1

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.

a. y.g.muradoglu@qmul.ac.uk

b. r.a.yusuf@qmul.ac.uk

c. d.bathia@qmul.ac.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

2

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?

3

‘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

4

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

5

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

6

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

7

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

8

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.

9

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

10

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.

11

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

12

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)

13

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

14

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

15

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

References

Aggarwal, R.K., and Samwick, A.A. (2003). Performance Incentives within firms: The effect

of managerial responsibility. Journal of Finance.

Armstrong, C. S., & Vashishtha, R. (2012). Executive stock options, differential risk-taking

incentives, and firm value. Journal of Financial Economics, 104(1), 70–88.

https://doi.org/10.1016/j.jfineco.2011.11.005

Bebchuk, Lucian A., and Jesse M. Fried. 2004. Pay without Performance: The Unfulfilled

Promise of Executive Compensation. Cambridge and London: Harvard University Press.

Bhagat, S., & Bolton, B. (2014). Financial crisis and bank executive incentive compensation.

Journal of Corporate Finance, 25, 313–341.

https://doi.org/10.1016/j.jcorpfin.2014.01.002

Bliss, R. T., & Rosen, R. J. (2001). CEO compensation and bank mergers. Journal of Financial

Economics, 61(1), 107–138. https://doi.org/10.1016/S0304-405X(01)00057-5

Bushman, R., Dai, Z., & Wang, X. (2010). Risk and CEO turnover. Journal of Financial

Economics, 96(3), 381–398. https://doi.org/10.1016/j.jfineco.2010.03.001

Cao, M., & Wang, R. (2013). Optimal CEO compensation with search: Theory and empirical

evidence. Journal of Finance. https://doi.org/10.1111/jofi.12069

Cen and Doukas (2017), CEO Personal investment decisions and firm risk,. European Financial

Management. doi: 10.1111/eufm.12117.

Chava, S., & Purnanandam, A. (2010). CEOs versus CFOs: Incentives and corporate policies.

Journal of Financial Economics, 97(2), 263–278.

https://doi.org/10.1016/j.jfineco.2010.03.018

Coles, J. L., Daniel, N. D., & Naveen, L. (2006). Managerial incentives and risk-taking.

Journal of Financial Economics, 79(2), 431–468.

https://doi.org/10.1016/j.jfineco.2004.09.004

Conyon, M. J., & He, L. (2011). Executive compensation and corporate governance in China.

Journal of Corporate Finance. https://doi.org/10.1016/j.jcorpfin.2011.04.006

Core, J. E., Holthausen, R. W., & Larcker, D. F. (1999). Corporate governance, chief executive

officer compensation, and firm performance. Journal of Financial Economics, 51(3), 371–406.

https://doi.org/10.1016/S0304-405X(98)00058-0

Deyoung, R., Peng, E. Y., & Yan, M. (2013). Executive compensation and business policy

choices at U.S. commercial banks. Journal of Financial and Quantitative Analysis (Vol.

48). https://doi.org/10.1017/S0022109012000646

Fahlenbrach, R., & Stulz, R. M. (2011). Bank CEO incentives and the credit crisis. Journal of

Financial Economics, 99(1), 11–26. https://doi.org/10.1016/j.jfineco.2010.08.010

Fernandes, N., Ferreira, M. A., Matos, P., & Murphy, K. J. (2018). The Society for Financial

Studies Are U . S . CEOs Paid More ? New International Evidence Author ( s ): Nuno

Fernandes , Miguel A . Ferreira , Pedro Matos and Kevin J . Murphy Source : The Review

of Financial Studies , Vol . 26 , No . 2 ( February 2013 ), 26(2), 323–367.

https://doi.org/10.1093/rfs/hhsl22

Gande, A., & Kalpathy, S. (2017). CEO compensation and risk-taking at financial firms:

Evidence from U.S. federal loan assistance. Journal of Corporate Finance, 47, 131–150.

https://doi.org/10.1016/j.jcorpfin.2017.09.001

Gibbsons, R., and Murphy, K. J. (1992). Optimal incentive contracts in the presence of career

concerns: Theory and Evidence. Journal of Political Economy.

Guay, W., R. (1999). The sensitivity of CEO wealth to equity risk: an analysis of the magnitude

and determinants. Journal of Financial Economics, 53, 43-71.

31

Hagendorff, J., & Vallascas, F. (2011). CEO pay incentives and risk-taking: Evidence from

bank acquisitions. Journal of Corporate Finance, 17(4), 1078–1095.

https://doi.org/10.1016/j.jcorpfin.2011.04.009

Harford, J., & Li, K. (2007). Decoupling CEO wealth and firm performance: The case of

acquiring CEOs. Journal of Finance, 62(2), 917–949. https://doi.org/10.1111/j.1540-

6261.2007.01227.x

Hsiao, C. (2007). Panel data analysis—advantages and challenges. Test, 16(1), 1-22. doi:

10.1007/s11749-007-0046-x

Jensen, M. C., & Meckling, W. H. (1976). Theory of the Firm: Managerial Behavior, Agency

Costs and Ownership Structure. Journal of Financial Economics, 3(4), 305–360.

https://doi.org/http://dx.doi.org/10.1016/0304-405X(76)90026-X

Jin, L. (2002). CEO compensation, diversification, and incentives. Journal of Financial

Economics, 66(1), 29–63. https://doi.org/10.1016/S0304-405X(02)00150-2

Larcker, D. F., and Tayan, B. (2012). Does your CEO compensation plan provide the right

incentives?. McKinsey Quarterly.

Minnick, K., Unal, H., & Yang, L. (2011). Pay for performance? CEO compensation and

acquirer returns in BHCs. Review of Financial Studies, 24(2), 439–472.

https://doi.org/10.1093/rfs/hhq107

Murphy, J. K. (1985). Corporate performance and managerial remuneration, an empirical

analysis. Journal of Accounting and Economics

Palia, D. (2001). The Endogeneity of Managerial Compensation in Firm Valuation :, 14(3),

735–764.

Perry, T., & Zenner, M. (2001). Pay for performance? Government regulation and the structure

of compensation contracts. Journal of Financial Economics, 62(3), 453–488.

https://doi.org/10.1016/S0304-405X(01)00083-6

Rajan, R. G., & Zingales, L. (1995). What Do We Know about Capital Structure? Some

Evidence from International Data What Do We Know about Capital Structure? Some

Evidence from International Data. Source: The Journal of Finance THE JOURNAL OF

FINANCE, 50(5), 1421–1460. https://doi.org/10.2307/2329322

Semykina, A., & Wooldridge, J. M. (2010). Estimating panel data models in the presence of

endogeneity and selection. Journal of Econometrics, 157(2), 375-380. DOI:

10.1016/j.jeconom.2010.03.039

Wallace, T. (2015). Q&A: What is bank ring-fencing?. The Telegraph, 15 October 2015.

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