ceo’s compensation and risk taking in uk firms. · corporate decision in uk firms incorporating...
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CEO’s compensation and risk taking in UK firms.
Yaz Gulnur Muradoglu a Rukaiyat Adebusola Yusuf b Deven Bathia c
Queen Mary, University of London, London UK.
Abstract
Prior empirical evidence on executive compensation and risk taking remained inconclusive. Most of
these researches failed to adequately consider the endogenous relationship between compensation and
risk. Furthermore, most studies focused on the US non-financial firm perhaps because US CEOs have
received the highest level of compensation but lately the trends has shifted to other countries including
the UK as well. I investigate whether higher executive compensation is related to greater riskier
corporate decision in UK firms incorporating an additional measure of compensation referred to as
Total wealth. CEO’s total wealth represents the accumulated equity linked compensation, which had
already been received by CEOs over the years in the firm; this variable has not be considered due to
unavailability of data. It is important to measure total wealth because executives will have greater
incentive to ensure that the value of their accumulated equity earnings (total wealth) over time goes up
and could therefore take more risk. I used panel data to control for unobservable heterogeneity in
contracting environment of firms, and fixed effect two stage least square regressions to deal with
endogeneity. I examine the relationship using six measure of compensation and two instruments (CEO
age and CEO experience). The sample include both financial and non-financial firms over the period
1999 to 2017 analysed separately. Results support my hypothesis of positive and increasing relationship
between all forms of compensation including total wealth and riskier corporate decisions (leverage and
R&D) in both financial and non-financial firm. In addition, the level of pay measured by the proportion
of each forms of compensation in total compensation matters as higher equity and higher salary reduces
risk taking in non-financial firms and financial firms respectively.
Keywords: Corporate decisions, financial firms, Non-financial firms, Book leverage, R&D.
1.0 Introduction
Corporate governance is a channel to a business world full of trust, transparency and
accountability, capable of supporting investment and overall sustainable economic growth.
The world as a global village brought about immense interdependence of businesses, and
finance. Most importantly, the fact that money and business operations continually cross
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borders has necessitated establishing and preserving trust by way of corporate governance,
which is essential for continuity in business relations across borders. The separation of
ownership between owners and controllers of companies has necessitated the delegation of
power to executives to align and take care of various shareholder’s interest. Since most
executives lack ownership, interest in the firms they control, their undiversified human
capital invested in a particular firm may tempt them to try diversifying their risk. They
would normally want to protect their interest even at the detriment of the owners of the
companies consistent with agency theory of Jensen and Meckling (1976). Without
measures put in place to align the interest of these groups, there is the tendency that they
would hold other employments that would make them negligent to their primary employer
in a bid to diversify their risk.
Executive compensation has been one of the greatest incentives in existence to induce chief
executive officers (CEO) to work. However, this pay has generated a lot of debate as it
keeps increasing to the extent that it becomes difficult relating it to firm performance.
Above all, it is difficult to say if CEOs deserve such huge pay. The rise in CEO pay in the
last 30 years has led regulators and stakeholders to seek measure to control this upward
trend. The use of regulations to ensure effective governance with more regulations after
most corporate scandals originated from the US where excessive CEO compensation was
initially noticed. This seems to have continued and extended to the UK. A good example
is the Sarbanes Oxley Act of 2002 in US because of corporate collapse such as Enron and
WorldCom. The Cadbury report of UK, and more recently in 2011 is the ring fencing of
the UK bank rules (Wallace, 2015) because of RBS failure, which now requires separation
between investment banking and every day banking effective 1st January 2019.
Despite various academic research on executive compensation, there is no general agreement
on the reason for enormous and continuous increase in executive compensation as well as
increased corporate failures. The most daunting part is the reoccurrence of corporate failures,
crisis or scandal despite recommendations, policies developed and various amendment to
compensation contracts, board compositions and leadership structure in corporate firms all with
the aim of aligning the interest of executives and stakeholders and overall preserving the
business world both within and across borders.
Royal Bank of Scotland (RBS) crisis of 2008 motivates this research. This paper answers the
question ‘how does executive compensation influence CEO’s risk taking?
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‘Royal Bank of Scotland boss Fred ‘the shred’ Goodwin pocketed 3.5 million pounds in wages
last year. That means he got more than a million pounds, on top of the pounds 2.58 million
received in 2002. His jump in salary is thought to be the result of a massive bonus paid under
the banks incentive plan. Under the terms of the deal, Goodwin is allowed a bonus of up to 200
percent of his basic salary if his banks outperforms all the others of a similar size’ The Express.
February 23 2004.
The above is an excerpt to help explain how huge executive compensation could be. Although
this example occurred in the bank, this research is directed towards both financial and non-
financial firms. Fred is a typical example of how rewards could induce CEOs to take wrong
decisions and still get away with it. Fred successfully earned bonuses that amount to £6 billion
during his tenure in RBS between 2000 to and 2008 before finally leading the bank to its ruin.
I argue that huge compensation paid to CEOs make them more reckless and plays a significant
role in shaping corporate policies. CEO’s incentives through salary, bonuses, stocks and
options increases the likelihood of making policies that could be detrimental to the firm.
Various studies on executive reward of US CEOs have provided evidence of a relationship
existing between CEOs compensation and risk taking by looking at the impact of CEO wealth
incentives on risk taking. However, there is no consensus on the relationship between
compensation and risk taking in firms. For example Guay (1999); Coles et al. (2006); gave
evidence of a positive relationship between pay-risk-sensitivity, and riskier policies
particularly the magnitude of investment in R&D. Armstrong and Vashishta (2012) gave
evidence of a decrease in Pay-performance-sensitivity (delta) with non-systematic risk but the
relationship with systematic risk remains unclear. Since investment and financial policies are
the two key corporate decisions, a firm can make. There is increased likelihood that CEOs will
increase firm risk through these crucial areas. Due to the high level of uncertainty that trails
research and development, it is regarded the most risky and critical to the maintenance of firm’s
competitive edge, therefore CEOs recklessness can manifest through this investment policy.
Furthermore, huge compensation could induce CEOs to alter capital structure by employment
of leverage especially to finance project they believe will improve value due to overconfidence.
Thus increasing firms leverage to the point where the cost of leverage outweighs its benefit.
Controlling for other firm characteristics, higher leverage and R&D are taken as riskier policy
choices. The first measure of riskier corporate decision is leverage, which captures the
riskiness of corporate financing. Higher leverage and R&D lead to increase in the total
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volatility of firm’s earnings, which has grave consequences for the firm’s stock returns. When
firms face negative shock to their normal business circumstances, the impact of such negative
shock on the firm’s net profitability is greater for highly levered firms. Highly levered firms
are considered riskier because it becomes very difficult to overcome sudden downturn, shocks
and increased market risk.
The other measure of riskiness of corporate decision is R&D, R&D expenditures are
investment expenses considered riskier compared to capital expenditures because of greater
uncertainties that trails future gains from R&D (Coles et al. 2006). Furthermore, R&D is
critical to the maintenance of firm’s competitive edge, therefore CEOs recklessness can
manifest through this investment policy.
Despite the great contributions of prior studies, they are incomplete in some ways. For instance,
Jin (2002) evaluated the effect of only pay-performance-sensitivity on systematic and non-
systematic risk but did not consider how they manifest in policy choices. Furthermore, most
prior research focused on the US non-financial firm perhaps because US CEOs have received
the highest level of compensation but lately the trends has shifted to other countries including
the UK as well (Fernandes, Ferreira, Matos and Murphy, 2013). Thus, the focus of these studies
are narrow and they produced mixed evidence on executive compensation incentives.
This study contributes to the available literature in a number of ways. Firstly, it will incorporate
an additional measure of compensation (Total wealth) in evaluating the relationship between
compensation and riskier corporate decisions. CEO’s total wealth represents the accumulated
equity linked compensation, which had already been received by CEOs over the years in the
firm. Boardex defined total wealth as the value of cumulative holdings over time of stocks,
options, and long-term incentive plans (LTIPs) for the individual or appropriate averages.
Previous studies could not examine total wealth due to unavailability of data on accumulated
equity link compensation of CEOs as they are not calculated and reported frequently.
Larcker and Tayan (2012) conducted an analysis by comparing total annual compensation with
total accumulated wealth and stock return volatility. They suggested that accumulated equity
compensation wealth effects could surpass that of year-to-year equity linked compensation.
Furthermore, total wealth differs from total equity linked compensation by the former being
cumulative (accumulated) and the later been yearly. Since Boardex reports total wealth, I take
advantage of the opportunity to evaluate the effect of total wealth on riskier corporate decisions.
It is important to measure total wealth because it reflects accumulated equity holdings over
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time, which increases with the CEOs tenure. Executives will have greater incentive to ensure
that the value of their cumulated equity earnings (total wealth) over time goes up and could
therefore take more risk. Total wealth could provide incentives far above the annual equity
linked compensation. Hence, a positive relationship is expected between equity compensation,
total wealth and riskier corporate decision. This is because increasing risk is beneficial to
managers as they can gain from greater firm risk most especially due to equity compensation.
