D
expenses, acquisition related expenses, and advertising expenses divided by total assets at the beginning of the year
(Faulkender and Petersen (2010)). The results are robust to using this alternative.
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potential problem that a firm’s current investment opportunity set is a function of the firm’s
investment decisions. For the first measure, I calculate each industry’s market-to-book ratio as
the median of each firm’s market-to-book ratio in each industry every year. I define industries at
the three-digit SIC level. I calculate a firm’s market-to-book ratio as the book value of assets
plus the market value of equity less the book value of equity less balance sheet deferred taxes
divided by the total book value of assets. I calculate the book value of equity as the book value of
shareholder equity less the value of preferred stock.
For the second measure, I calculate each industry’s factor score as the median of each
firm’s factor score in each industry every year. I define industries at the three-digit SIC level.
Following Baber, Janakiraman, and Kang (1996) and Guay (1999), I create a growth factor
score to proxy for the firm’s investment opportunity set by creating one variable from combining
the firm’s three-year average investment intensity, the firm’s three-year annual growth rate in
market value of assets, the firm’s market-to-book ratio, and the firm’s R&D expenditures. 6
To analyze how the influence of CEO age on investment activity varies with the firm's
investment opportunity set, each year, I partition firms into two groups based on whether the
firm has above or below median investment opportunities in that year based on the proxy used. I
then create indicator variables that equal one if the firm is in the group with the highest
investment opportunities and zero otherwise.
3.3 CEO age proxies and equity-based performance sensitive compensation
Next, I obtain the age of the CEO from Execucomp and create a discrete and continuous
measure for CEO age. By creating two variables that capture the age of the CEO, I want to show
6 For more details regarding the methodology for creating the growth factor score and specific variable definitions,
see Baber, Janakiraman, and Kang (1996).
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that the results are robust to the measure used to capture CEO age, and that the variable
definition is not driving the results. For the first measure, each year, I rank CEOs into terciles
based on the CEO's age. I then create an indicator variable, Older CEO, that is equal to one if the
CEO’s age falls into the top age tercile and that is equal to zero if the CEO’s age falls into the
bottom two age terciles. Due to ease of interpretation, I base most of my discussion on this
measure. For the second measure, I simply use the CEO's age in the given year.
To measure equity-based pay-performance sensitivity, I follow the method outlined in
Core and Guay (2002) to calculate the CEO's delta of new option and stock awards. Delta is the
dollar change in the CEO's option and stock grant for a 1% change in the firm's stock price.
Following their method, I calculate stock option values and their sensitivity to stock price based
on the Black-Scholes formula adjusted for dividend payouts (Black and Scholes (1973); Merton
(1973)).
Table I presents summary statistics of the data. The mean level of investment is 10.6%.
The average CEO is 55 years old and has been CEO for 6.8 years. Since I am analyzing
Execucomp firms, which correspond to S&P 1500 firms, the firm characteristics are reflective of
larger, more profitable firms.
4. Empirical results
4.1 Univariate results
Table II compares CEO and firm characteristics between older and younger CEOs. I
classify a CEO as older if his age is in the top tercile in a given year, whereas I classify a CEO as
younger if his age is in the bottom two terciles in a given year. This table shows that both firm
and CEO characteristics differ significantly between the subsamples based on whether a CEO is
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older or younger. Thus, controlling for firm and CEO characteristics in multivariate regressions
is important. Table II also documents univariate results suggesting that older CEOs invest less
and receive less equity compensation than younger CEOs.
An older CEO is on average 62.5 years old with tenure of 9.6 years, whereas a younger
CEO is on average 51.3 years old with tenure of 5.6 years. Moreover, 13% of older CEOs are
classified as founders of the firm, while only 10% of younger CEOs are classified as founders. In
addition, older CEOs manage larger, more mature, more levered, and less profitable firms, with
slightly lower growth opportunities. Since older CEOs manage larger firms, this partially
explains why older CEOs receive more compensation, as older CEOs receive larger annual
salaries, receive more total compensation, and have higher portfolio Deltas.
4.2 Multivariate results
In this section, I test Hypotheses 1 and 2 that the aggressiveness of a CEO’s investment
behavior is increasing (decreasing) in his age. I first report regressions of investment activity on
CEO age and investment opportunities to analyze differences in investment behavior between
older and younger CEOs. I then investigate the nature of the investments made by older and
younger CEOs and whether differences in investment activity translate into differences in firm
performance. Lastly, I test efficient contracting predictions and document whether firms
compensate older CEOs in a manner that is consistent with firms attempting to mitigate potential
underinvestment problems.
In all regressions, I measure the dependent variable at time t, while I measure the
independent variables at time t-1 to reduce the possibility of reverse causality. Since investment
activity and the average level of awarded equity-based performance sensitive compensation is
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likely to vary by year and industry, in all regressions, I include year and industry fixed effects in
my models. Moreover, all t-statistics are calculated using standard errors clustered at the firm
level.
4.2.1 The relation between CEO age, investment opportunities, and investment activity
To test Hypotheses 1 and 2 that investment behavior becomes more or less aggressive as
the CEO ages, I examine the difference in investment activity between older and younger CEOs.
Panel A of Table III provides the regression results of firm-level investment activity on CEO age
and other control variables. The dependent variable in all six models is the sum of R&D
expenditures and capital expenditures divided by the firm’s book value of assets.
I control for CEO tenure because it is correlated with CEO age (0.32), and CEO tenure is
likely to also impact the investments made by a CEO. Thus, by controlling for CEO tenure, I
isolate the effect that is unique to CEO age. I also include the delta of the CEO’s portfolio, which
measures the change in CEO wealth given a 1% change in their firm’s stock price. Following
Faulkender and Petersen (2010), I control for firm size, as measured by the firm’s market value
of assets, the firm’s market-to-book ratio, and the firm's pre-investment profits scaled by book
value of assets. I calculate the firm's pre-investment profits as the sum of earnings before
interest, taxes, depreciation, and amortization (EBITDA), R&D expenses, and advertising
expenses. Moreover, I include an indicator variable that is equal to one if a firm is in an industry
with above median growth opportunities, as proxied by median industry market-to-book and
median industry growth factor score (Baber, Janakiraman, and Kang (1996)).
In addition to the variables used by Faulkender and Petersen (2010), I also include the
firm's market leverage and the firm's three-year average dividend yield. Since a firm’s
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investment activity depends on available capital and since Yermack (1995) and Dechow, Hutton,
and Sloan (1996) hypothesize that liquidity constrained firms may compensate CEOs with stock
options rather than cash compensation, I include the firm's cash holdings in all regressions. I also
include firm age and whether the CEO is a founder. To determine if the CEO is a founder, I
follow the process outlined in Bebchuk, Cremers, and Peyer (2011) and define a CEO as a
founder if the CEO was CEO of the firm when the firm first began trading. 7
The results from Models 1 and 2 show that older CEOs invest less than younger CEOs
do. This result is consistent with Hypothesis 2 that predicts that the aggressiveness of CEOs’
investment behavior is decreasing in CEO age. The coefficient estimates from Model 1 imply
that an older CEO invests 0.46 percentage points below the investment level of younger CEOs.
Since the average investment level of younger CEOs is 11.14%, older CEOs invest 4.13% less
than younger CEOs do.
In Models 3-6, I interact proxies for CEO age and proxies for whether a firm has high
growth opportunities to determine whether the underinvestment of older CEOs is a potential
source of agency costs. To classify firms as those with high growth opportunities, in Models 3
and 4, I create an indicator variable that is equal to one if the firm is in an industry with an above
median market-to-book ratio. In Models 5 and 6, I create an indicator variable that is equal to
one if a firm is in an industry with an above median industry growth factor score. Models 3-6
indicate that it is only in firms with above median growth opportunities that older CEOs invest
less than younger CEOs. 8 The coefficient estimates from Model 3 imply that an older CEO of a
7 All variables are winsorized at the 1% and 99% levels and adjusted to 2009 dollar values. However, variables that
are naturally censored at zero are only winsorized at the 99% level.
8 Since firms with above median growth opportunities have higher investment levels compared to firms with below
median growth opportunities (13.32% vs. 7.96%), the result that older CEOs invest less than younger CEOs do only
in high growth firms could be a mechanical relation. To check if this is the case, I partition the sample into firms
with above median investment opportunities and those with below median investment opportunities and run separate
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high growth firm invests 0.90 (=0.09 + 0.81) percentage points, which is equivalent to 6.44%,
less than younger CEOs managing similar firms. Since investment should typically have a larger
impact on firm value in high growth firms, this underinvestment is a potential source of
shareholder value destruction.
Consistent with other studies, I document that firms that are more profitable and firms
with higher cash holdings have higher levels of investment. In addition, firms that pay higher
dividends invest less. A percentage point increase in the firm's dividend yield, results in a 0.67
percentage point decrease in investment. This is equivalent to a 6.32% decrease from the mean
investment level.
In Panel B of Table III, I provide some insights on whether CEO power might in part
drive the results in Panel A. If older CEOs prefer a quiet life and want to pursue a less aggressive
investment style, then older CEOs with power over the board might invest less so that they can
pursue the quiet life (Bertrand and Mullainathan (2003)). Older CEOs who also have long tenure
at their firm may be more likely to have power and influence over the board. In Panel B of Table
III, I examine whether the results in Panel A may in part be driven by CEO power by interacting
the two proxies of CEO age with the mean-adjusted logarithm of CEO tenure. Since older CEOs
only invest less than younger CEOs in firms with above median investment opportunities, in
Models 1 and 2, I limit the sample to those firms with above median industry market-to-book
ratios, and in Models 3 and 4, I restrict the sample to those firms with above median industry
factor scores. The dependent variable in all four models is the sum of R&D expenditures and
capital expenditures divided by the firm’s book value of assets.
regressions. The results continue to indicate that older CEOs only significantly invest less than younger CEOs do in
high growth firms.
17

The results in Models 1-4 show that the negative relation between CEO age and
investment is more pronounced when CEOs have longer tenure. In fact, the coefficient estimates
from Models 1 and 3 imply that older CEOs only invest less than younger CEOs do when they
have longer tenures. Overall, the results suggest that when older CEOs have greater power due to
being with a firm longer, they use their influence to pursue a less aggressive investment style and
the quiet life. In addition, since older CEOs have almost twice the tenure length as younger
CEOs, this further supports the notion that there exists an underinvestment problem and agency
costs associated with older CEOs.
To further investigate how CEO age affects the aggressiveness of the CEO’s investment
behavior, in Panels C and D of Table III, I decompose investment into R&D expenditures and
capital expenditures and rerun the regressions from Panel A. In Panel C, the dependent variable
is R&D expenditures divided by total assets, and in Panel D, the dependent variable is capital
expenditures divided by total assets. The results of the two panels show that the lower investment
level of older CEOs is driven by older CEOs making fewer investments in R&D. The coefficient
estimates from Model 3 of Panel C imply that in high growth firms, an older CEO invests 0.63
percentage points less in R&D than younger CEOs do. The average investment in R&D by
younger CEOs is 7.55%, which implies older CEOs invest 8.34% less in R&D than younger
CEOs do. Since R&D is generally considered a more risky form of investment (e.g., Kothari,
Laguerre, and Leone (2002); Coles, Daniel, and Naveen (2006); Cassell, Huang, Sanchez, and
Stuart (2012)) and investment in R&D is a large component of value in high growth firms, this
finding further supports the hypothesis that the aggressiveness of CEOs’ investment behavior is
decreasing in his age and costly to shareholders.
18

An alternative explanation for the Table III results is that there is a matching process
between firms and CEOs, where older CEOs choose to work for firms with lower investments
rates or firms with lower investment rates choose to hire older CEOs. To further test the
hypothesis that older CEOs invest less than younger CEOs due to traits that are specifically tied
to the age of the CEO, in Table IV, I examine investment activity around CEO turnover. If older
CEOs truly drive lower investment rates, then when an older CEO replaces a younger CEO,
firm-level investment should significantly decline. I use two proxies to capture the difference in
ages between the newly hired CEO and the recently departed CEO. The first proxy is simply the
age of the hired CEO less the age of the departing CEO. I call this variable the Age Difference.
For the second proxy, I rank Age Difference into terciles, where the highest tercile represents
turnovers where the new CEO is older than the replaced CEO. I then create indicator variables
for the second and third terciles and call these variable Medium Age Difference and Largest Age
Difference, respectively.
Table IV provides further evidence that older CEOs invest less than younger CEOs. In
Models 1 and 2, I measure the change in investment as the investment rate in the year after the
CEO was hired less the investment rate one year before the CEO was hired. In Models 3 and 4, I
measure the change in investment as the two-year average investment rate over the two years
after the CEO was hired less the two-year average investment rate over the two years before the
CEO was hired. All models indicate that when older CEOs replace younger CEOs, firm-level
investment activity decreases substantially. For example, if there is a two standard deviation
increase in Age Difference (2*9.8), then the coefficient estimates from Model 1 imply that a 60
year-old CEO replacing a 40 year-old CEO is expected to cut investment by 0.98 percentage
points. Since the mean investment rate in the year preceding the turnover is 10.8%, this translates
19

into a 9.1% cut in investment. Using the change in the two-year average investment rate, the
coefficient estimates from Model 3 imply that a two standard deviation increase in Age
Difference translates into a reduction in investment of 11.3%. Overall, the results suggest that
when an older (younger) CEO replaces a younger (older) CEO, the new CEO decreases
(increases) investments.
4.2.2 The relation between CEO age and the nature of investments
Tables III and IV document that older CEOs invest significantly less than younger CEOs
do in firms with the above median investment opportunities, suggesting that the investment
behavior of older CEOs may conflict with the interests of shareholders. In this section, I examine
the nature of the investments made by older and younger CEOs to see if the effect of CEO age
on investment behavior extends beyond the CEO’s choice of investment level. An observable
investment choice is whether the CEO diversifies the firm’s asset base. Since diversification has
been shown to reduce firm value (e.g., Berger and Ofek (1995); Denis, Denis, and Sarin (1997);
Denis, Denis, and Yost (2002)), if older CEOs are more likely to diversify the firm, this would
provide further evidence that greater age is likely to create agency costs between managers and
shareholders.
I only study firms with above median investment opportunities, as it is in these firms that
the underinvestment problem exists. In Table V, I present the second stage results from a
Heckman Two-Step selection model. In the first stage, I model the choice of whether the firm
makes an acquisition in a given year. 9 Given a firm makes an acquisition in a given year, in the
second stage, I model whether the acquisition is a diversifying acquisition. A diversifying
acquisition occurs when the target firm is not in the same two-digit SIC industry as the acquirer.
9 First stage results are in Table I of Appendix A.
20

The sample selection process and the selection of control variables are influenced by Masulis,
Wang, and Xie (2007). To enter the acquisition sample, the acquisition must be completed, and
the acquirer must control less than 50% of the target prior to the acquisition announcement and
own 100% of the target after the acquisition is completed. In addition, the deal value must be
greater than $1 million and at least 1% of the acquirer’s market value of equity measured on the
11 th trading day prior to the acquisition announcement.
In Models 1 and 2, I limit the sample to those firms with above median industry market-
to-book ratios, and in Models 3 and 4, I restrict the sample to those firms with above median
industry factor scores. The coefficient estimates represent marginal effects. Overall, the results
indicate that older CEOs are more likely to make diversifying acquisitions compared to younger
CEOs. The coefficient estimates from Model 1 imply that older CEOs are 6.8% more likely to
acquire a firm that does not share the same two-digit SIC industry as their firm. Thus, the Table
V findings suggest that when older CEOs make investments, they are more likely to make
investments in an effort to reduce the firm’s overall risk.
As an additional test, I examine the relation between the age of the CEO and firm-level
diversification. I collect business segment data from the Compustat Industrial Segments Tapes.
To capture firm level diversification, I count the number of different business segments that the
firm operates in, and I create a Herfindahl Index that captures revenue concentration across
segments. I define the Herfindahl Index as the sum of the square of segment sales divided by the
square of firm sales.
Table VI reports the results of Tobit regressions of firm-level diversification on CEO age
in firms with the greatest investment opportunities. In Models 1-4, I limit the sample to those
firms with above median industry market-to-book ratios, and in Models 5-8, I restrict the sample
21

to those firms with above median industry factor scores. The dependent variable in Models 1, 2,
5, and 6 is the number of operating segments that the firm operates in, and in Models 3, 4, 7, and
8 the dependent variable is the Herfindahl Index. The coefficient estimates represent marginal
effects. The results show that older CEOs manage more diversified firms, as captured by a
greater number of operating segments and a lower Herfindahl Index. For example, the coefficient
estimates from Models 1 and 3 indicate that older CEOs manage firms with 0.20 more business
segments and a Herfindahl Index that is 0.044 lower, respectively. For firms run by younger
CEOs, the mean number of operating segments is 1.91 and the mean Herfindahl Index is 0.80.
This means that older CEOs manage firms with 10.5% more operating segments and a 5.5%
lower Herfindahl Index. One caveat of this test is that I am unable to discern whether it is older
CEOs who diversify the firm’s operating segments or if it is because older CEOs manage firms
that tend to be diversified that is driving the result. Although I am unable to make this
distinction, the results from this table along with the results from Table V are consistent with the
explanation that older CEOs tend to diversify their firms.
In sum, the results of Tables V and VI suggest that older CEOs prefer to diversify the
firm’s asset base. Since diversification is associated with lower firm value, these results further
suggest that the investment behavior of older CEOs creates agency costs between older managers
and shareholders, not only through foregone profitable investments, but through firm
diversification too.
4.2.3 The relation between CEO age, investment behavior, and firm performance
In this section, I analyze whether the investment behavior of older CEOs translates into
reduced shareholder wealth. If the lower investment level of older CEOs truly represents an
22

underinvestment problem, then the performance of firms managed by older CEOs should be
worse than the performance of firms managed by younger CEOs. I capture firm performance by
examining abnormal stock returns, industry-adjusted sales growth, and industry-adjusted growth
in operating income.
Table VII reports the results of risk-adjusted portfolio abnormal stock returns. In Panel A,
I construct a long portfolio that consists of equal weighted returns of all firms that have a
younger CEO, where a younger CEO is defined as a CEO whose age is in the bottom tercile of
CEO ages in a given year. I then construct a short portfolio that consists of equal weighted
returns of all firms that have an older CEO, where an older CEO is defined as a CEO whose age
is in the top tercile of CEO ages in a given year. Each month, I subtract the return on the short
portfolio from the return on the long portfolio. Following Carhart (1997), I examine portfolio
returns in excess of his four-factor model. I regress the differences in returns between the two
portfolios on the CRSP value-weighted market return less the risk-free rate, SMB (small minus
big), HML (high minus low), and MOM (momentum). SMB, HML, and MOM are the returns on
zero-investment factor-mimicking portfolios designed to capture size, book-to-market, and
momentum effects, respectively. Alpha is the abnormal return in excess of passive investment in
the factors. Following this strategy, an investor would have earned an abnormal return of 0.50%
per month or 6.00% a year, suggesting that firms managed by older CEOs significantly
underperform firms run by younger CEOs.
In Panel B, I investigate whether underinvestment is a source of the underperformance in
firms that are managed by older CEOs. I restrict the sample to firms managed by older CEOs and
partition the sample into firms with above median growth opportunities and firms with below
median growth opportunities, as approximated by the firm’s industry market-to-book ratio. For
23

each group of firms, I construct a long portfolio that consists of equal weighted returns of firms
that have a positive investment residual and a short portfolio that consists of equal weighted
returns of firms that have a negative investment residual. I calculate a firm's investment residual
by regressing the firm's investment rate on the firm's market-to-book ratio, logarithm of market
value of assets, cash holdings, market leverage, pre-investment profitability, dividend yield, firm
age, year fixed effects, and Fama and French 12 industries. Firms with a negative residual are
then classified as firms with negative investment residuals, and firms with a positive residual are
classified as firms with positive investment residuals. After applying the same methodology as in
Panel A to measure abnormal returns, I find that this strategy generates an abnormal return of
0.38% per month or 4.52% a year in firms with above median growth opportunities and an
abnormal return that is not significantly different from zero in firms with below median growth
opportunities. However, even though the results are only significantly different from zero in the
sample of high growth firms, the two results are not statistically different from one another at
conventional levels. In sum, the results in Panel B of Table VII are consistent with the
underinvestment by older CEOs being costly for shareholders when a firm has large growth
opportunities.
In Table VIII, I provide further evidence that firms with above median investment
opportunities that are managed by older CEOs underperform compared to firms managed by
younger CEOs. In Models 1-4, I regress the firm’s industry-adjusted growth in sales from period
t to period t+2 on the two proxies for CEO age, where industry adjusted-sales growth is the
firm’s sales growth less the corresponding three-digit SIC industry’s median sales growth in a
given year. In Models 1 and 2, I limit the sample to those firms with above median industry
market-to-book ratios, and in Models 3 and 4, I restrict the sample to those firms with above
24

median industry factor scores. The results indicate that firms managed by older CEOs grow sales
significantly slower than firms managed by younger CEOs. For example, the coefficient
estimates from Model 1 imply that the sales growth of firms run by older CEOs is 6.05
percentage points lower than firms run by younger CEOs. Since the average three-year sales
growth of firms managed by younger CEOs is 22.28%, this means firms managed by older CEOs
grow their sales 27.16% more slowly.
In Models 5-8 of this table, I regress the firm’s industry-adjusted growth in operating
income before depreciation from period t to period t+2 on the two proxies for CEO age, where
industry-adjusted operating income growth is the firm’s operating income growth less the
corresponding three-digit SIC industry’s median operating income growth in a given year. In
Models 5 and 6, I limit the sample to those firms with above median industry market-to-book
ratios, and in Models 7 and 8, I restrict the sample to those firms with above median industry
factor scores. The results indicate that firms managed by older CEOs grow profits significantly
slower than firms managed by younger CEOs. For example, the coefficient estimates from
Model 5 imply that the operating income growth of firms run by older CEOs is 10.46 percentage
points lower than firms run by younger CEOs. Since the average three-year operating income
growth of firms managed by younger CEOs is 36.94%, this means firms managed by older CEOs
grow operating income 28.33% more slowly.
In summary, the results of Tables VII and VIII indicate that differences in investment
behavior between older and younger CEOs in firms with above median investment opportunities
translates into significant underperformance of firms managed by older CEOs compared to firms
managed by younger CEOs. These results imply that the effect that age has on the investment
25