Secondly, it will provide evidence on how monetary executive rewards contribute to risk
taking. Thirdly, it will provide evidence from both the financial and non-financial firms in UK.
The next section highlight relevant literature with researches on non-financial and financial
firms separately. Section 3 describes the data and methods; section 4 presents the preliminary
findings, while section 5 concludes.
2.0 Literature Review
2.1 Introduction
This section reviews literature on executive compensation with researches in nonfinancial firms
and financial firms discussed separately. I draw conclusions from both strands of researches
and developed hypothesis based on the literature reviewed.
2.2 CEO Compensation in Non-Financial Firms
Jin (2002) examined the relationship between CEO’s incentives and firm’s risk by
decomposing total risk into market risk and firm-specific risk. Since shareholders can
somehow diversify their risk but the CEO, has undiversified risk in the firm. This is
especially due to his role in maintaining non-systematic risk of the firm and their huge
stake in the firms because of incentive based compensation schemes. Different measures
of risk were used to test his predictions on the effect of incentives level on risk, controlling
for firm or industry effect in some of the regressions.
Results suggest that there exists a negative relationship between incentive level and firm
specific risk but for market risk and incentive level, the relationship is inconsistent. He
concluded that the relationship between PPS and total risk is inverse and does not differ
with separation of the total risk into systematic and non-systematic. Overall results suggest
a robust relationship between non-systematic risk but not systematic risk and incentive
level. Incentive level of CEOs looks unaffected by market risk without trading restriction
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but affected by both systematic and non-systematic risk in a situation where there are
restrictions such as short selling.
Among earlier researches on CEO incentives and risk was Coles, Daniel and Naveen,
(2006) who investigate the association between executive compensation schemes,
investment policy, debt policy and firms risk. Given the increase in the use of options in
compensation contracts (Perry and Zenner 2001), which has been perceived to align the
managerial incentives with shareholder’s interest and the greater responsiveness of CEOs
wealth to stock price (pay-performance-sensitivity).
They argued that on one-part, increase in pay-performance-sensitivity could motivate
managers to be hardworking because they share cost and benefits with shareholders; it also
makes them vulnerable to greater risk due to the undiversified nature of their investment in
the firm as regards the nature of their compensation. On the other part, increase in pay-risk-
sensitivity could actually resolve the issues surrounding the probability of managers
rejecting value creating risky project because of increased pay-performance-sensitivity.
Examining divergent results from various studies, they conclude that there is the tendency
for causal relationship from both side for pay performance-sensitivity and pay-risk-
sensitivity. The particular focus of the study was on the effects of pay-risk-sensitivity on
the riskiness of firm investment, and financial policy. They also evaluate the effect on
choice of pay-risk-sensitivity in compensation scheme.
Results suggest that, the degree of responsiveness of stock options in executive
compensation contracts to changes in stock prices induce CEOs to take decisions that are
more drastic. This includes investing in risky projects and adoption of hostile debt policies.
They found a strong positive relationship between divergence in stock price and research
and development projects, leverage and focus of the firms. Results also suggest rise in pay-
performance-sensitivity because of greater volatility in stock returns, which is against the
findings of Jin (2002).
Informed by Aggarwal and Samwick (2003) suggestion that corporate decisions are not
solely that of the CEO in most firms but rather of teams. Chava and Purnanandam, (2010)
investigates the effect of risk taking incentives of both the CEO and CFO on financial
policies of firms. They hypothesize that managers chose very risky policies when they are
motivated to take risk due to firm’s stock and options held and vice versa. Consistent with
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prior studies, they adopted pay risk-sensitivity and Pay-performance-sensitivity as the most
appropriate measure of the extent to which managers are expected to take risk.
Results suggest a strong association between CEO’s risk preference and financial policies
in form of corporate debt and cash holdings. CEOs but not CFOs with huge pay-
performance-sensitivity employ less debt, and keep more cash while those with huge Pay-
risk-sensitivity employed more debt and keep less cash in their capital structure, which is
in line with Coles et al. (2006). CFOs with greater pay-performance-sensitivity employed
lesser short-term debt compared to those with less pay-performance-sensitivity whereas for
CEO, the result seems ambiguous. For earnings management in form of accruals
management CFOs of firms with higher pay-performance-sensitivity are associated with
great accrual related earning management,
They concluded that the attitude of CFOs towards risk have grave consequences for
decisions on corporate debt and accrual. Hence, CEOs attitude towards risk influences
broader decisions of firms while that of the CFO is vital for financial policies adopted by
firms, which supports the agency theorist view that results of a particular project, or
decision is best explainable through the motives of agents directly responsible for it.
Further, into the debate on the degree of CEOs incentive to increase risk, Armstrong and
Vashishtha (2012) investigates the effect of executive stock options on CEOs tendency to
adjust both firm-specific risk and market risk. Just like Jin (2002), they decompose total
risk into systematic and non-systematic. They argue that Pay-risk-sensitivity induce risk
intolerant executives to increase market risk because it can lead to enormous increase in
CEO stock option value compared to such degree of increase in non-systematic risk but for
pay-performance-sensitivity. CEO could hedge risk by taking advantage of trading in the
market portfolio to eliminate undesirable firm specific risk. Hence, executive stock option
might reduce rather than increase the tendency of risk averse CEOs investing in risky
positive net present value project.
They concluded that there exist a significant association between CEO equity portfolio
Pay-risk-sensitivity and the degree of total risk and market risk but not firm-specific risk.
A positive relationship was found between Pay-performance-sensitivity and total risk and
no relationship when total risk was decomposed. The results suggest that executive stock
options might not prompt managers to invest in risky value enhancing projects with more
firm-specific risk compared to market risk, which they could hedge. Hence, they might go
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for value eroding projects with market risk that they could hedge thereby reducing firm
value or increasing market risk.
In furtherance to the ongoing debate generated by constant increase in executive’s pay
which has generated interest in stakeholders and academics. Cao and Wang (2013)
integrate agency problem into search theory specifically to answer the questions: how does
PPS depend on systematic and unsystematic risk? How does the pay-size ratio depend on
these risks? The study focused on risk neutral and effort-averse CEOs due to competition
among firms for CEOs, which affects incentive, based contracts.
Cao and Wang (2013) revealed that despite the increase in CEO pay partly due to increase
in firm value, incentive pay as the major component of the pay increased at a greater rate
than the increase noticeable in firm value. These led the authors to the conclusion that the
clue to understanding rise in CEO pay lies in understanding the factors that drive pay-
performance-sensitivity (PPS). CEO mobility and the composition of risk faced by a firm
are argued to be of significance to PPS. The findings predict optimal PPS as less than one
despite the CEO neutrality to risk, equilibrium PPS was positively related to firm’s specific
risks and negatively associated to systematic risk.
2.3 CEO Compensation in Financial Firms
Due to 2008-2009 financial crisis that warranted the US government bailout of banks, using
tax payer’s money, DeYoung, Peng and Yan (2013) examined the relationship between
CEOs incentive to take risk and executive compensation contracts prior to 2000s. They
also sought to ascertain if commercial banks take any measures to reduce executives risk
taking incentives in compensation schemes up to 2006. They evaluated the responsiveness
of wealth incentives of CEOs in US large commercial banks to business policy as well as
the level of risks between the periods 1995 to 2006.
Results suggests that a significant relationship exist between business policy as well as risk
taking in banks, and incentives in CEO compensation packages. The higher the pay-risk-
sensitivity of the bank, the greater the amount of market and firm specific risk. In addition,
such banks had huge investments in non-traditional banking activities such as private
mortgages, compared to usual lending. The relationship is more pronounced in the periods
after 2000, which was after the Gramm-Leach-Bliley Act (GBLA) of 1999. Furthermore,
results also suggests that CEOs in US commercial banks are now more motivated to take
risk due to enormous amount of stock options in their compensation schemes.
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Hagendorff and Vallascas (2011) evaluate the risk taking decisions of CEOs in the US
banking industry because of incentives included in the compensation contracts. This was
motivated by the views that these incentives induced CEOs into embracing riskier projects.
They focused on mergers and acquisitions decisions of CEOs after deregulation in the
banking industry believed to have opened more avenues for bank to take unnecessary risk.
GBLA granted commercial banks in US the right to invest in projects outside normal
banking activities. This act has cleared the way for CEOs to engage in various mergers and
acquisition as well as the use of more equity as part of the component of CEO compensation
packages.
They concluded that compensation contracts motivate CEOs of the big banks to explore
investment opportunities that arise after the GBLA came into effect, more especially with
increased pay-risk-sensitivity. This shows that such CEOs are motivated by shareholders
to undertake riskier investments at the detriment of regulators and other stakeholders.