ehavior of CEOs is a potential source of agency costs and a potential source of destruction of
shareholder value.
4.2.4 The relation between underinvestment and equity-based performance sensitive
compensation
The previous results show that in firms with above median growth opportunities older
CEOs invest less than younger CEOs, and this underinvestment is costly to shareholders. In this
section, I test Hypothesis 3 and efficient contracting predictions that predict that firms with older
CEOs provide their CEOs with more equity-based performance sensitive compensation to
mitigate underinvestment problems. However, if older CEOs are awarded less equity-based
performance sensitive compensation, this suggests that the compensation incentives of older
CEOs are not structured in such a way as to align the interests of older CEOs with those of
shareholders.
It is possible that by tying an already risk-averse manager's wealth even more closely to
the firm's performance, it will cause the CEO to become even more risk-averse and reluctant to
engage in risky investments. 10 In Table IX, I examine whether within my sample, awarding older
CEOs more equity-based performance sensitive compensation leads to greater investment levels.
The dependent variable in all four models is the sum of the firm’s R&D expenditures and capital
expenditures divided by the firm’s total assets. To capture any differential effect of awarded
equity-based performance sensitive compensation on investment between older and younger
CEOs, I interact the two proxies for CEO age with the mean-adjusted logarithm of the total delta
from option and restricted stock grants in given year. Since older CEOs only invest less than
10 See Lambert, Larcker, and Verrecchia (1991), Carpenter (2000), and Parrino, Poteshman, and Weisbach (2005)
for a further discussion on why providing CEOs with greater equity-based compensation can make them more rather
than less risk-averse.
26

younger CEOs in firms with above median investment opportunities, in Models 1 and 2, I limit
the sample to those firms with above median industry market-to-book ratios, and in Models 3
and 4, I restrict the sample to those firms with above median industry factor scores.
The results of Table IX imply that when CEOs are awarded more equity-based
performance sensitive compensation, both older and younger CEOs invest more. Further, there
is no statistically significant difference in the effectiveness of awarding equity-based
performance sensitive compensation in reducing underinvestment by older or younger CEOs.
The coefficient estimates from Model 1 imply that by doubling the delta awarded to older CEOs,
older CEOs will increase their level of investment by approximately 0.23 (=0.403 - 0.176)
percentage points. Since an older CEO with an average delta award invests 0.68 percentage
points less than younger CEOs do, a doubling of their delta award reduces the underinvestment
problem by 33.8%. Thus, within my sample, efficient contracting predicts that awarding older
CEOs more equity-based performance sensitive compensation will be a highly effective tool in
mitigating the underinvestment problem.
In the previous sections, I provide evidence that older CEOs underinvest compared to
younger CEOs, and this underinvestment translates into worse firm performance. Therefore,
Hypothesis 3 and efficient contracting predicts that older CEOs should be awarded more equity-
based performance sensitive compensation to align older CEOs’ interests with those of
shareholders and to incentive older CEOs to pursue a more aggressive investment style.
However, if older CEOs are awarded less equity-based performance sensitive compensation than
younger CEOs, this result would be a potential explanation of why older CEOs appear to invest
in a way that does not maximize firm value.
27

In Table X, I test the efficient contracting prediction. The dependent variable in Models
1-4 is the logarithm of one plus the delta of new option and restricted stock grants. I then run a
Tobit regression and regress the dependent variable on the two proxies for CEO age and other
control variables. In Models 1 and 2, I limit the sample to those firms with above median
industry market-to-book ratios, and in Models 3 and 4, I restrict the sample to those firms with
above median industry factor scores. The coefficient estimates represent marginal effects.
The results in Table X show that, inconsistent with efficient contracting predictions, in
firms with above median growth opportunities, older CEOs actually receive less equity-based
performance sensitive compensation than younger CEOs. For example, the coefficient estimates
from Model 1 imply that the delta of equity grants awarded to older CEOs is approximately
13.7% below that of younger CEOs. Thus, the compensation structure of older CEOs does not
appear to be designed to incentivize older CEOs to increase their level of investment. This result
along with the result of Table IX suggests that one potential reason why older CEOs underinvest
is that older CEOs do not receive enough equity-based performance sensitive compensation to
incentivize them to expend more effort and invest more aggressively.
A possible interpretation of the results of Table X is that the reason why older CEOs
receive less equity-based performance sensitive compensation is that they simply receive less
total compensation. However, in untabulated results, I do not find any evidence that older CEOs
receive less total compensation than younger CEOs.
As a further test of whether the compensation structure of older CEOs does not properly
incentivize them to pursue more aggressive investment strategies, I examine the relation between
CEO age and the CEO’s annual cash salary. In Table XI, I regress the CEO’s annual cash salary
on the two proxies for CEO age. In Models 1 and 2, I limit the sample to those firms with above
28

median industry market-to-book ratios, and in Models 3 and 4, I restrict the sample to those firms
with above median industry factor scores. The results show that older CEOs receive more annual
cash salary than younger CEOs do. 11 The coefficient estimates from Model 1 imply that in firms
with above median investment opportunities, an older CEO receives 5.2% more annual cash
salary than do younger CEOs. This finding indicates that older CEOs do not receive less equity-
based compensation because their overall pay level is lower, but instead, the lower level of
equity-based compensation is the result of a substitution of performance-sensitive compensation
for compensation that is not sensitive to performance.
The univariate results in Table II show that older CEOs tend to have longer tenures. Since
a longer tenure is associated with an increase in the manager’s power and influence over the
board, a potential explanation for why older CEOs receive less equity-based performance
sensitive compensation and a greater annual cash salary than younger CEOs do is that older
CEOs may have greater influence over the structure of their compensation due to power over the
board. This would be consistent with managerial power theories that predict that since
performance sensitive compensation ties more of the CEO's wealth to the firm's performance,
undiversified, risk-averse CEOs who have enough power over the board to influence the
structure of their compensation will award themselves less performance sensitive compensation
(e.g., Bebchuk, Fried, and Walker (2002); Bebchuk and Fried (2003); Cheng and Indjejikian
(2009)).
In summary, the results of this section imply that although the underinvestment problem
associated with older CEOs could be mitigated by awarding them more equity-based
performance sensitive compensation, older CEOs actually receive less equity-based performance
11 The results are robust to using salary + bonus as the definition of cash compensation. I continue to find that older
CEOs receive significantly less cash compensation than do younger CEOs.
29

sensitive compensation. Moreover, older CEOs receive more annual cash salary than younger
CEOs do. Together, the results imply that the compensation structure of older CEOs does not
properly incentivize them to pursue more aggressive investment strategies that would be
preferable to shareholders, and that the compensation structure of older CEOs is likely a source
of the underinvestment problem associated with older CEOs.
5. Conclusion
I provide evidence that the age of the CEO significantly affects his investment behavior.
Specifically, I find that older CEOs tend to invest less than younger CEOs, and that this finding
is concentrated in firms with above median investment opportunities. The result suggests that
older CEOs may be passing up investing in valuable projects, and thereby, creating agency costs
through underinvestment.
As further evidence that the investment behavior of older CEOs is associated with the
creation of agency costs, I document that when older CEOs make acquisitions, they are more
likely to make diversifying acquisitions. In addition, I show that older CEOs are more likely to
manage diversified firms.
Next, I compare the performance of firms managed by older CEOs compared to the
performance of firms managed by younger CEOs. Consistent with the notion that the investment
behavior of older CEOs creates agency costs, I find that risk-adjusted stock returns, industry-
adjusted sales growth, and industry-adjusted operating income growth are all lower in firms
managed by older CEOs compared to firms managed by younger CEOs.
Finally, I examine whether the structure of older CEOs’ compensation is designed to
mitigate the underinvestment problem or if it is a potential source of the problem. In contrast to
30

efficient contracting that predicts firms will award older CEOs more equity-based performance
sensitive compensation to mitigate underinvestment, I document that older CEOs actually
receive less equity-based performance sensitive compensation than younger CEOs. Moreover,
older CEOs receive more non-performance sensitive cash compensation. These results suggest
that instead of using compensation incentives to reduce the underinvestment problem, the less
performance sensitive compensation awarded to older CEOs is likely a source of the
underinvestment problem.
Overall, my findings imply that, on average, the investment decisions made by older
CEOs conflict with shareholders’ interests. In addition, my results suggest that the low level of
equity-based performance sensitive compensation provided to older CEOs is likely a potential
source of the underinvestment problem associated with older CEOs.
31

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36

Table I
Descriptive Statistics
Older CEOs are defined as CEOs whose age is in the top tercile of all CEO ages in a given year. Younger CEOs are defined as CEOs whose age is in the bottom two terciles of all
CEO ages in a given year. CEO Age is the age of the CEO. Tenure is the number of years that the CEO has been CEO of the firm. Founder is an indicator variable that equals one if
the CEO was CEO of the firm when the firm first began trading and zero otherwise. Investment Activity is (capital expenditures + R&D expenses) / total assets. Firm Market-to-
Book equals (book value of assets + the market value of equity - the book value of equity - balance sheet deferred taxes) / total assets. Market Value of Assets equals (market
value equity + book value of long-term debt + debt in current liabilities) and is in millions. Cash Holding equals (book value of cash and short-term investments) / total assets.
Market Leverage equals (book value of long-term debt + debt in current liabilities) / (book value of long-term debt + debt in current liabilities + market value of common shares).
Pre-Investment Profits / Book Assets equals (EBITDA + R&D expenses + advertising expenses) / total assets. Dividend Yield is the average dividend yield over the current year
and the previous two years. Firm Age is the number of years that the firm has been publicly trading. Industry Adjusted Sales Growth is the firm's sales growth over the next three
years less the median three-digit SIC industry sales growth. Industry Adjusted Operating Income Growth is the firm's growth in operating income before depreciation over the
next three years less the median three-digit-SIC industry growth rate. Industry Market-to-Book is the median market-to-book ratio of a firm's corresponding industry in a given
year, where an industry is measured at the three-digit SIC level. High Industry MB is an indicator variable that is equal to one if a firm's corresponding Industry MB is above the
median. Industry Factor Score is the median Factor Score of a firm's corresponding industry in a given year, where an industry is measured at the three-digit SIC level. Factor
Score is a common factor extracted from a firm's investment intensity, market-to-book value of assets, R&D expenditures, and growth rate of market value of assets (Baber,
Janakiraman, and Kang (1996). High Industry Factor Score is an indicator variable that is equal to one if a firm's corresponding Industry Factor Score is above the median.
Portfolio Delta is a thousand dollar change in CEO wealth given a 1% change in stock price. New Delta is the sum of the Delta from option awards and the Delta of stock awards.
Total Compensation equals (Salary + Bonus + Other Annual Compensation + Restricted Stock Grants + LTIP Payouts + All Other Compensation + Value of Option Grants) and is
in thousands. Salary is annual CEO salary and is in thousands. Equity Compensation is the sum of the value of option and stock awards and is in thousands.
Observations Mean Std. Dev. P25 Median P75
CEO Age 13769 54.74 6.97 50.00 55.00 59.00
Tenure 13769 6.819 6.377 2.000 5.000 9.000
Founder 13769 0.110 0.312 0.000 0.000 0.000
Investment 13769 10.60 9.46 4.27 7.85 13.62
Firm Market-to-Book 13769 2.059 1.400 1.221 1.609 2.337
Market Value of Assets 13769 9280 27908 750 1904 5997
Cash Holdings 13769 0.146 0.173 0.022 0.073 0.211
Market Leverage 13769 0.198 0.195 0.033 0.148 0.298
Pre-Investment Profits / Book Assets 13769 0.190 0.110 0.121 0.176 0.244
Dividend Yield 13769 0.010 0.014 0.000 0.002 0.016
Firm Age 13769 24.16 19.69 9.00 18.00 33.00
Industry Adjusted 3-Year Sales Growth 12435 0.136 0.658 -0.182 0.000 0.266
Industry Adjusted 3-Year Operating Income Growth 12231 0.253 1.114 -0.209 0.080 0.555
Industry Market-to-Book 13769 2.077 0.808 1.470 1.886 2.476
Industry Factor Score 13769 0.046 0.544 -0.341 -0.146 0.365
Portfolio Delta 13769 599.0 1055.3 100.1 245.8 612.0
New Delta 12760 52.04 85.59 5.25 21.08 59.31
Total Compensation 13769 4989 5959 1471 2902 5926
Salary 13769 773.0 363.3 503.5 715.8 981.1
Equity Compensation 13769 2925 4413 316 1317 3550

Table II
Univariate Results Comparing Firms with Older CEOs to Firms with Younger CEOs
Older CEOs are defined as CEOs whose age is in the top tercile of all CEO ages in a given year. Younger CEOs are defined as CEOs whose age is in the bottom two terciles of all CEO ages in a
given year. CEO Age is the age of the CEO. Tenure is the number of years that the CEO has been CEO of the firm. Founder is an indicator variable that equals one if the CEO was CEO of the firm
when the firm first began trading and zero otherwise. Investment Activity is (capital expenditures + R&D expenses) / total assets. Firm Market-to-Book equals (book value of assets + the market
value of equity - the book value of equity - balance sheet deferred taxes) / total assets. Market Value of Assets equals (market value equity + book value of long-term debt + debt in current
liabilities) and is in millions. Cash Holding equals (book value of cash and short-term investments) / total assets. Market Leverage equals (book value of long-term debt + debt in current liabilities)
/ (book value of long-term debt + debt in current liabilities + market value of common shares). Pre-Investment Profits / Book Assets equals (EBITDA + R&D expenses + advertising expenses) /
total assets. Dividend Yield is the average dividend yield over the current year and the previous two years. Firm Age is the number of years that the firm has been publicly trading. Industry
Adjusted Sales Growth is the firm's sales growth over the next three years less the median three-digit SIC industry sales growth. Industry Adjusted Operating Income Growth is the firm's growth
in operating income before depreciation over the next three years less the median three-digit-SIC industry growth rate. Industry Market-to-Book is the median market-to-book ratio of a firm's
corresponding industry in a given year, where an industry is measured at the three-digit SIC level. High Industry MB is an indicator variable that is equal to one if a firm's corresponding Industry
MB is above the median. Industry Factor Score is the median Factor Score of a firm's corresponding industry in a given year, where an industry is measured at the three-digit SIC level. Factor
Score is a common factor extracted from a firm's investment intensity, market-to-book value of assets, R&D expenditures, and growth rate of market value of assets (Baber, Janakiraman, and Kang
(1996). High Industry Factor Score is an indicator variable that is equal to one if a firm's corresponding Industry Factor Score is above the median. Portfolio Delta is a thousand dollar change in
CEO wealth given a 1% change in stock price. New Delta is the sum of the Delta from option awards and the Delta of stock awards. Total Compensation equals (Salary + Bonus + Other Annual
Compensation + Restricted Stock Grants + LTIP Payouts + All Other Compensation + Value of Option Grants) and is in thousands. Salary is annual CEO salary and is in thousands. Equity
Compensation is the sum of the value of option and stock awards and is in thousands. *, **, *** indicate significance at the 10%, 5%, and 1% levels respectively.
Firms with Younger CEOs Firms with Older CEOs
Observations Mean Observations Mean Difference p-Value
CEO Age 9519 51.27 4250 62.52 -11.25*** 0.000
Tenure 9519 5.559 4250 9.643 -4.084*** 0.000
Founder 9519 0.100 4250 0.131 -0.031*** 0.000
Investment 9519 11.14 4250 9.38 1.76*** 0.000
Firm Market-to-Book 9519 2.120 4250 1.922 0.198*** 0.000
Market Value of Assets 9519 8450 4250 11137 -2686*** 0.000
Cash Holdings 9519 0.158 4250 0.12 0.038*** 0.000
Market Leverage 9519 0.191 4250 0.214 -0.023*** 0.000
Pre-Investment Profits / Book Assets 9519 0.194 4250 0.183 0.011*** 0.000
Dividend Yield 9519 0.009 4250 0.012 -0.003*** 0.000
Firm Age 9519 22.56 4250 27.75 -5.186*** 0.000
Industry Adjusted 3-Year Sales Growth 8576 0.161 3859 0.083 0.078*** 0.000
Industry Adjusted 3-Year Operating Income Growth 8421 0.277 3810 0.201 0.076*** 0.000
High Industry Market-to-Book 9519 0.515 4250 0.442 0.073*** 0.000
High Industry Factor Score 9519 0.511 4250 0.454 0.058*** 0.000
Portfolio Delta 9519 552.8 4250 702.6 -149.8*** 0.000
New Delta 8874 51.87 3886 52.44 -0.568 0.730
Total Compensation 9519 4843 4250 5317 -474.9*** 0.000
Salary 9519 735.6 4250 857.0 -121.4*** 0.000
Equity Compensation 9519 2977 4250 2810 167.1** 0.040
38

Table III
Panel A: The Relation between CEO Age and Investment Activity
The dependent variable is measured in time period t, whereas the independent variables are measured in time period t-1. The dependent variable in Panel
A and Panel B is Investment Activity. Investment Activity is (capital expenditures + R&D expenses) / total assets. In Panel C, the dependent variable is
R&D expenses / total assets. In Panel D, the dependent variable is capital expenditures / total assets. Oldest CEO is an indicator variable that equals one if
the CEO's age is in the top third of CEO ages in a given year and zero otherwise. CEO Age is the age of the CEO. Industry Market-to-Book is the median
market-to-book ratio of a firm's corresponding industry in a given year, where an industry is measured at the three-digit SIC level. High Industry MB is an
indicator variable that is equal to one if a firm's corresponding Industry MB is above the median. Industry Factor Score is the median Factor Score of a
firm's corresponding industry in a given year, where an industry is measured at the three-digit SIC level. Factor Score is a common factor extracted from a
firm's investment intensity, market-to-book value of assets, R&D expenditures, and growth rate of market value of assets (Baber, Janakiraman, and Kang
(1996). High Industry Factor Score is an indicator variable that is equal to one if a firm's corresponding Industry Factor Score is above the median.
Portfolio Delta is a thousand dollar change in CEO wealth given a 1% change in stock price. Tenure is the number of years that the CEO has been CEO of
the firm. Market Value of Assets equals (market value equity + book value of long-term debt + debt in current liabilities) and is in millions. Firm Market-to-
Book equals (book value of assets + the market value of equity - the book value of equity - balance sheet deferred taxes) / total assets. Cash Holding
equals (book value of cash and short-term investments) / total assets. Market Leverage equals (book value of long-term debt + debt in current liabilities) /
(book value of long-term debt + debt in current liabilities + market value of common shares). Pre-Investment Profits / Book Assets equals (EBITDA +
R&D expenses + advertising expenses) / total assets. Dividend Yield is the average dividend yield over the current year and the previous two years.
Founder is an indicator variable that equals one if the CEO was CEO of the firm when the firm first began trading and zero otherwise. Firm Age is the
number of years that the firm has been publicly trading. Industry fixed effects are measured using the Fama and French 12 industry breakdown. Standard
errors are clustered by firm. T-statistics are presented in parentheses. *, **, *** indicate significance at the 10%, 5%, and 1% levels respectively.
Investment Opportunity Proxy
(1) (2) (3) (4) (5) (6)
Older CEO -0.460* -0.086 -0.005
(-1.91) (-0.33) (-0.02)
CEO Age -0.062*** -0.023 -0.013
(-3.01) (-0.97) (-0.57)
Older CEO * High Industry MB -0.807**
(-2.02)
CEO Age * High Industry MB -0.078**
(-2.43)
Older CEO * High Industry Factor Score -0.970**
(-2.34)
CEO Age * High Industry Factor Score -0.095***
(-2.93)
High Industry MB 0.486** 0.471** 0.746*** 0.499**
(2.10) (2.04) (2.73) (2.14)
High Industry Factor Score 1.378*** 1.107***
(4.46) (4.21)
Log 1+Portfolio Delta -0.104 -0.123 -0.106 -0.129 -0.100 -0.121
(-0.73) (-0.87) (-0.75) (-0.91) (-0.71) (-0.86)
Log Tenure 0.265 0.384* 0.266 0.388** 0.257 0.378*
(1.40) (1.95) (1.40) (1.97) (1.36) (1.92)
Log Market Value of Assets -0.412*** -0.385*** -0.410*** -0.383*** -0.420*** -0.396***
(-2.95) (-2.75) (-2.94) (-2.74) (-3.00) (-2.84)
Firm Market-to-Book 1.334*** 1.321*** 1.332*** 1.315*** 1.329*** 1.309***
(7.21) (7.13) (7.20) (7.09) (7.19) (7.07)
Cash Holdings 9.289*** 9.169*** 9.225*** 9.052*** 9.091*** 8.894***
(7.07) (6.99) (7.03) (6.92) (6.94) (6.80)
Market Leverage -0.491 -0.540 -0.499 -0.554 -0.421 -0.477
(-0.65) (-0.72) (-0.66) (-0.73) (-0.56) (-0.64)
Pre-Investment Profits / Book Assets 15.708*** 15.689*** 15.709*** 15.726*** 15.790*** 15.815***
(8.50) (8.49) (8.51) (8.53) (8.57) (8.59)
Dividend Yield -66.194*** -65.254*** -66.513*** -65.944*** -64.944*** -64.441***
(-7.16) (-7.07) (-7.19) (-7.15) (-6.95) (-6.91)
Founder CEO -0.558 -0.524 -0.574 -0.556 -0.572 -0.559
(-1.16) (-1.09) (-1.19) (-1.16) (-1.19) (-1.17)
Firm Age -0.009 -0.007 -0.009 -0.007 -0.009 -0.007
(-1.20) (-0.98) (-1.19) (-0.97) (-1.19) (-0.96)
Intercept 6.145*** 5.716*** 6.031*** 5.763*** 5.987*** 5.711***
(6.30) (5.73) (6.20) (5.79) (6.19) (5.77)
Observations 13769 13769 13769 13769 13769 13769
Adjusted R 2 High Industry Market-to-Book High Industry Factor Score
0.393 0.395 0.394 0.395 0.395 0.397
Industry FE Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes
39