Furthermore, risky investment through acquisitions are linked with greater pay-risk
sensitivity. Results also support the conclusion of the study by DeYoung et al. (2013) who
concluded that the risk taking incentives of CEO with stock options as part of their
compensation contract has increased in the financial industry compared to the other
industries.
Fahlenbrach and Stulz (2011) evaluated the association between CEO incentives prior to
the crisis and performance of banks during the crisis. They argued that CEOs whose
incentives are significantly aligned to shareholder’s interests have tendencies of taking risk
in a different and cautious manner compared to those with less aligned incentive. Giving
attention to losses incurred by CEOs through reduction in value of stock owned in the
company during the crisis, they investigated the likelihood that CEOs have anticipated the
crisis. Therefore, take measures to hedge the risk that might arise during the crisis by
selling their share prior to the crisis.
They concluded that CEOs whose interest are aligned to compensation contracts showed
deteriorating performance and there is no evidence that performance at such banks is better
when compared to those whose CEOs interest differs from the shareholders. Furthermore,
banks where CEOs compensation include huge option and cash bonus seem not to face
deteriorating performance. Overall CEOs also incurred enormous loss in the light of the
crisis as they did not see the crisis coming and did not sell their shares before or during the
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crisis. Therefore, they are not be blame for the crisis, which resulted due to reasons out of
their control but majorly of risk associated to investment strategies. Hence, the conclusion
that CEOs are not to blame for poor performance in banks during the crisis and such blame
goes to unanticipated risk.
Bhagat and Bolton (2014) evaluated the compensation contracts of CEOs in biggest 14 US
banks (classified as Too-Big-to-Fail) with 37 others who had nothing to do with Trouble
Asset Relief Program (TARP) of the States treasury. They focused on salary and bonus of
CEOs, the nature of sales of bank stock and vice versa, and losses suffered by CEO during
the financial crisis of 2008 with substantial decline in share prices. They argue against the
results of Fahlenbrach and Stulz (2011) which they termed as ‘Unforeseen Risk
Hypothesis’. This hypothesis blames the unexpected risk associated` to banks investment
and trade strategy for poor performance and not the CEOs who took decisions aimed at
maximising shareholder’s wealth. They concluded that incentives embedded in executive
compensation contracts is positively related to greater portion of unnecessary risk taken by
banks consistent with managerial incentive hypothesis. CEOs of the largest banks in the
study sold great portion of stocks. Results are in contrast to Fahlenbrach and Stulz (2011)
who attributed poor performance of banks during the crisis to sudden or unanticipated risk.
Gande and Kalpathy (2017) examined the relationship between CEO equity incentive prior
to 2008 financial crisis and large US financial firm risk taking. They evaluated the impact
of equity incentives embedded in CEO pay schemes on performance of banks. Bank
performance was measured particularly in respect of the quantum of US government
Federal Reserve loan received by the banks within the crisis period. They argued that bank
performance during the crisis depends on the likelihood of its surviving the crisis, which
also depends on the degree of financial help received from the government.
They concluded that there exists a positive relationship between an increase in CEO risk-
taking incentive before the crisis and the degree of financial assistance rendered to firms
during the crisis. Overall, they concluded that equity incentives as part of the compensation
scheme are positively related to level of risk undertaken by banks. This increases the
tendency for solvency problems in banks. In addition, results suggest that where incentives
are adequately aligned, the solvency problems were reduced although the evidence is
insignificant.
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Bliss and Rosen (2001) investigated the link between mergers and CEO compensation in
US banks. They examined the effects of both firm size and stock price performance on
executive compensation. They argue that if boards are aware that executives’ acquisition
decisions are solely for self-benefits, they may decide to reward for growth attributable to
other factors aside mergers. In addition, the tendency of making acquisition due to the
amount of cash or stock based compensation of executives was evaluated.
They concluded that there is a link between bank size and compensation, CEOs
compensation increased in response to any form of growth be it merger or non-merger
growth in banks. Results suggest that, enormous stock based compensation decreases the
likelihood of bank executives to engage in acquisitions, which is in line with the view that
CEOs make less value reducing mergers where stocks are greater part of their
compensation packages. Overall, results suggest that most mergers led to increase in the
compensation of executives at the detriment of shareholders. Hence, they concluded that
mergers are an effortless way to increase executive compensations.
Extending the work of Bliss and Rosen (2001), Minnick, Unal, and Yang (2011), evaluated
the relationship between managerial incentives and decisions of banks to acquire other
banks. They examined the effect of CEOs pay-for-performance sensitivity on the choice of
bank holding companies to make acquisitions. They used both multivariate and univariate
regressions to test their hypothesis and a multinomial logit model to ascertain the effect of
PPS on bank’s profitability by examining the impact of PPS on the likelihood of making
acquisitions.
They concluded that acquisition decisions that will enhance shareholder’s wealth are made
where CEO’s compensation includes a considerable amount of banks stocks. Results also
suggests that acquirer’s banks with higher PPS prior to acquisition continue to enjoy
increases returns on assets, equity efficiency, and stock for at least 3 years post acquisition.
Results suggests that some acquisitions provide positive price effect. Overall, results
suggest that managerial incentives discourage value-destroying acquisitions and
encourages value-enhancing acquisitions, hence beneficial to other stakeholders in addition
to shareholders.
Harford and Li (2007) investigated the likelihood of executives to engage in more mergers
because of compensation schemes after prior mergers. Specifically, they evaluate how
CEO’s pay in acquiring firms differs and if the sensitivity of the CEO wealth and
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compensation differs after acquisition. They sought to find out if the decisions of CEOs to
acquire other firms gives the boards more information to make decision on retaining or
firing CEOs. They concluded that executives are always better off in mergers even in
situations where such mergers led to deterioration in shareholder’s wealth.
Results suggests that CEOs pay and wealth are unaffected by decline in stock performance
after acquisition but increases with improvement in stock price after acquisitions. For firms
with stronger boards, their CEOs compensation remains responsive to deterioration in
performance because of mergers. In addition, CEOs are rewarded for mergers but not for
other significant capital expenditures. This implies differential treatment, by the board and
CEO for internal and external investments, hence motives behind both differs. Consistent
with Bliss and Rosen (2001), they are of the opinion that acquisitions are a natural avenue
for upward review of CEOs compensation but it is not the same with other large capital
expenditures.
Stock and options as a component of CEO’s total pay are measures taking to ameliorate
agency conflict between managers and shareholders by aligning the interest of both parties.
Stocks granted to CEOs make their overall wealth (pay) responsive to changes in stock
price. Although this means both shareholders and managers would be sharing gains and
losses. It could also make managers more vulnerable to risk due to undiversified firm
specific wealth of managers compared to diversified shareholders. One can infer from
available literature that equity based compensation can reduce or increase incentives to take
risk, which depends on if the right amounts of equity-linked compensation, are included in
the compensation contract. Because CEOs wealth are tied directly to share price through
equity-linked compensation, they are encouraged to take more risk. I expect a positive
relationship between equity compensation and riskier corporate decisions. Taking more
risk could be beneficial as it increases the likelihood of more returns to the CEOs similar
to shareholders. However, the inclusion of too much equity in compensation contract could
be detrimental to achieving its purpose of aligning the interest of owners and managers.
This is because it remains unclear if equity incentives are able to achieve its aim (Bebchuck
and Fried, 2004).
Furthermore, Guay (1999) argued that compensation that is more direct gives managers the
opportunity to diversify their wealth through investment outside the firm, which increase
the likelihood of executives favouring riskier corporate policies. Bhagat and Bolton (2014)
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also linked salary and bonuses to excessive risk taking in banks. Hence, a positive
relationship is expected between direct compensation and riskier corporate decisions.
Finally, every component of compensation individually and as a whole have greater
likelihood of motivating CEOs towards riskier decisions. The issue with what proportion
of equity linked or direct compensation should be included in executive compensation
contract remains unresolved. In addition, the optimal point to stop increasing the equity or
cash portion of compensation remains an empirical question.
Consistent with literature, I hypothesize thus
1. CEOs with huge compensation are likely to engage in riskier corporate decisions
e.g. leverage and R&D.
2. The higher the total wealth of CEO in a firm the greater the degree of riskier
corporate decisions they engage in.
3.0 Data and Methods
The research focuses on all listed companies in United Kingdom (UK) from the year 1999 to
2017 because Boardex reports CEO compensation data for UK firms starting from 1999.