Table III - Continued
Panel B: The Relation between Tenure, CEO Age, and Investment Activity
High Industry Market-to-Book
Investment Opportunity Proxy
High Industry Factor Score
(1) (2) (3) (4)
Older CEO -0.408 -0.415
(-1.10) (-1.11)
CEO Age -0.090*** -0.097***
(-2.97) (-3.25)
Older CEO * Log Tenure -1.392*** -1.377***
(-3.18) (-3.10)
CEO Age * Log Tenure -0.071*** -0.063**
(-2.70) (-2.37)
High Industry MB 0.700** 0.681**
(2.22) (2.19)
High Industry Factor Score 3.672*** 3.692***
(5.60) (5.63)
Log 1+Portfolio Delta -0.094 -0.123 -0.119 -0.139
(-0.39) (-0.52) (-0.51) (-0.60)
Log Tenure 0.552 0.345 0.486 0.295
(1.47) (1.02) (1.34) (0.91)
Log Market Value of Assets -0.228 -0.205 -0.209 -0.194
(-1.11) (-1.01) (-0.99) (-0.92)
Cash Holdings 15.496*** 15.306*** 14.171*** 13.935***
(10.14) (9.95) (10.16) (9.91)
Market Leverage -2.192 -2.177 -2.151* -2.114*
(-1.55) (-1.54) (-1.76) (-1.72)
Pre-Investment Profits / Book Assets 21.117*** 21.107*** 21.911*** 21.885***
(10.11) (10.13) (10.37) (10.37)
Dividend Yield -120.496*** -119.137*** -126.881*** -124.860***
(-6.53) (-6.49) (-6.34) (-6.24)
Founder CEO -0.283 -0.284 -0.344 -0.361
(-0.41) (-0.41) (-0.51) (-0.53)
Firm Age -0.014 -0.010 -0.025** -0.021*
(-1.15) (-0.84) (-2.26) (-1.91)
Intercept 7.186*** 7.103*** 9.370*** 9.075***
(4.90) (4.88) (5.78) (5.61)
Observations 6785 6785 6798 6798
Adjusted R 2 0.373 0.376 0.373 0.376
Industry FE Yes Yes Yes Yes
Year FE Yes Yes Yes Yes
40

Table III - Continued
Panel C: The Relation between CEO Age and R&D Expenditures
Investment Opportunity Proxy
High Industry Market-to-Book High Industry Factor Score
(1) (2) (3) (4) (5) (6)
Older CEO -0.207 0.155 0.203
(-1.06) (1.18) (1.60)
CEO Age -0.030* 0.007 0.013
(-1.71) (0.62) (1.20)
Older CEO * High Industry MB -0.782**
(-2.31)
CEO Age * High Industry MB -0.074***
(-2.74)
Older CEO * High Industry Factor Score -0.881**
(-2.56)
CEO Age * High Industry Factor Score -0.085***
(-3.13)
High Industry MB 0.633*** 0.625*** 0.885*** 0.651***
(4.19) (4.13) (4.64) (4.30)
High Industry Factor Score 1.460*** 1.217***
(6.63) (6.79)
Log 1+Portfolio Delta -0.160 -0.169 -0.161 -0.174 -0.155 -0.166
(-1.41) (-1.48) (-1.43) (-1.53) (-1.38) (-1.47)
Log Tenure 0.046 0.107 0.047 0.111 0.038 0.100
(0.28) (0.61) (0.28) (0.63) (0.23) (0.57)
Log Market Value of Assets -0.376*** -0.362*** -0.374*** -0.361*** -0.384*** -0.374***
(-2.90) (-2.74) (-2.89) (-2.73) (-2.98) (-2.85)
Firm Market-to-Book 1.098*** 1.092*** 1.096*** 1.086*** 1.097*** 1.085***
(4.93) (4.89) (4.92) (4.85) (4.92) (4.84)
Cash Holdings 14.618*** 14.557*** 14.556*** 14.446*** 14.416*** 14.286***
(11.07) (11.01) (11.04) (10.96) (10.97) (10.87)
Market Leverage 2.490*** 2.466*** 2.483*** 2.452*** 2.541*** 2.509***
(4.74) (4.70) (4.73) (4.67) (4.86) (4.80)
Pre-Investment Profits / Book Assets 4.904** 4.892** 4.905** 4.928** 4.999** 5.028**
(2.38) (2.38) (2.38) (2.39) (2.43) (2.44)
Dividend Yield -14.448** -13.965** -14.762** -14.625** -13.083** -12.985**
(-2.48) (-2.41) (-2.53) (-2.51) (-2.27) (-2.26)
Founder CEO -0.869** -0.852** -0.885** -0.883** -0.884** -0.886**
(-2.30) (-2.26) (-2.35) (-2.34) (-2.35) (-2.35)
Firm Age 0.013** 0.014** 0.013** 0.014** 0.013** 0.014***
(2.35) (2.55) (2.37) (2.57) (2.38) (2.59)
Intercept 0.135 -0.075 0.025 -0.031 0.019 -0.053
(0.15) (-0.08) (0.03) (-0.03) (0.02) (-0.06)
Observations 13765 13765 13765 13765 13765 13765
Adjusted R 2 0.412 0.412 0.412 0.413 0.414 0.415
Industry FE Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes
41

Table III - Continued
Panel D: The Relation between CEO Age and Capital Expenditures
High Industry Market-to-Book
Investment Opportunity Proxy
High Industry Factor Score
(1) (2) (3) (4) (5) (6)
Older CEO -0.206 -0.165 -0.119 -0.119
(-1.09) (-0.67) (-0.47) (-0.47)
CEO Age -0.034** -0.022
(-2.01) (-0.97)
Older CEO * High Industry MB -0.087
(-0.29)
CEO Age * High Industry MB -0.024
(-0.87)
Older CEO * High Industry Factor Score -0.180 -0.180
(-0.58) (-0.58)
CEO Age * High Industry Factor Score
High Industry MB -0.156 -0.166 -0.128 -0.157
(-0.75) (-0.79) (-0.54) (-0.75)
High Industry Factor Score -0.015 -0.015
(-0.05) (-0.05)
Log 1+Portfolio Delta 0.125 0.114 0.125 0.112 0.124 0.124
(1.12) (1.03) (1.12) (1.01) (1.12) (1.12)
Log Tenure 0.132 0.207 0.132 0.209 0.131 0.131
(0.93) (1.42) (0.93) (1.43) (0.92) (0.92)
Log Market Value of Assets -0.205* -0.190* -0.205* -0.189* -0.206* -0.206*
(-1.82) (-1.68) (-1.82) (-1.68) (-1.82) (-1.82)
Firm Market-to-Book 0.641*** 0.634*** 0.641*** 0.632*** 0.636*** 0.636***
(5.89) (5.84) (5.89) (5.83) (5.85) (5.85)
Cash Holdings -4.124*** -4.198*** -4.130*** -4.234*** -4.132*** -4.132***
(-6.14) (-6.23) (-6.13) (-6.24) (-6.12) (-6.12)
Market Leverage -2.272*** -2.302*** -2.272*** -2.306*** -2.242*** -2.242***
(-3.69) (-3.74) (-3.69) (-3.75) (-3.68) (-3.68)
Pre-Investment Profits / Book Assets 9.196*** 9.178*** 9.196*** 9.189*** 9.185*** 9.185***
(7.61) (7.57) (7.61) (7.60) (7.62) (7.62)
Dividend Yield -50.810*** -50.209*** -50.845*** -50.423*** -50.886*** -50.886***
(-6.16) (-6.10) (-6.16) (-6.13) (-6.10) (-6.10)
Founder CEO 0.297 0.318 0.295 0.308 0.297 0.297
(0.72) (0.77) (0.71) (0.74) (0.72) (0.72)
Firm Age -0.020*** -0.019*** -0.020*** -0.019*** -0.020*** -0.020***
(-3.35) (-3.20) (-3.35) (-3.20) (-3.34) (-3.34)
Intercept 6.355*** 6.111*** 6.343*** 6.125*** 6.292*** 6.292***
(9.09) (8.63) (9.07) (8.66) (9.07) (9.07)
Observations 13765 13765 13765 13765 13765 13765
Adjusted R 2 0.312 0.313 0.312 0.313 0.312 0.312
Industry FE Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes
42

Table IV
The Relation between Changes in Investment Activity and CEO Turnover
The dependent variable is the change in investment activity around a CEO turnover. Investment Activity is (capital expenditures
+ R&D expenses) / total assets. In Models 1 and 2, the dependent variable is the investment rate in the year after the CEO is hired
minus the investment rate in the year before the CEO was hired. In Models 3 and 4, the dependent variable is the 2-year average
investment rate over the two years after the CEO is hired minus the 2-year average investment rate over the two years before the
CEO was hired. Age Difference is the age of the newly hired CEO less the age of the replaced CEO. Largest Age Difference is an
indicator variable equal to one if Age difference is in the highest tercile. The highest tercile consists of firms where a younger
CEO was replaced by an older CEO. Medium Age Difference is an indicator variable equal to one if Age difference is in the
second tercile. Market-to-Book equals (book value of assets + the market value of equity - the book value of equity - balance
sheet deferred taxes) / total assets. Market Value of Assets equals (market value equity + book value of long-term debt + debt in
current liabilities) and is in millions. Cash Holding equals (book value of cash and short-term investments) / total assets. Market
Leverage equals (book value of long-term debt + debt in current liabilities) / (book value of long-term debt + debt in current
liabilities + market value of common shares). Pre-Investment Profits / Book Assets equals (EBITDA + R&D expenses +
advertising expenses) / total assets. Dividend Yield is the average dividend yield over the current year and the previous two
years. In Models 1 and 2, the change variables are calculated by taking the variable in year after the CEO is hired minus the
variable in the year before the CEO was hired. In Models 3 and 4, the change variables are calculated by taking the 2-year
average of the variable over the two years after the CEO is hired minus the 2-year average of the variable over the two years
before the CEO was hired. Firm Age is the number of years that the firm has been publicly trading in the year of the turnover.
Industry fixed effects are measured using the Fama and French 12 industry breakdown. Standard errors are clustered by firm. Tstatistics
are presented in parentheses. *, **, *** indicate significance at the 10%, 5%, and 1% levels respectively.
Change in Investment Activity
2-Year Average Change in Investment
Activity
(1) (2) (3) (4)
Age Difference -0.049** -0.063***
(-2.20) (-3.13)
Medium Age Difference -0.195 -0.110
(-0.42) (-0.24)
Largest Age Difference -1.319*** -1.494***
(-2.82) (-3.20)
Change in Market-to-Book 0.619*** 0.616*** 0.172 0.170
(4.27) (4.31) (1.08) (1.08)
Change in Log Market Value of Assets -0.825 -0.814 -1.308** -1.275**
(-1.34) (-1.33) (-2.38) (-2.33)
Change in Cash Holdings 3.880 3.897 4.590 4.608
(1.21) (1.22) (1.38) (1.39)
Change in Market Leverage 4.809 4.796 -1.993 -2.085
(1.62) (1.62) (-0.59) (-0.61)
Change in Pre-Investment Profits / Book Assets 11.001** 11.002** 21.459*** 21.389***
(2.07) (2.08) (4.73) (4.72)
Change in Dividend Yield -6.019 -6.248 0.004 0.004*
(-1.19) (-1.23) (1.58) (1.66)
Firm Age 0.010 0.009 0.017** 0.016**
(1.28) (1.16) (2.18) (2.02)
Intercept 0.593 1.662* -1.098 0.127
(0.62) (1.72) (-0.99) (0.12)
Observations 1195 1195 1175 1175
Adjusted R 2 0.250 0.251 0.251 0.252
Industry FE Yes Yes Yes Yes
Year FE Yes Yes Yes Yes
43