Executive compensation data was retrieved from Boardex by downloading entire compensation
data for UK firms for each year in excel. Excel data filter was used to sort data; all yearly data
individually sorted were combined into a worksheet sorted by company and year to arrive at
the unbalanced CEO panel data. I then excluded compensation data of Deputy CEO/chief
executive, regional executives and division CEO/chief executive as corporate policies are only
adopted with the consent of CEO. Furthermore, this research is directed towards the major
decision marker of the companies and the excluded executives control a portion of the
organisation be it divisions or region. In the data, chief executive officer refer to CEO, Group
CEO, Chief executive, Group chief executive, and interim CEO. Annual financial data from
Compustat global fundamentals was obtained through Wharton research data service (WRDS)
on 28th of February 2018. I extracted the International Securities Identification Number (ISIN)
of firms from CEO compensation data to construct code list in text file format. I downloaded
annual financial data for all firms with compensation data from 1999 to 2017. It is essential for
a firm to have executive compensation data to be included in the study. The data was edited
using the CEO compensation data so that compensation data matches the financial data. The
initial sample included all listed firms in UK during 1999 to 2017; the final data include 1,861
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companies with 14, 763 firm year observations. The final dataset include 236, 208
observations.
3.1.0 Main Study Variables
Riskier corporate decision as dependent variable is measured as book leverage and R&D. R&D
is set to zero when R&D is reported missing in Compustat. The choice of explanatory variables
is influenced by literature with CEOs total wealth as an additional measure, which captures the
wealth of CEOs over time in form of stocks and options. There is the tendency that this measure
explains risk taking better than the year-to-year equity component of compensation. The
accumulated wealth could give CEOs more incentive to take riskier corporate decisions to
increase the value of their shares, stocks and options. They include salary, bonus, equity
compensation, direct compensation, total compensation, total wealth, salary-percent equity-
percent, and bonus-percent. The natural logarithm of independent variables was used in my
analysis except the percentages. The percentages capture the level of the components of
compensation. All variables are defined in the appendix.
3.1.1 Control Variables.
The choice of control variables was influenced by Cole et al. (2006); Armstrong and
Vashishtha (2012). Control variables include Firm size, ROA, and CEO turnover. CEO
turnover will account for the effect of changes in who occupies the position of CEO in the
firm’s decisions. These variables have been reported to influence compensation as well as
corporate decisions. For instance, firm size and growth opportunities were found by studies
such as Bliss and Rosen (2001), Core et al. (1999) to influence compensation. Therefore, bigger
firms who have more growth opportunities are likely to employ highly skilled executives and
pay more as compensation. In addition, since performance has also been found to influence
executive compensation (Conyon and He, 2011), ROA as measure of performance is included
as control for the influence of performance on compensation. Furthermore, studies on capital
structure such as Rajan and Zingales (1995) suggest that firm size, ROA and collateral
availability, influence leverage.
3.1.2 Instrumental Variables
The instrumental variables for the study includes CEO experience, and CEO’s age. They have
been used as valid instrument by previous research (Palia, 2001); (Gande and Kalpathy, 2017)
and (Cen, and Doukas, 2017) and are expected to have influence on riskiness of the firm
indirectly through their effect on compensation incentives. According to Murphy (1985), the
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ability of a manager is usually unknown during his early years. Thus, firm performance is used
to appraise manager’s ability, which affects pay-performance-sensitivity. Later into the tenure
of CEOs, changes in performance are merely due to variations in output since estimation of
managerial ability are more accurate. It is normal for CEOs pay to differ based on their
experience. Murphy concluded that increase in CEO’s compensation is very sensitive to stock
market returns in CEOs earlier years. If CEOs are more experienced, it could trigger more pay
for them. CEOs experience is measured as the number of years a CEO has spent in a firm as
CEO. According to Palia (2001), Gibbsons and Murphy (1992) suggest that CEO pay-
performance-sensitivity should increase with CEO’s age due to fewer incentives provided by
career concerns as CEO ages and moves towards retirement. Hence, CEOs require more
motivation by way of compensation as they age. CEO’s age is measured as age in years.
However, there is no evidence of a direct relationship between CEOs experience, CEO age,
and riskier corporate decisions. The natural logarithm transformation of instrument are used in
the regressions. The validity of instruments was tested using, Hansen (1982) test of over-
identifying restrictions, under identification test, and weak identification test. A J-statistics
significantly different from zero is an indication that at least one of the assumptions of the test
is violated. For the forms of compensation, both instruments are used expect in the case of
bonus instrumented with CEO experience in both groups and equity where CEO experience
was used in financial firms and CEO age was used in non-financial firms. For the level of
compensation CEOs experience was used as instrument expect for financial firms where CEO
age was used as instrument for equity percent. The decision of the most appropriate instrument
for each regression was made after post estimation on the instruments together and
individually.
3.2 Methods
3.2.1 Model Specification
The research model for the first chapter is as follows:
Riskier corporate decision it = α + β1compensation it -1+ β2controlsit + €it (1)
In the above equations, t stands for time, i represent each firm, riskier corporate decision
represents leverage, or R&D. Compensation represents various component of compensation
(cash, bonus, direct compensation, total compensation, equity, total wealth, salary-percent,
16
bonus-percent, and equity-percent) as measures of CEO incentives. This equation is to
ascertain the relationship between compensation and riskier corporate decisions.
3.2.2 Two-stage Least Square and Justification
Two-stage least square (2SLS) regression was performed using STATA to test the study’s
hypothesis consistent with recent studies (Armstrong and Vashishtha, 2012; Cen and Doukas,
2017; Gande and Kalpathy, 2017). Previous studies such as Cole et al. (2006), attempted to
control for the potential endogeneity issues between compensation and risk by using more
control variables in their ordinary least square regression (OLS). However, OLS is incapable
of providing parameter estimates that are consistent where there are omitted variables or
measurement errors in independent variables.
Although compensation influences riskier corporate decision, there is the probability that
causality runs from both directions. Hence, there could be a bidirectional relationship between
compensation and riskier corporate decisions as suggested by Coles et al. (2006) and DeYoung
et al. (2013). Since the board of directors are likely to pre-empt and integrate the effect of
compensation on managerial decisions into the compensation scheme. The endogenous
relationship between compensation and riskier corporate policies cannot be ignored. Hence,
this research uses fixed effect two stage least square (FE-2SLS), which is a widespread
complete solution to endogenous regressor. FE-2SLS controls for any firm level heterogeneity.
Furthermore, according to Semykina and Wooldridge, (2010), 2SLS accounts for endogeneity
as well as correlated unobserved heterogeneity. Thus, FE-2SLS will help alleviate bias because
both dependent and independent variables are likely to be jointly determined hence
endogenous.
4.0 Empirical analysis
4.1 Correlation between Variables of Interest
Tables 1 explore correlation between various components of executive compensation and firm
variables. This shows that the forms of compensation including the total wealth are positively
associated with each other. The correlation between the forms of compensation is high between
salary and both direct compensation and total compensation. This could be due to that fact that
both direct and total compensation include salary. There is a negative correlation between
compensation and R&D but positive correlation between leverage and compensation. The
17
correlation between the various forms of compensation is not an issue for concern since no
equation is using them as independent variables at a time.
**********[Insert Table 1]**********
4.2 .0 Univariate Analysis
4.2.1 Compensation Structure in UK Financial and Non-financial Firms
Tables 2 provide descriptive statistic of the variables for my analysis. All variables expect CEO
age, CEO experience, and CEO turnover are winsorized in the 1st and 99th Percentile
consistent with Coles et al. (2006), Armstrong and Vashishtha, (2012) to help reduce the effect
of spurious outliers. Winsorization is done by replacing all values in the 1st and 99th percentile
with the next large value outside these percentiles. The mean and median total compensation
in financial firms stood at £1,395,000 and £549,000 with mean and median total wealth
£15,783,000 and £2,521,000 respectively. The non-financial firms have mean and median total
compensation of £892,000 and £390,000 respectively with total wealth of £6,123,000 and
£1,299,000. This suggest that CEO compensation is higher in financial firms compared to Non-
financial firm. Both mean and median salary looks similar in both Financial (£320,000)
(£250,000), non-financial firms (£288,000) (£226,000).
However, equity and bonus is higher in financial firms compared to non-financial firms. This
suggest that financial firms compensate CEOs with more bonus and equity. Furthermore, there
are some firms without breakdown of total direct compensation i.e. there was no salary and
bonus reported but just the total direct compensation. The financial group have 216 firms while
non-financial group have 1,645 firms with 1,710 observation and 13,053 firm year observations
respectively. A comparison of the value of annual equity compensation and total wealth
indicates that the longer the CEO’s tenure in a firm, the more stake they acquire in the firm if
more equity-linked compensation are granted to them. The highest total wealth recorded is over
80 times more than the maximum equity linked compensation in financial firms and over 20
times in non-financial firms. Total wealth is significantly high with maximum value of
£480,220,000 (financial) and £119,719,000 (non-financial) after winsorization. This is not
surprising as we have CEOs that have served as CEO for 33 years (Financial) and 43 years
(non-financial). Mean R&D in nonfinancial firms (£32,000) is higher compare to financial
firms (£2,000), suggesting that R&D is less important to financial firms. Mean ROA is negative
in both groups because some firms reported net loss during the sample period.
18
The salary-percent, equity-percent, and bonus-percent captures the percentage of equity, salary
and bonus that makes the total compensation and is a measure of the level of each forms of
annual compensation. This ratio is affected by lack of breakdown of direct compensation by
some firms in my sample. On average executives in UK financial firms, receive 52.9% of their
total compensation in salary, 20% in bonus and 27% in equity. For non-financial firms, salary,
bonus and equity represent 56%, 14%, and 26% of executive’s total compensation respectively.
This suggest that on average bonus represents the smallest portion of total compensation in
UK. To give an idea of annual compensation for each year of study, average annual
compensation for financial and nonfinancial firms are presented in figure 2 in the appendix.
**********[Insert Table 2]**********
4.3.0 Multivariate Analysis
4.3.1 Impact of Compensation on the Riskiness of Corporate Decisions
Table 3, present results from first stage regressions to obtain predicted values of compensation
used in second stage regressions. Table 3 shows a significant relationship between most
compensation measures, CEO’s age (negative) and CEO experience (positive) across groups.
This shows that the instruments are important determinants of compensation, hence suggest
the instruments are valid. ROA, Firm size and CEO turnover are positively related to
compensation except total wealth and salary-percent in both groups, and salary in non-financial
firms with most relationships significant at 1% level. Results shows that bigger firms pay more
compensation. Furthermore, compensation increases with CEO’s experience as well as
performance. First stage regressions is similar for all, therefore just one table is reported, the
difference lies in the J-statistics which is not reported for other regressions with different
dependent variables. However, the result is similar. From table 4 and 5 with leverage and R&D
as dependent variable, the coefficients on compensation measures are significantly positive at
1% in non-financial and financial firms for all forms of compensation. For measures of level
of compensation in financial firms, the level of salary decreases leverage and R&D but the
level of bonus and equity increases leverage although not statistically significant for the level
of equity. The level of salary and bonus but not equity increases leverage significantly in non-
financial firms with p-value=0.00.
The coefficients and t-statistics (0.604, 3.67), (0.155, 2.92), (0.518, 3.65) , (0.312, 1.90),
(0.576, 3.67), (0.168, 3.16), (-7.180, -1.75), (7.247, 2.15) (4.85, 0.91) on salary, bonus, direct
19
compensation, equity, total compensation, total wealth, bonus-percent and equity-percent,
respectively in financial firms confirms my prediction of positive relationship between
compensation and book leverage. It suggest that the level of salary with coefficient (-7.180)
could reduce leverage in financial firms but the level of equity increases leverage above the
level of bonus. This means that the larger the proportion of equity in total compensation, the
greater the level of leverage which could be mitigated by increasing salary perhaps above
equity in financial firms. The coefficients and t-statistics (0.039, 4.37), (0.025, 3.36), (0.037,
4.38), (0.018, 1.93), (0.055, 4.36) (0.016, 3.87), (0.732, 2.82), (1.67, 2.44) on salary, bonus,
direct compensation, equity, total compensation, total wealth, salary-percent, and bonus-
percent respectively in non-financial firms also confirms the positive relationship between
compensation and book leverage.
However, the level of equity granted in non-financial firms reduces leverage with coefficient
(-0.516). This suggest that for non-financial firms equity portion of total compensation could
help mitigate riskier corporate decisions. The coefficient (0.016) on total wealth is significant
with p= 0.000 in non-financial firms but higher for financial firms (0.168) which suggests that
total wealth matters for CEOs in non-financial firms and increases the tendency of employing
more leverage. This suggests that despite the portion of equity likely to reduce risk taking, the
inclusion of equity to the point where CEOs of non-financial firms have great-accumulated
equity (total wealth) could increase leverage. This could explain why total wealth is lower in
non-financial firms compared to financial firms.
Although bonus paid in financial firms is higher than non-financial, bonus in form and level
increases leverage times two in non-financial firms with coefficients and t-statistics (0.025,
3.36) compared to (0.010, 1.60) in financial firms. Similarly, bonus also increased R&D more
in non-financial firms with coefficient and t-statistics (0.006, 3.80) compared to financial firms
(0.001, 2.51). This suggests that bonus increases riskier corporate decisions in non-financial
firms no matter how little the level of bonus paid.
From table 5 with R&D as dependent variable, the coefficients on all forms of compensation
are positive and significant in both financial and financial firms expect on equity in financial
firms. For the level of compensation, the coefficient on bonus percent is positive and significant
in both financial and non-financial firms which implies that higher bonus encourages CEOs to
take riskier decisions by increasing the intensity of R&D investment. Although the coefficients
on equity percent (-0.014, -0.109) suggests that higher equity reduces risk taking in both
20
groups, with that on salary percent (-0.061) suggesting that higher salary in financial firms
reduces riskier corporate decision, results are not significant. However, the coefficients on
compensation in R&D regressions are quite small compared to that of Book leverage. This
suggest that CEOs risk taking due to compensation incentives manifest more in Leverage than
R&D.
In summary, results shows a positive relationship between compensation and riskier decision.
This gives evidence suggesting that all forms of compensation gives CEOs incentives to take
more risk, which increases with higher compensation. Furthermore, results suggest that
increasing the equity portion of total compensation reduces riskier corporate decisions in non-
financial firms while increasing the salary portion of total compensation in financial firms
reduces risk taking. Overall, results are consistent with Coles et al. (2006), DeYoung et al.
(2013), Bhagat and Bolton (2014), Gande, and Kalpathy (2017) who found a positive
relationship between compensation and risk taking both in financial and non-financial firms.
The F-statistics from both regression is statistically significant with p-value 0.000 which
indicate that the model is well specified.
Since, there is no precise test to assess the validity and relevance of instruments. I used Stata
user written xtivreg2 post estimation to test the instruments for under identification, weak
identification, and over identification. In addition, the independent variable was tested for
endogeneity. The F-statistics (Cragg-Donald Wald F-statistic and Sanderson-Windmeijer) is
greater than the Stock and Yogo (2005) 10% critical values (19.93, in the case of two
instruments and one endogenous variable) and (16.38, in the case of one instrument and one
endogenous variable) expect for equity in financial firms for all forms of compensation.
For the level of compensation with one instrument and one endogenous variable, F-statistics (
Cragg-Donald Wald F-statistic and Sanderson-Windmeijer ) is greater than Stock and Yogo
(2005) 10% and 15% critical values (16.38 and 8.96) only in the case of non-financial firms
for equity percent and both salary and bonus percent respectively. This shows that, the
instruments are not weak, thus we can easily reject the null hypothesis that our instrument are
weak expect for the case of level of compensation in financial firms.
If I am to follow the earlier rule of Thumb, F-statistics from first stage regressions are greater
than 10 in all regressions expects equity in financial firms for all forms of compensation, which
further confirms the instruments are strong. However, for the level of compensation, F-statistics
are only greater than 10 in salary percent and equity percent regressions in non-financial firms.
21
The overall F statistics for excluded variables are significant at 1 % for all forms of
compensation expect equity compensation in financial firms. This further confirms the validity
of my instruments. For the level of compensation, overall F-statistics is only significant in non-
financial firms. Since most regressions have two instruments against one endogenous variables,
the exogeneity of instruments are tested using Hansen Sargan test for over-identifying
restrictions. The Hansen J statistics is not significantly different from zero for all regressions
with two instruments which further supports the validity of instruments.
To assess whether compensation is in fact endogenous, the endog option included in xtivreg2
gave F-statistics with significant P-values mostly at 1% level. This shows that compensation is
endogenous and I am correct in treating the forms and level of compensation as endogenous in
all regressions. Finally, my regressions do not suffer from under-identification, and weak
instrument choice, the Sanderson-Windmeijer and Anderson canon. Corr. LM statistic F-
statistics are all significant at 1% level. Overall, the post estimation tests using Stata xtivreg2
established that compensation is endogenous and my instruments are strong and valid, hence
regressions are properly identified and reliable.
********** [Insert Table 3] **********
********** [Insert Table 4] **********
********** [Insert Table 5] **********
4.4.1 Robustness checks
To assess the robustness of results, the regressions where run using alternative measure of book
leverage and R&D. In this set of regressions, book leverage is measured as total long-term debt
scaled by total assets. R&D (scaled by total assets) is treated as missing where Compustat does
not report R&D. From table 6, with book leverage measured as total long-term debt scaled by
total assets. The coefficients on compensation is positive and highly significant in non-financial
firms expect for equity percent which suggests that higher equity reduces leverage which is
very similar to that of table 4. For financial firms, the coefficient on compensation is positive
and significant in most forms of compensation but barely significant in the level of
compensation with higher salary reducing leverage in financial firms consistent with results in
table 4.
From table 7, When R&D is treated as missing against zero in table 5, the coefficients on
compensation is positive expect for bonus, total wealth, salary percent and bonus percent in
22
financial firms but insignificant. The coefficient on compensation in non-financial firms is
higher, remains positive and significant expect for salary percent. This could be explained by
the fact that R&D is higher in non-financial firms with mean (£32,000) compared to financial
firms with mean (£2000). Most firms in the financial firms do not invest in R&D as data suggest
R&D as less important to them.