Table V
Heckman Two-Step Model of the Propensity to Make Diversifying Acquisitions in Firms with Above
Median Investment Opportunities
The dependent variable in the first step is an indicator variable that is equal to one if the firm makes an acquisition in a given year. First stage step
regresions are reported as Table I in Appendix A. The dependent variable in the second step is an indicator variable equal to one if the acquirer and the
target do not share the same two-digit SIC and zero otherwise. Results from the second step are presented below. Oldest CEO is an indicator variable that
equals one if the CEO's age is in top third of CEO ages in a given year, and equals zero otherwise. CEO Age is the age of the CEO. Industry MB is the
median market-to-book ratio of a firm's corresponding industry in a given year, where an industry is measured at the three-digit SIC level. Industry Factor
Score is the median Factor Score of a firm's corresponding industry in a given year, where an industry is measured at the three-digit SIC level. Factor
Score is a common factor extracted from a firm's investment intensity, market-to-book value of assets, R&D expenditures, and growth rate of market value
of assets (Baber, Janakiraman, and Kang (1996). Tenure is the number of years that the CEO has been CEO of the firm. Market Value of Assets equals
(market value equity + book value of long-term debt + debt in current liabilities). R&D Expense equals R&D expenses scaled by total assets. Market
Leverage equals (book value of long-term debt + debt in current liabilities) / (book value of long-term debt + debt in current liabilities + market value of
common shares). Stock Run Up is the cumulative abnormal return using Carhart's four factor model during the period (-210,-11) prior to the announcement
day. Relative Deal Size is the deal value divided by the acquirers market value of equity on the 11 th trading day prior to the announcement day. Public
Target is an indicator variable equal to one if the target is a public firm and zero otherwise. Private Target is an indicator variable equal to one if the target
is a private firm and zero otherwise. All Cash Deal is an indicator variable equal to one if the deal was purely financed with cash and zero otherwise. Stock
Deal is an indicator variable equal to one if the deal was at least partially financed with stock and zero otherwise. Firm Age is the number of years that the
firm has been publicly trading. The Inverse Mills Ratio is from the first step of the Heckman Two-Step procedure. Industry fixed effects are measured
using the Fama and French 12 industry breakdown. T-statistics are presented in parentheses. *, **, *** indicate significance at the 10%, 5%, and 1%
levels respectively.
Investment Opportunity Proxy
High Industry Market-to-Book High Industry Factor Score
(1) (2) (3) (4)
Older CEO 0.068** 0.053*
(2.28) (1.90)
CEO Age 0.004** 0.005**
(2.27) (2.54)
Industry MB -0.000 0.001
(-0.01) (0.05)
Industry Factor Score 0.090** 0.090**
(2.37) (2.38)
Log Tenure -0.014 -0.016 -0.020 -0.025
(-0.81) (-0.94) (-1.20) (-1.51)
Log Market Value of Assets -0.037*** -0.038*** -0.043*** -0.043***
(-3.27) (-3.31) (-3.99) (-4.00)
R&D Expenses 0.007** 0.007*** 0.005** 0.006**
(2.55) (2.60) (1.97) (2.08)
Market Leverage 0.525*** 0.529*** 0.426*** 0.426***
(4.61) (4.65) (3.75) (3.76)
Stock Run Up -0.068*** -0.066*** -0.089*** -0.087***
(-2.75) (-2.65) (-3.60) (-3.50)
Relative Deal Size -0.075* -0.075* -0.090* -0.090*
(-1.75) (-1.76) (-1.93) (-1.91)
Public Target 0.001 -0.000 0.000 -0.002
(0.04) (-0.01) (0.00) (-0.07)
Private Target 0.034 0.035 0.035 0.036
(1.36) (1.40) (1.41) (1.45)
All Cash Deal 0.001 0.002 0.006 0.007
(0.06) (0.08) (0.26) (0.28)
Stock Deal -0.023 -0.019 -0.030 -0.026
(-0.78) (-0.66) (-1.01) (-0.89)
Firm Age 0.004*** 0.003*** 0.004*** 0.004***
(4.29) (4.16) (4.98) (4.74)
Intercept 1.246*** 1.034*** 1.207*** 0.996***
(4.54) (3.78) (4.67) (3.91)
Inverse Mills Ratio -0.601*** -0.605*** -0.538*** -0.546***
(-4.44) (-4.45) (-4.03) (-4.06)
Observations 7270 7270 7301 7301
Pseudo R 2 0.071 0.070 0.097 0.097
Industry FE Yes Yes Yes Yes
Year FE Yes Yes Yes Yes
44

Table VI
Tobit Regression of the Relation between Firm Diversification and the Age of the CEO in Firms with Above Median Investment Opportunities
In Models 1, 2, 5, and 6 the dependent variable is the number of business segments that the firm operates in. In Models 3, 4, 7, and 8 the dependent variable is a Herfindahl Index constructed from the percentage of
business segment sales in each firm and is defined as the sum of the square of segment sales divided by the square of firm sales. The coefficient estimates represent marginal effects. Oldest CEO is an indicator variable that
equals one if the CEO's age is in the top third of CEO ages in a given year and zero otherwise. CEO Age is the age of the CEO. Industry MB is the median market-to-book ratio of a firm's corresponding industry in a given
year, where an industry is measured at the three-digit SIC level. Industry Factor Score is the median Factor Score of a firm's corresponding industry in a given year, where an industry is measured at the three-digit SIC level.
Factor Score is a common factor extracted from a firm's investment intensity, market-to-book value of assets, R&D expenditures, and growth rate of market value of assets (Baber, Janakiraman, and Kang (1996). Portfolio
Delta is a thousand dollar change in CEO wealth given a 1% change in stock price. Tenure is the number of years that the CEO has been CEO of the firm. Market Value of Assets equals (market value equity + book value of
long-term debt + debt in current liabilities) and is in millions. Cash Holding equals (book value of cash and short-term investments) / total assets. Market Leverage equals (book value of long-term debt + debt in current
liabilities) / (book value of long-term debt + debt in current liabilities + market value of common shares). Pre-Investment Profits / Book Assets equals (EBITDA + R&D expenses + advertising expenses) / total assets.
Dividend Yield is the average dividend yield over the current year and the previous two years. Founder is an indicator variable that equals one if the CEO was CEO of the firm when the firm first began trading and zero
otherwise. Firm Age is the number of years that the firm has been publicly trading. Industry fixed effects are measured using the Fama and French 12 industry breakdown. Standard errors are clustered by firm. T-statistics
are presented in parentheses. *, **, *** indicate significance at the 10%, 5%, and 1% levels respectively.
Firms with Above Median Investment Opportunities Using Industry Market-to-Book Firms with Above Median Investment Opportunities Using Industry Factor Score
Number of Segments HHI of Business Segments Number of Segments HHI of Business Segments
(1) (2) (3) (4) (5) (6) (7) (8)
Older CEO 0.201** -0.044** 0.220** -0.049**
(1.98) (-2.04) (2.17) (-2.27)
CEO Age 0.021** -0.005*** 0.021*** -0.005***
(2.49) (-2.80) (2.63) (-2.82)
Industry MB 0.076 0.078 -0.023 -0.023
(1.02) (1.05) (-1.46) (-1.50)
Industry Factor Score 0.223 0.220 -0.063* -0.063*
(1.20) (1.18) (-1.73) (-1.71)
Log 1+Portfolio Delta 0.074 0.083 -0.016 -0.018 0.074 0.082 -0.015 -0.017
(1.30) (1.44) (-1.34) (-1.51) (1.32) (1.44) (-1.32) (-1.46)
Log Tenure -0.095 -0.130* 0.020 0.029* -0.104 -0.139* 0.017 0.025
(-1.29) (-1.69) (1.34) (1.84) (-1.44) (-1.85) (1.15) (1.63)
Log Market Value of Assets 0.194*** 0.187*** -0.022* -0.021* 0.198*** 0.192*** -0.023** -0.022*
(3.25) (3.13) (-1.89) (-1.74) (3.33) (3.23) (-1.98) (-1.86)
Cash Holdings -2.654*** -2.611*** 0.651*** 0.640*** -2.801*** -2.751*** 0.693*** 0.681***
(-7.74) (-7.59) (9.17) (8.99) (-7.97) (-7.81) (9.68) (9.49)
Market Leverage 0.582* 0.580* -0.124* -0.123* 0.402 0.406 -0.073 -0.074
(1.73) (1.72) (-1.75) (-1.74) (1.23) (1.24) (-1.07) (-1.09)
Pre-Investment Profits / Book Assets -2.044*** -2.040*** 0.444*** 0.442*** -1.947*** -1.939*** 0.411*** 0.409***
(-4.08) (-4.07) (4.30) (4.28) (-3.88) (-3.86) (3.98) (3.96)
Dividend Yield 18.077*** 18.042*** -3.340*** -3.329*** 16.764*** 16.642*** -2.860*** -2.830***
(3.51) (3.51) (-3.32) (-3.32) (3.26) (3.24) (-2.87) (-2.84)
Founder CEO -0.091 -0.099 0.006 0.007 -0.064 -0.070 0.002 0.004
(-0.47) (-0.50) (0.14) (0.19) (-0.33) (-0.37) (0.06) (0.10)
Firm Age 0.026*** 0.025*** -0.005*** -0.005*** 0.028*** 0.027*** -0.006*** -0.005***
(6.64) (6.45) (-6.94) (-6.70) (7.38) (7.21) (-8.18) (-7.95)
Intercept -1.756*** -1.644*** 1.384*** 1.357*** -1.369*** -1.224** 1.288*** 1.255***
(-3.38) (-3.14) (13.38) (13.06) (-2.66) (-2.35) (12.92) (12.45)
Observations 6411 6411 6411 6411 6469 6469 6469 6469
Pseudo R 2 0.102 0.103 0.208 0.210 0.114 0.115 0.235 0.237
Industry FE Yes Yes Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes Yes Yes
45

Table VII
The Relation between CEO Age and Stock Market Performance
In Panel A, I construct a long portfolio that consists of equal weighted returns of all firms that have a younger CEO,
where a younger CEO is defined as a CEO whose age is in the bottom tercile of CEO ages in a given year. I then
construct a short portfolio that consists of equal weighted returns of all firms that have an older CEO, where an older
CEO is defined as a CEO whose age is in the top tercile of CEO ages in a given year. Each month, I subtract the return
on the short portfolio from the return on the long portfolio. Following Carhart (1997), I examine portfolio returns in
excess of the four-factor model. I regress the differences in returns between the two portfolios on the CRSP valueweighted
market return less the risk-free rate, SMB (small minus big), HML (high minus low), and MOM (momentum).
SMB, HML, and MOM are the returns on zero-investment factor-mimicking portfolios designed to capture size, bookto-market,
and momentum effects, respectively. Alpha is the abnormal return in excess of passive investment in the
factors. Factor returns are from Kenneth R. French's website. In Panel B, I apply the same methodology but with
different portfolios. In Panel B, I restrict the sample to firms managed by older CEOs and partition the sample into firms
with above median growth opportunities and firms with below median growth opportunities, as approximated by the
firm’s industry market-to-book. For each group of firms, I construct a long portfolio that consists of equal weighted
returns of firms that have a positive investment residual and a short portfolio that consists of equal weighted returns
of firms that have a negative investment residual. I calculate a firm's investment residual by regressing the firm's
investment in R&D + CAPEX on the firm's Market-to-Book ratio, logarithm of Market Value of Assets, Cash Holdings,
Market Leverage, Pre-Investment Profitability, Dividend Yield, Firm Age, year fixed effects, and Fama and French 12
industries. Firms with a negative residual are then classified as firms with negative investment residuals, and firms with
a positive residual are classified as firms with positive investment residuals. Standard Errors are White Robust
Standard Errors. T-statistics are presented in parentheses. *, **, *** indicate significance at the 10%, 5%, and 1%
levels respectively.
Panel A: Excess stock returns from taking a long position in portfolios consisting of younger CEOs and a short
position in portfolios consisting of older CEOs
Alpha RMRF SMB HML MOM Adj. R 2
Estimate 0.500*** 0.078** 0.226*** -0.334*** -0.048* 0.604 204
t-Statistic (4.69) (2.27) (6.38) (-7.77) (-1.85)
Panel B: Excess stock returns from taking a long position in portfolios consisting of older CEOs who have a
positive investment residual and a short position in portfolios consisting of older CEOs who have a negative
investment residual (underinvest)
Firms with above median growth opportunities
Alpha RMRF SMB HML MOM Adj. R 2
Estimate 0.377* 0.180*** 0.099 -0.225*** -0.035 0.220 204
t-Statistic (1.79) (3.24) (1.33) (-2.93) (-0.55)
Firms with below median growth opportunities
Alpha RMRF SMB HML MOM Adj. R 2
Estimate -0.022 -0.038 0.016 -0.056 -0.034 0.014 204
t-Statistic (-0.15) (-1.05) (0.31) (-1.07) (-0.88)
Obs.
Obs.
Obs.
46