********** [Insert Table 6] **********
********** [Insert Table 7] **********
5.0 Conclusion
I used panel data to control for unobservable heterogeneity in contracting environment of firms,
using a set of simultaneous equations to evaluate the relationship between compensation and
riskier corporate decisions in UK. I examine the relationship using six measure of
compensation and two instruments (CEO age and experience) using FE-2SLS. Results support
my hypothesis of positive and increasing relationship between compensation and riskier
corporate decisions. CEOs with high compensation employ more leverage and invest more in
R&D as there is a positive relationship between salary, bonus, direct compensation, equity
compensation, total compensation, total wealth, and bonus percent. Results are barely
significant in financial firms but significant in non-financial firms when leverage is measured
as total abilities scaled by total assets and vice versa when measured as total debt to equity.
The level of significance look similar with leverage as debt to assets. It also suggest that the
level of pay measured by the proportion of each forms of compensation in total compensation
matters, as equity percent and salary percent can reduce risk taking in non-financial firms and
financial firms respectively. Further analysis on compensation at the top and bottom, shows
that CEOs with compensation in 75th percentile (top) employ more leverage. Finally, total
wealth also increase risk taking although it matters more in non-financial firms despite financial
firms recoding total wealth four times more the non-financial firms.
23
Table 1 Correlation between variables of interest
Financial Firms
1 2 3 4 5 6 7 8 9 10 11 12
1 Salary 1.00
2 Bonus 0.48 1.00
3 Direct Compensation 0.69 0.93 1.00
4 Equity 0.57 0.50 0.58 1.00
5 Total Compensation 0.66 0.73 0.82 0.92 1.00
6 Total Wealth 0.12 0.34 0.34 0.24 0.32 1.00
7 Salary-percent -0.40 -0.61 -0.62 -0.56 -0.64 -0.16 1.00
8 Bonus-percent 0.13 0.65 0.54 -0.02 0.22 0.18 -0.49 1.00
9 Equity-percent 0.37 0.22 0.29 0.66 0.56 0.03 -0.76 -0.16 1.00
10 Book leverage 0.45 0.24 0.34 0.28 0.33 0.08 -0.20 0.06 0.18 1.00
11 R&D -0.08 -0.07 -0.08 -0.06 -0.07 -0.04 0.12 -0.09 -0.07 -0.07 1.00
12 ROA 0.17 0.21 0.22 0.11 0.16 0.18 -0.28 0.29 0.10 -0.01 -0.37 1.00
13 Firm size 0.58 0.19 0.35 0.42 0.43 0.03 -0.20 -0.04 0.26 0.29 -0.03 0.03
Non-financial firm variables
1 2 3 4 5 6 7 8 9 10 11 12
1 Salary 1.00
2 Bonus 0.69 1.00
3
Direct
Compensation 0.89 0.93 1.00
4 Equity 0.60 0.58 0.64 1.00
5
Total
Compensation 0.75 0.74 0.81 0.96 1.00
6 Total Wealth 0.30 0.29 0.32 0.29 0.32 1.00
7 Salary-percent -0.39 -0.51 -0.49 -0.54 -0.57 -0.15 1.00
24
8 Bonus-percent 0.24 0.53 0.43 -0.01 0.14 0.11 -0.38 1.00
9 Equity-percent 0.30 0.26 0.29 0.59 0.54 0.10 -0.86 -0.13 1.00
10 Book leverage 0.23 0.15 0.20 0.10 0.14 0.02 -0.08 0.08 0.04 1.00
11 R&D -0.13 -0.09 -0.12 -0.06 -0.09 -0.06 0.08 -0.08 -0.04 -0.07 1.00
12 ROA 0.25 0.20 0.24 0.13 0.18 0.14 -0.21 0.23 0.10 -0.08 -0.39 1.00
13 Firm size 0.45 0.36 0.43 0.49 0.51 0.16 -0.20 0.04 0.19 0.06 -0.05 0.06
Table 2 Compensation in UK firms
This table presents descriptive statistics for 216 listed UK financial firms and 1,645 listed UK Non-financial firms from 1999
to 2017. CEO compensation and firm size are absolute numbers in million pounds, other variables are in percentages expect
CEO age and CEO experience in absolute numbers. CEO turnover is a dummy variable. Variables are defined in table 8. Data
are presented after winsorization; some firms did not report a breakdown of direct compensation.
Financial Firms Non-financial Firms
Variable Mean Standard Deviation
Median Max Mean Standard Deviation
Median Max
Salary (£) 0.322 0.246 0.25 1.125 0.289 0.215 0.226 1.066
Bonus (£) 0.361 0.611 0.084 3.223 0.153 0.268 0.035 1.498
Direct Compensation (£)
0.696 0.77 0.400 3.95 0.445 0.453 0.285 2.465
Equity Compensation (£)
0.696 1.268 0.037 6.392 0.451 1.043 0.043 6.976
Total Compensation (£)
1.42 1.895 0.549 9.288 0.898 1.374 0.39 8.673
Total Wealth (£) 16.05 62.389 2.521 480.22 6.239 16.364 1.299 119.719
Salary-percent (%) 0.524 0.344 0.453 1.000 0.596 0.313 0.588 1.000
Bonus-percent (%) 0.199 0.221 0.151 0.995 0.139 0.16 0.092 1.000
Equity-percent (%) 0.274 0.302 0.138 0.999 0.263 0.289 0.000 0.999
Book leverage (%) 0.581 0.315 0.659 0.996 0.475 0.224 0.50 0.999
R&D (%) 0.002 0.012 0.00 0.107 0.03 0.081 0.00 0.54
ROA (%) -0.015 0.204 0.012 0.346 -0.039 0.253 0.029 0.265
Firm size 50765.12 182232.7 309.173 1227361 1277.613 4104.828 94.11 29216
CEO turnover 0.107 0.310 0.000 1.000 0.109 0.311 0.00 1.00
CEO experience 4.791 5.327 3.200 33.200 5.014 5.131 3.40 43.50
CEO Age 51.215 7.422 51 76 50.846 7.324 51 82
Number of Observations
1710 1710 1710 1710 13053 13053 13053 13053
25
Table 3 First Stage Regression
This table presents result from first stage regression. Compensation is treated as endogenous. The exogenous variables are
ROA, firm size, and CEO turnover. CEO experience and CEO age are instruments with Book leverage and R&D as the
dependent variable. The t-statistics are presented in brackets below coefficients. All variables are defined in table 8. Singleton
groups detected, for financial firms 11 observations not used and for non-financial firms, 107 observations not used. ‘*’ ‘**’ ‘***’ indicate
statistical significance at 10%, 5%, and 1% level respectively.
Financial firms
Ln Salary Ln
Bonus
LnDirect
Compensation
Ln Equity LnTotal
Compensation
LnTotal
Wealth
Salary
percent
Bonus
percent
Equity
percent ROA 0.101 3.706*** 0.412*** 1.34* 0.461*** 2.561*** -0.162*** 0.119*** 0.051
(0.9) (5.14) (3.11) (1.75) (4.12) (7.09) (4.37) (4.33) (1.27)
CEO turnover 0.04 0.654 0.077 0.765* 0.190** -0.075 -0.056** 0.009 0.048** (0.59) (1.54) (0.99) (1.68) (2.88) (-0.35) (-2.57) (0.61) (2.05)
Lnfirm size 29.287 147.33 31.34 266.32* 17.11 -125.10* -9.114 2.17 7.391 (1.31) (1.04) (1.2) (1.75) (0.77) (1.77) (1.24) (0.4) (0.94)
LnCEO experience 0.194*** 0.719*** 0.226*** 0.360** 0.201*** 0.587*** -0.015* 0.015**
(7.89) (4.57) (7.81) (2.14) (8.22) (7.44) (-1.86) (2.57)
Lnage -6.9
-7.58
-4.49 31.68*
-2.14 (-1.20)
(-1.13)
(-0.79) (1.73)
(-1.05)
F test of excluded instruments: 31.91*** 20.87*** 31.21*** 4.59*** 34.16*** 29.08*** 3.46*** 6.62*** 1.11*** Under identification test
Sanderson-Windmeijer 64.03*** 20.93*** 62.62*** 4.6** 68.54*** 58.36*** 3.47* 6.64** 1.12
Under identification test
Anderson canon. corr. LM
statistic
61.39*** 20.64*** 60.1*** 4.58** 65.54*** 56.17*** 3.46*** 6.61*** 1.11
weak id Sanderson-Windmeijer 31.91 20.87 31.21 4.59 34.16 29.08 3.46 6.62 1.11 Weak identification (Cragg-
Donald Wald F statistic): 31.91 20.87 31.21 4..59 34.16 29.08 3.46 6.62 1.11
J-statistic (over identification) 1.606 0 1.649 0 1.874 3.579 0 0 0 P-values 0.205 0 0.02 0 0.171 0.06 0 0 0
Number of Observations 1699 1699 1699 1699 1699 1699 1699 1699 1699
Non-financial firms
Ln Salary Ln
Bonus
LnDirect
Compensation
Ln Equity LnTotal
Compensation
LnTotal
Wealth
Salary
percent
Bonus
percent
Equity
percent ROA -0.018 2.512*** 0.1195
1.089*** 0.265*** 1.512*** -0.128*** 0.065*** 0.064***
(-0.60) (12.45) (3.69) (4.87) (8.81) (11.95) (-11.88) (10.9) 5.92
CEO turnover 0 -0.246 0.15 0.525** 0.069*** -0.718*** -0.027*** -0.01** 0.039***
0 (-1.56) (0.6) (3.01) (2.93) (-7.27) (-3.20) (2.41) (4.58) Lnfirmsize 40.55*** 229.9*** 44.57*** 240.65*** 38.81*** -0.062 -6.36** 2.36 4.53
(-4.87) (-4.15) (-5.04) (-3.92) (-4.6) (0) (-2.15) (-1.45) (1,52) LnCEO experience 0.224*** 0.334*** 0.238***
0.16*** 0.516*** 0.011*** 0.005*** -0.02***
(26.24) (5.88) (26.22)
(18.93) (14.47) (3.74) (2.98) (-5.26) Lnage -10.07*** -11.180*** -64.91*** -9.886*** -0.847
(-4.71)
(-4.92) (-4.11) (-4.65) (-0.09)
F test of excluded instruments: 355.85*** 34.52*** 356.35*** 16.97*** 190.32*** 104.74*** 13.98*** 8.90*** 27.67***
Under identification test
Sanderson-Windmeijer 712*** 34.53*** 713.02*** 16.97*** 380.8*** 209.57*** 13.98*** 8.91*** 27.68***
26
Table 4 Second Stage Regression with Book Leverage as Dependent Variable
This table presents results from the second stage FE-2SLS regression with firm level fixed effects; the instruments are
excluded from this regression. The dependent variable Book leverage is regressed on the predicted values of compensation
from the first regression. Book leverage is measured as total liability scaled by total asset in non-financial firms, and as long-
term debt scaled by equity in financial firms. The t-statistics are presented below coefficients in brackets. Compensation in
columns A, B, C, D, E, F, G, I represents predicted values of salary, bonus, direct compensation, equity, total compensation, total wealth, and
salary-percent bonus-percent equity-percent respectively. All variables are defined in table 8. ‘*’ ‘**’ ‘***’ indicate statistical significance at
10%, 5%, and 1% level respectively.
Under identification test
Anderson canon. corr. LM
statistic
670.18*** 34.43*** 671.07*** 16.95*** 368.5*** 205.79*** 13.96*** 8.9*** 27.62***
weak id Sanderson-Windmeijer 355.85 34.52 356.35 16.97 190.32 104.74 13.98 8.9 27.67 Weak identification test (Cragg-
Donald Wald F statistic): 355.85 34.52 356.35 16.97 190.32 104.74 13.98 8.9 27.67
J-statistic (over identification) 2.126 0 2.027 0 1.336 4.311 0 0 0 P-values 0.145 0 0.155 0 0.248 0.04 0 0 0
Number of Observations 12,946 12,946 12,946 12,946 12,946 12,946 12,946 12,946 12,946
Financial firms
Dependent Variable is Book leverage
A B C D E F G H I
^
Compensation
0.604*** 0.155*** 0.518*** 0.312* 0.576*** 0.168*** -7.180* 7.247** 4.85
(3.67) (2.92) (3.65) (1.90) (3.67) (3.16) (-1.75) (2.15) (0.91)
ROA -0.401** -0.907*** -0.554*** -0.763** -0..609*** -0.776*** -1.503** -1.182** -0.616*
(-2.67) (-3.50) (-3.47) (-2.20) (-3.74) (-3.94) (-2.09) (-2.61) (-1.67)
CEO
Turnover
0.247*** 0.161* 0.229** 0.020 0.158** 0.255*** -0.147 0.194 -0.131
(2.91) (1.76) (2.77) (0.14) (2.10) (2.89) (-0.72) (1.50) (-0.47)
Lnfirmsize -3.43** 0.175 -2.905** 3.94 -1.662 -1.183 5.571 -0.983 4.02
(-2.62) (0.14) (-2.36) (1.33) (-1.56) (-1.15) (1.39) (-0.58) (0.78)
F-statistics 5.55*** 3.65*** 5.50*** 1.55 5.60*** 4.77*** 1.30*** 1.98* 1.09
Observations 1699 1699 1699 1699 1699 1699 1699 1699 1699
Non-financial firms ^
Compensation
0.039*** 0.025*** 0.037*** 0.018* 0.055*** 0.016*** 0.732*** 1.67** -0.516***
(4.37) (3.36) (4.38) (1.93) (4.36) (3.87) (2.82) (2.44) (-3.32)
ROA -0.258*** -0.321*** -0.263*** -0.277*** -0.273*** -0.282*** -0.164*** -0.366*** -0.225***
(-35.29) (-15.21) (-35.35) (-20.80) (-32.68) (-27.70) (-4.74) (-7.88)) (-17.15)
CEO
Turnover
0.024*** 0.029*** 0.023*** 0.003 0.020*** 0.035*** 0.043*** 0.042*** 0.043***
(4.18) (3.63) (4.14) (0.43) (3.74) (4.30) (3.19) (2.76) (3.76)
Lnfirmsize -0.183 0.047 -0.168** 0.169 -0.108 -0.066 -0.653** 0.027 -0.444***
(-2.53)*** (0.55) (-2.39) (1.39) (-1.63) (-1.00) (-2.71) (0.26) (-3.00)
F-statistics 321.47*** 221.99*** 356.35*** 239.70*** 308.70*** 156.18*** 14.52*** 116.71*** 216.48***
Observations 12946 12946 12946 12946 12946 12946 12946 12946 12946
27
Table 5 Second Stage Regression with R&D as Dependent Variable
This table presents results from the second stage FE-2SLS regression with firm level fixed effects; the instruments are excluded from this regression. The dependent variable R&D is regressed on the predicted values of compensation from the first regression. The t-statistics are presented below coefficients in brackets. Compensation in columns A, B, C, D, E, F, G, I represents predicted values of salary, bonus, direct compensation, equity, total compensation, total wealth, and salary-percent bonus-percent equity-percent respectively. All variables are defined in table 8. ‘*’ ‘**’ ‘***’ indicate statistical significance at 10%, 5%, and 1% level respectively.
Financial firms
Dependent variable is R&D
A B C D E F G H I
^
Compensation
0.005*** 0.001*** 0.004*** 0.003* 0.005*** 0.002*** -0.061 0.059** -0.014
(2.93) (2.60) (2.93) (1.73) (2.97) (2.97) (-1.60) (2.00) (-0.043)
ROA -0.008*** -0.012*** -0.009*** -0.011*** -0.009*** -0.011*** -0.017** -0.014*** -0.007***
(-5.5) (-5.00) (-5.96) (-3.45) (-6.04) (-5.82) (-2.56) (-3.52) (-2.99)
CEO
Turnover
-0.000** -0.001 -0.000** -0.002* -0.001* -0.000 -0.004** -0.0007 -0.001
(-0.48) (-1.18) (-0.64) (-1.66) (-1.43) (-0.04) (-1.90) (-0.65) (-0.44)
Lnfirmsize -0.047*** -0.020* -0.043*** 0.010 -0.035*** -0.032*** 0.025 -0.028** -0.037
(-3.86) (-1.77) (-3.78) (0.40) (-3.44) (-3.16) (0.70) (-1.96) (-1.18)
F-statistics 11.53*** 8.60*** 11.46*** 4.00** 11.52*** 11.06*** 3.20*** 5.06*** 8.97***
Observations 1699 1699 1699 1699 1699 1699 1699 1699 1699
Non-financial firms ^
Compensation
0.009*** 0.005*** 0.009*** 0.008** 0.013*** 0.003*** 0.155** 0.352** -0.109**
(3.52) (2.65) (3.54) (2.69) (3.66) (2.93) (2.33) (2.11) (-2.59)
ROA -0.066*** -0.079*** -0.067*** -0.075*** -0.069*** -0.071*** -0.046*** -0.089*** -0.059***
(-31.08) (-13.96) (-31.07) (-17.18) (-29.03) (-24.86) (-5.19) (-7.80) (-16.54)
CEO
Turnover
0.003* 0.004* 0.003 -0.004* 0.002 0.004** 0.006 0.006* 0.003
(1.69) (1.68) (1.64) (-1.71) (1.29) (2.13) (1.90) (1.70) (1.27)
Lnfirmsize 0.016 0.070*** 0.019 0.140*** 0.033* 0.044** -0.080 0.063** 0.004
0.75 (3.27) (0.93) (3.53) (1.71) (2.37) (-1.30) (2.48) (0.12)
F-statistics 246.74*** 198.19*** 246.56*** 146.38*** 242.84*** 238.77*** 153.13*** 125.64*** 189.46***
Observations 12,946 12,946 12,946 12,946 12,946 12,946 12,946 12,946 12,946
.