Table VIII
The Relation between CEO Age, Sales Growth, and Income Growth
In Models 1, 2, 3, and 4, the dependent variable is the firm's industry adjusted sales growth over the next three years. In Models 5, 6, 7, and 8, the dependent variable is the firm's industry adjusted growth in operating income before
depreciation over the next three years. Both growth measures are industry adjusted by subtracting the median growth rate of the corresponding three-digit SIC industry. Investment Activity is (capital expenditures + R&D expenses) / total
assets. Oldest CEO is an indicator variable that equals one if the CEO's age is in top third of CEO ages in a given year and zero otherwise. CEO Age is the age of the CEO. Industry Market-to-Book is the median market-to-book ratio of a
firm's corresponding industry in a given year, where an industry is measured at the three-digit SIC level. Industry Factor Score is the median Factor Score of a firm's corresponding industry in a given year, where an industry is measured at
the three-digit SIC level. Factor Score is a common factor extracted from a firm's investment intensity, market-to-book value of assets, R&D expenditures, and growth rate of market value of assets (Baber, Janakiraman, and Kang (1996).
Portfolio Delta is a thousand dollar change in CEO wealth given a 1% change in stock price. Tenure is the number of years that the CEO has been CEO of the firm. Market Value of Assets equals (market value equity + book value of longterm
debt + debt in current liabilities) and is in millions. Firm Market-to-Book equals (book value of assets + the market value of equity - the book value of equity - balance sheet deferred taxes) / total assets. Cash Holding equals (book
value of cash and short-term investments) / total assets. Market Leverage equals (book value of long-term debt + debt in current liabilities) / (book value of long-term debt + debt in current liabilities + market value of common shares).Pre-
Investment Profits / Book Assets equals (EBITDA + R&D expenses + advertising expenses) / total assets. Dividend Yield is the average dividend yield over the current year and the previous two years. Founder is an indicator variable
that equals one if the CEO was CEO of the firm when the firm first began trading and zero otherwise. Firm Age is the number of years that the firm has been publicly trading. Industry fixed effects are measured using the Fama and French 12
industry breakdown. Standard errors are clustered by firm. T-statistics are presented in parentheses. *, **, *** indicate significance at the 10%, 5%, and 1% levels respectively.
Three-Year Growth in Sales
Three-Year Growth in Operating Income
High Industry Market-to-Book High Industry Factor Score High Industry Market-to-Book High Industry Factor Score
(1) (2) (3) (4) (5) (6) (7) (8)
Older CEO -6.052** -4.263 -10.463** -8.143*
(-2.21) (-1.50) (-2.46) (-1.94)
CEO Age -0.535** -0.518** -0.715** -0.648**
(-2.52) (-2.33) (-2.28) (-2.05)
Industry MB 1.855 1.775 -0.831 -0.930
(0.93) (0.89) (-0.24) (-0.27)
Industry Factor Score -1.607 -1.471 -0.630 -0.445
(-0.37) (-0.34) (-0.10) (-0.07)
Investment Activity 1.616*** 1.597*** 1.617*** 1.592*** 0.147 0.129 0.228 0.207
(8.69) (8.57) (8.58) (8.43) (0.63) (0.55) (0.94) (0.86)
Log 1+Portfolio Delta 10.061*** 9.878*** 11.014*** 10.849*** 4.885** 4.676* 6.515** 6.343**
(6.41) (6.30) (6.79) (6.71) (1.98) (1.88) (2.50) (2.42)
Log Tenure -2.468 -1.653 -2.824 -1.782 -0.448 0.231 -0.676 0.154
(-1.25) (-0.81) (-1.43) (-0.87) (-0.17) (0.08) (-0.24) (0.05)
Log Market Value of Assets 1.663 1.816 1.097 1.233 7.258*** 7.425*** 6.099*** 6.241***
(1.24) (1.36) (0.80) (0.90) (3.38) (3.45) (2.69) (2.75)
Cash Holdings 34.626*** 33.864*** 29.612*** 28.550*** -12.399 -13.046 -22.994 -23.984*
(3.53) (3.43) (3.10) (2.97) (-0.86) (-0.90) (-1.59) (-1.66)
Market Leverage -39.345*** -39.174*** -29.080*** -28.995*** -23.352* -23.075* -25.179* -25.063*
(-4.45) (-4.44) (-3.27) (-3.27) (-1.67) (-1.65) (-1.75) (-1.75)
Pre-Investment Profits / Book Assets -116.194*** -115.854*** -112.580*** -112.341*** 26.120 26.767 29.430 29.823
(-7.24) (-7.22) (-6.87) (-6.85) (1.24) (1.28) (1.37) (1.39)
Dividend Yield -327.608*** -327.234*** -334.520** -331.671** -986.040*** -988.550*** -929.344*** -929.212***
(-2.66) (-2.66) (-2.55) (-2.54) (-5.66) (-5.66) (-5.13) (-5.12)
Founder CEO 6.727 6.793 5.998 6.062 7.239 7.294 3.299 3.336
(1.36) (1.37) (1.17) (1.18) (0.97) (0.97) (0.44) (0.44)
Firm Age -0.685*** -0.669*** -0.686*** -0.668*** -0.539*** -0.523*** -0.502*** -0.487***
(-7.81) (-7.61) (-7.86) (-7.63) (-4.49) (-4.34) (-4.20) (-4.06)
Intercept -3.958 22.370 6.967 31.655** -23.291 12.110 -28.984* 2.236
(-0.35) (1.46) (0.59) (1.98) (-1.40) (0.53) (-1.74) (0.10)
Observations 6102 6102 6118 6118 5962 5962 5959 5959
Adjusted R 2 0.181 0.182 0.174 0.175 0.040 0.040 0.040 0.041
Industry FE Yes Yes Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes Yes Yes
47

Table IX
The Relation between the Age of the CEO, Performance Sensitive Compensation, and Investment
Activity in Firms with Above Median Investment Opportunities
The dependent variable is measured in time period t, whereas the independent variables are measured in time period t-1. The dependent variable is Investment
Activity. Investment Activity is (capital expenditures + R&D expenses) / total assets. Oldest CEO is an indicator variable that equals one if the CEO's age is in
the top third of CEO ages in a given year and zero otherwise. CEO Age is the age of the CEO. New Delta is the sum of the Delta from new stock and option
awards and represents a thousand dollar change in CEO compensation given a 1% change in stock price. Industry Market-to-Book is the median market-tobook
ratio of a firm's corresponding industry in a given year, where an industry is measured at the three-digit SIC level. Industry Factor Score is the median
Factor Score of a firm's corresponding industry in a given year, where an industry is measured at the three-digit SIC level. Factor Score is a common factor
extracted from a firm's investment intensity, market-to-book value of assets, R&D expenditures, and growth rate of market value of assets (Baber, Janakiraman,
and Kang (1996). Portfolio Delta is a thousand dollar change in CEO wealth given a 1% change in stock price. Tenure is the number of years that the CEO has
been CEO of the firm. Market Value of Assets equals (market value equity + book value of long-term debt + debt in current liabilities) and is in millions. Firm
Market-to-Book equals (book value of assets + the market value of equity - the book value of equity - balance sheet deferred taxes) / total assets. Cash Holding
equals (book value of cash and short-term investments) / total assets. Market Leverage equals (book value of long-term debt + debt in current liabilities) / (book
value of long-term debt + debt in current liabilities + market value of common shares). Pre-Investment Profits / Book Assets equals (EBITDA + R&D expenses +
advertising expenses) / total assets. Dividend Yield is the average dividend yield over the current year and the previous two years. Founder is an indicator
variable that equals one if the CEO was CEO of the firm when the firm first began trading and zero otherwise. Firm Age is the number of years that the firm has
been publicly trading. Industry fixed effects are measured using the Fama and French 12 industry breakdown. Standard errors are clustered by firm. T-statistics
are presented in parentheses. *, **, *** indicate significance at the 10%, 5%, and 1% levels respectively.
High Industry Market-to-Book
Investment Opportunity Proxy
High Industry Factor Score
(1) (2) (3) (4)
Older CEO -0.680* -0.629
(-1.66) (-1.56)
CEO Age -0.065** -0.070**
(-2.00) (-2.17)
Older CEO * Log 1+New Delta -0.176 -0.077
(-1.07) (-0.46)
CEO Age * Log 1+New Delta -0.016 -0.012
(-1.43) (-1.04)
Log 1+New Delta 0.403*** 0.338*** 0.375*** 0.341***
(3.56) (3.71) (3.33) (3.73)
Industry MB 0.488 0.483
(1.44) (1.44)
Industry Factor Score 3.627*** 3.639***
(5.18) (5.19)
Log Lagged Portfolio Delta -0.546*** -0.556*** -0.571*** -0.583***
(-2.64) (-2.69) (-2.77) (-2.83)
Log Tenure+1 0.460 0.557 0.316 0.444
(1.21) (1.40) (0.87) (1.15)
Log Market value of Assets -0.243 -0.225 -0.231 -0.212
(-1.28) (-1.18) (-1.18) (-1.08)
Cash Holdings 14.829*** 14.703*** 13.244*** 13.088***
(8.55) (8.45) (8.48) (8.34)
Market Leverage -2.358 -2.384 -2.144 -2.156
(-1.42) (-1.43) (-1.48) (-1.48)
Pre-Investment Profits / Book Assets 22.428*** 22.344*** 22.986*** 22.894***
(9.92) (9.91) (10.12) (10.10)
Dividend Yield -126.051*** -125.321*** -128.205*** -127.154***
(-6.20) (-6.20) (-5.98) (-5.95)
Founder CEO -0.908 -0.891 -0.950 -0.936
(-1.24) (-1.22) (-1.34) (-1.32)
Firm Age -0.008 -0.005 -0.021* -0.019
(-0.62) (-0.44) (-1.78) (-1.57)
Intercept 9.359*** 8.943*** 11.358*** 10.815***
(5.13) (4.83) (5.97) (5.59)
Observations 5169 5169 5185 5185
Adjusted R 2 0.383 0.383 0.387 0.388
Industry FE Yes Yes Yes Yes
Year FE Yes Yes Yes Yes
48