28
Table 6 Second stage regression with Book Leverage as Long-term-debt scaled by Total assets
This table presents results from the second stage FE-2SLS regression with firm level fixed effects; the instruments are
excluded from this regression. The dependent variable Book leverage is regressed on the predicted values of compensation
from the first regression. The t-statistics are presented below coefficients in brackets. Compensation in columns A, B, C, D, E, F,
G, I represents predicted values of salary, bonus, direct compensation, equity, total compensation, total wealth, and salary-percent bonus-
percent equity-percent respectively. All variables are defined in table 8. Book leverage is measured ad Total long-term debt scaled by Total
assets. . ‘*’ ‘**’ ‘***’ indicate statistical significance at 10%, 5%, and 1% level respectively.
Financial firms
Dependent variable is Book leverage
A B C D E F G H I
^
Compensation
0.034** 0.009* 0.029** 0.017 0.033** 0.009* -0.414 0.405 0.460
(2.26) (1.92) (2.25) (1.53) (2.20) (1.71) (-1.39) (1.60) (0.86)
ROA -0.064*** -0.092*** -0.072*** -0.083*** -0.075*** -0.082*** -0.126** -0.108*** -0.084**
(-4.60) (-4.22) (-4.94) (-3.54) (-4.97) (-4.47) (-2.47) (-3.15) (-2.42)
CEO
Turnover
0.027*** 0.021*** 0..025*** 0.014 0.021*** 0.026*** 0.004 0.023** -0.004
(3.38) (2.80) (3.33) (1.40) (3.03) (3.13) (0.25) (2.43) (-0.14)
Lnfirmsize -0.062 0.143 -0.033 0.347* 0.036 0.071 0.447 0.084 0.519
(-0.52) (1.40) (-0.29) (1.73) (0.36) (0.73) (1.60) (0.68) (1.04)
F-statistics 8.51*** 7.13*** 8.45*** 4.51*** 8.46*** 8.00*** 3.75*** 4.93*** 2.91***
Observations 1699 1699 1699 1699 1699 1699 1699 1699 1699
Non-financial firms ^
Compensation
0.019*** 0.011*** 0.017*** 0.009* 0.026*** 0.007*** 0.337** 0.767** -0.238***
(3.27) (2.64) (3.27) (1.75) (3.31) (2.92) (2.38) (2.08) (-2.63)
ROA -0.038*** -0.068*** -0.041*** -0.049*** -0.046*** -0.050*** 0.004 -0.089*** -0.024***
(-8.48) (-5.48) (-8.84) (-6.15) (-8.88)) (-8.09) (0.24) (-3.54) (-3.09)
CEO
Turnover
0.014*** 0.016*** 0.014*** 0.004 0.012*** 0.019*** 0.023*** 0.022*** 0.023***
(3.96) (3.49) (3.94) (0.93) (3.72) (3.87) (3.11) (2.73) (3.43)
Lnfirmsize -0.029 0.078** -0.022 0.151** 0.006 0.026 -0.244* 0.070 -0.148*
(-0.64) (1.72) (-0.50) (2.06) (0.14) (0.65) (-1.86) (1.21) (-1.72)
F-statistics 23.27*** 17.92*** 23.21*** 17.59*** 22.82*** 21.86*** 14.62*** 11.19*** 17.79***
Observations 12,946 12,946 12,946 12,946 12,946 12,946 12,946 12,946 12,946
29
Table 7 Second stage Regressions with R&D treated as Missing
This table presents results from the second stage FE-2SLS regression with firm level fixed effects. The instruments are
excluded from this regression. The dependent variable R&D is regressed on the predicted values of compensation from the
first regression. The t-statistics are presented below coefficients in brackets. Compensation in columns A, B, C, D, E, F, G, I
represents predicted values of salary, bonus, direct compensation, equity, total compensation, total wealth, salary-percent bonus-percent
equity-percent respectively All variables are defined in table 8. R&D is treated as missing when not reported in Compustat.
‘*’ ‘**’ ‘***’ indicate statistical significance at 10%, 5%, and 1% level respectively
Financial firms
Dependent variable R&D
A B C D E F G H I
^
Compensation
0.009 -0.0004 0.002 -0.0003 0.052 -0.001 0.006 -0.031 0.176
(0.23) (-0.07) (0.03) (-0.07) (0.21) (-0.16) (0.07) (-0.07) (0.24)
ROA -0.003 -0.003 -0.003 -0.001 -0.048 -0.001 -0.002 -0.007 -0.053
(-0.26) (-0.23) (-0.22) (-0.03) (-0.23) (-0.03) (-0.12) (-0.12) (-0.26)
CEO
Turnover
0.002 0.001 0.0004 0.00001 0.026 -0.002 0.0002 0.001 0.017
(0.17) (0.07) (0.03) (0.00) (0.21) (-0.13) (-0.02) (0.07) (0.24)
Lnfirmsize -0.012 0.009 0.005 0.014 -0.177 0.009 0.011 -0.002 -0.090
(-0.11) (0.14) (0.03) (0.15) (-0.20) (0.12) (0.15) (-0.01) (-0.20)
F-statistics 0.05 0.04 0.03 0.03 0.02 0.04 0.03 0.03 0.02
Observations 1699 1699 1699 1699 1699 1699 1699 1699 1699
Non-financial firms ^
Compensation
0.016** 0.007* 0.014** 0.038 0.020** 0.004 0.635 0.359 -0.234
(2.09) (1.68) (2.13) (0.56) (2.47) (1.45) (0.67) (1.48) (-1.36)
ROA -0.160*** -0.179*** -0.162*** -0.192*** -0.165*** -0.164*** -0.081 -0.185*** -0.147***
(-38.80) (-13.68) (-36.35) (-3.09) (-33.92) (-29.18) (-0.70) (-9.87) (-14.51)
CEO
Turnover
0.003 0.002 0.003 -0.022 0.0002 0.002 0.036 0.002 0.016
(0.81) (0.53) (0.74) (-0.63) (0.09) (0.41) (0.62) (0.52) (1.09)
Lnfirmsize -0.024 0.094* -0.014 0.435 -0.002 0.009 -0.484 0.074 -0.129
(-0.52) (1.72) (-0.33) (0.60) (-0.06) (0.22) (-0.62) (1.36) (-0.99)
F-statistics 389.88*** 309.02*** 390.94*** 34.13*** 382.68*** 391.22*** 49.85*** 238.98*** 201.40***
Observations 12,946 12,946 12,946 12,946 12,946 12,946 12,946 12,946 12,946
30
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32
Appendix
Table 8 Variables Definition
Variable
name
Variable definition Data source WRDS
codes
Book
Leverage
This ratio represents book value total liabilities
scaled by book value of total assets in non-financial
firms
WRDS Compustat LT, AT
Firm size This item represents the total assets. WRDS Compustat AT
ROA This ratio represents income used to calculate
earnings per share as reported by the company
scaled by total assets.
NICON, AT
WRDS Compustat
Research and
Development
(R&D)
This ratio represents all costs incurred during the
year that relate to the development of new products
or services scaled by total assets.
WRDS Compustat XRD, AT
Equity
compensation
This is the sum of shares awarded, values of options
awarded and long-term incentive plan (LTIPs)
awarded in the period
BOARDEX
Direct
compensation
This represents the sum of salary and bonus. BOARDEX
Total
compensation
Total direct compensation plus total equity linked
compensation for the period
BOARDEX
Total wealth Value of cumulative holdings over time of stock,
options, and LTIPs for the individual or the
appropriate averages.
BOARDEX
Salary Base annual pay BOARDEX
Bonus An annual payment made in addition to salary BOARDEX
Salary-
percent
This is salary scaled by total compensation, which
captures the level of salary in total pay.
Bonus-
percent
This is bonus scaled by total compensation, which
captures the level of bonus in total pay.
Equity-
percent
This is bonus scaled by total compensation, which
captures the level of equity in total pay.
CEO This is a dummy variable that indicate a change in
the person occupying the post of CEO. Variable
equals 1 if there is a change in CEO or 0 otherwise.
Turnover
CEO
experience
This is measured as the number of years spent by
the CEO in his/her role.
BOARDEX
CEO age This is measured by the age of CEO. BOARDEX
Book leverage Book value of total long-term debt (components of
liability) scaled by book value of common/ordinary
equity-total in financial firms.
WRDS Compustat DLTT, CEQ
Book leverage Book value of total long term debt (components of
liability) scaled by book value of total assets
WRDS Compustat DLTT
Research and
Development
(R&D)
This ratio represents all costs incurred during the
year that relate to the development of new products
or services scaled by total assets.
XRD, AT