Table X
Tobit Regression of the Relation between CEO Age and the Delta of New Equity Awards in Firms
with Above Median Investment Opportunities
The dependent variable is measured in time period t, whereas the independent variables are measured in time period t-1. The dependent variable is the
logarithm of one plus the Delta of new equity awards, where the Delta of new equity awards is the sum of the Delta from option awards and the Delta of
stock awards. Delta represents a thousand dollar change in CEO compensation given a 1% change in stock price. The coefficient estimates represent
marginal effects. Oldest CEO is an indicator variable that equals one if the CEO's age is in the top third of CEO ages in a given year and zero otherwise.
CEO Age is the age of the CEO. Industry Market-to-Book is the median market-to-book ratio of a firm's corresponding industry in a given year, where an
industry is measured at the three-digit SIC level. Industry Factor Score is the median Factor Score of a firm's corresponding industry in a given year,
where an industry is measured at the three-digit SIC level. Factor Score is a common factor extracted from a firm's investment intensity, market-to-book
value of assets, R&D expenditures, and growth rate of market value of assets (Baber, Janakiraman, and Kang (1996). Portfolio Delta is a thousand dollar
change in CEO wealth given a 1% change in stock price. Tenure is the number of years that the CEO has been CEO of the firm. Market Value of Assets
equals (market value equity + book value of long-term debt + debt in current liabilities) and is in millions. Firm Market-to-Book equals (book value of
assets + the market value of equity - the book value of equity - balance sheet deferred taxes) / total assets. Cash Holding equals (book value of cash and
short-term investments) / total assets. Market Leverage equals (book value of long-term debt + debt in current liabilities) / (book value of long-term debt
+ debt in current liabilities + market value of common shares). Pre-Investment Profits / Book Assets equals (EBITDA + R&D expenses + advertising
expenses) / total assets. Dividend Yield is the average dividend yield over the current year and the previous two years. Founder is an indicator variable
that equals one if the CEO was CEO of the firm when the firm first began trading and zero otherwise. Firm Age is the number of years that the firm has
been publicly trading. Industry fixed effects are measured using the Fama and French 12 industry breakdown. Standard errors are clustered by firm. Tstatistics
are presented in parentheses. *, **, *** indicate significance at the 10%, 5%, and 1% levels respectively.
Investment Opportunity Proxy
High Industry Market-to-Book High Industry Factor Score
(1) (2) (3) (4)
Older CEO -0.137*** -0.147***
(-3.24) (-3.77)
CEO Age -0.014*** -0.014***
(-4.69) (-4.87)
Industry MB 0.024 0.022
(0.80) (0.74)
Industry Factor Score -0.019 -0.014
(-0.32) (-0.24)
Investments 0.003* 0.003 0.004** 0.003*
(1.80) (1.56) (2.12) (1.84)
Log 1+Portfolio Delta 0.216*** 0.211*** 0.214*** 0.208***
(8.40) (8.28) (8.60) (8.47)
Log Tenure -0.105*** -0.082*** -0.096*** -0.073***
(-3.76) (-2.86) (-3.55) (-2.63)
Log Market Value of Assets 0.425*** 0.429*** 0.424*** 0.428***
(19.57) (19.91) (20.35) (20.71)
Cash Holdings 0.390*** 0.369*** 0.428*** 0.405***
(3.46) (3.28) (3.88) (3.66)
Market Leverage 0.099 0.100 0.045 0.047
(0.72) (0.74) (0.35) (0.37)
Pre-Investment Profits / Book Assets 0.430*** 0.432*** 0.322** 0.328**
(2.69) (2.71) (2.01) (2.06)
Dividend Yield -5.993** -5.883** -4.709** -4.635**
(-2.48) (-2.42) (-2.15) (-2.11)
Founder CEO 0.043 0.046 0.033 0.036
(0.64) (0.70) (0.49) (0.54)
Firm Age 0.001 0.001 0.002 0.002*
(0.41) (0.67) (1.63) (1.90)
Intercept -4.594*** -4.671*** -4.605*** -4.695***
(-23.42) (-23.66) (-23.43) (-23.77)
Observations 6280 6280 6299 6299
Pseudo R 2 0.196 0.197 0.202 0.203
Industry FE Yes Yes Yes Yes
Year FE Yes Yes Yes Yes
49

Table XI
The Relation between CEO Age and Annual Cash Salary in Firms with Above Median
Investment Opportunities
The dependent variable is measured in time period t, whereas the independent variables are measured in time period t-1. The dependent variable
is the logarithm of one plus the CEO's annual salary. Oldest CEO is an indicator variable that equals one if the CEO's age is in the top third of
CEO ages in a given year and zero otherwise. CEO Age is the age of the CEO. Industry Market-to-Book is the median market-to-book ratio of a
firm's corresponding industry in a given year, where an industry is measured at the three-digit SIC level. Industry Factor Score is the median
Factor Score of a firm's corresponding industry in a given year, where an industry is measured at the three-digit SIC level. Factor Score is a
common factor extracted from a firm's investment intensity, market-to-book value of assets, R&D expenditures, and growth rate of market value
of assets (Baber, Janakiraman, and Kang (1996). Portfolio Delta is a thousand dollar change in CEO wealth given a 1% change in stock price.
Tenure is the number of years that the CEO has been CEO of the firm. Market Value of Assets equals (market value equity + book value of longterm
debt + debt in current liabilities) and is in millions. Firm Market-to-Book equals (book value of assets + the market value of equity - the
book value of equity - balance sheet deferred taxes) / total assets. Cash Holding equals (book value of cash and short-term investments) / total
assets. Market Leverage equals (book value of long-term debt + debt in current liabilities) / (book value of long-term debt + debt in current
liabilities + market value of common shares). Pre-Investment Profits / Book Assets equals (EBITDA + R&D expenses + advertising expenses) /
total assets. Dividend Yield is the average dividend yield over the current year and the previous two years. Founder is an indicator variable that
equals one if the CEO was CEO of the firm when the firm first began trading and zero otherwise. Firm Age is the number of years that the firm
has been publicly trading. Industry fixed effects are measured using the Fama and French 12 industry breakdown. Standard errors are clustered
by firm. T-statistics are presented in parentheses. *, **, *** indicate significance at the 10%, 5%, and 1% levels respectively.
Investment Opportunity Proxy
High Industry Market-to-Book High Industry Factor Score
(1) (2) (3) (4)
Older CEO 0.052*** 0.050***
(2.72) (2.79)
CEO Age 0.006*** 0.005***
(3.75) (3.71)
Industry MB 0.024** 0.025**
(2.31) (2.40)
Industry Factor Score 0.069*** 0.067***
(2.89) (2.81)
Investments -0.004*** -0.004*** -0.004*** -0.004***
(-4.32) (-4.12) (-4.33) (-4.10)
Log 1+Portfolio Delta 0.027*** 0.029*** 0.028** 0.029***
(2.61) (2.84) (2.54) (2.71)
Log Tenure -0.015 -0.025* -0.013 -0.022
(-1.05) (-1.69) (-0.98) (-1.62)
Log Market Value of Assets 0.141*** 0.140*** 0.142*** 0.141***
(11.49) (11.37) (11.28) (11.17)
Cash Holdings -0.292*** -0.284*** -0.334*** -0.324***
(-4.37) (-4.24) (-5.04) (-4.90)
Market Leverage 0.347*** 0.346*** 0.339*** 0.339***
(5.88) (5.86) (5.91) (5.91)
Pre-Investment Profits / Book Assets 0.118 0.118 0.050 0.049
(1.57) (1.57) (0.66) (0.66)
Dividend Yield 0.806 0.769 0.940 0.905
(0.99) (0.95) (1.17) (1.13)
Founder CEO 0.010 0.009 0.005 0.005
(0.30) (0.27) (0.16) (0.15)
Firm Age 0.004*** 0.004*** 0.004*** 0.004***
(6.85) (6.57) (7.32) (7.08)
Intercept 5.251*** 5.282*** 5.349*** 5.385***
(62.69) (62.10) (59.94) (58.77)
Observations 6727 6727 6740 6740
Adjusted R 2 0.505 0.508 0.521 0.524
Industry FE Yes Yes Yes Yes
Year FE Yes Yes Yes Yes
50

Appendix A
Table I
First Step of Heckman Two-Step Model of the Propensity to Make Diversifying Acquisitions in Firms
with Above Median Investment Opportunities
The dependent variable in the first step is an indicator variable that is equal to one if the firm makes an acquisition in a given year. First stage step regresions
are reported here. The dependent variable in the second step is an indicator variable equal to one if the acquirer and the target do not share the same two-digit
SIC and zero otherwise. Results from the second step are presented in Table V. Oldest CEO is an indicator variable that equals one if the CEO's age is in top
third of CEO ages in a given year, and equals zero otherwise. CEO Age is the age of the CEO. Industry MB is the median market-to-book ratio of a firm's
corresponding industry in a given year, where an industry is measured at the three-digit SIC level. Industry Factor Score is the median Factor Score of a firm's
corresponding industry in a given year, where an industry is measured at the three-digit SIC level. Factor Score is a common factor extracted from a firm's
investment intensity, market-to-book value of assets, R&D expenditures, and growth rate of market value of assets (Baber, Janakiraman, and Kang (1996).
Tenure is the number of years that the CEO has been CEO of the firm. Market Value of Assets equals (market value equity + book value of long-term debt +
debt in current liabilities). R&D Expense equals R&D expenses scaled by total assets. Market Leverage equals (book value of long-term debt + debt in current
liabilities) / (book value of long-term debt + debt in current liabilities + market value of common shares). Cash Holding equals (book value of cash and shortterm
investments) / total assets. Pre-Investment Profits / Book Assets equals (EBITDA + R&D expenses + advertising expenses) / total assets. Propert, Plant,
and Equipement is property, plant, and equipement / total assets. Dividend Yield is the average dividend yield over the current year and the previous two
years. Firm Age is the number of years that the firm has been publicly trading. Industry fixed effects are measured using the Fama and French 12 industry
breakdown. T-statistics are presented in parentheses. *, **, *** indicate significance at the 10%, 5%, and 1% levels respectively.
High Industry Market-to-Book
(1) (2) (3) (4)
Older CEO -0.044 -0.029
(-1.15) (-0.79)
CEO Age -0.004 -0.004*
(-1.50) (-1.74)
Log Tenure -0.013 -0.008 -0.015 -0.006
(-0.59) (-0.36) (-0.67) (-0.26)
Log Market Value of Assets 0.058*** 0.058*** 0.056*** 0.056***
Industry MB -0.034 -0.035
(4.78) (4.78) (4.69) (4.68)
(-1.13) (-1.11)
Investment Opportunity Proxy
High Industry Factor Score
Industry Factor Score -0.093* -0.092*
(-1.82) (-1.79)
R&D Expenses -0.015*** -0.015*** -0.017*** -0.017***
(-5.57) (-5.60) (-6.01) (-6.06)
Market Leverage -0.473*** -0.471*** -0.587*** -0.584***
(-3.51) (-3.50) (-4.53) (-4.50)
Cash Holdings -0.399*** -0.406*** -0.344*** -0.356***
(-3.72) (-3.77) (-3.26) (-3.36)
Pre-Investment Profits / Book Assets 0.029 0.029 0.074 0.071
(0.18) (0.18) (0.45) (0.44)
Property, Plant, and Equipement -0.435*** -0.433*** -0.411*** -0.408***
(-6.96) (-6.91) (-6.51) (-6.46)
Dividend Yield -3.278* -3.217* -5.839*** -5.737***
(-1.76) (-1.73) (-3.12) (-3.06)
Firm Age -0.001 -0.001 0.000 0.000
(-0.87) (-0.77) (0.20) (0.35)
Intercept -0.892*** -0.709*** -0.822*** -0.621***
(-4.97) (-3.29) (-4.54) (-2.92)
Observations 7270 7270 7301 7301
Pseudo R 2
0.046 0.046 0.041 0.041
Industry FE Yes Yes Yes Yes
Year FE Yes Yes Yes Yes
51