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UNIVERSITY
THE RELATIONSHIP BETWEEN EXECUTIVE REMUNERATION AND CREDIT
RISK OF BANKS LISTED IN KENYA
KINYANJUI BRENDA WANGECHI
067166
Submitted in partial fulfillment of the requirements for the Degree of
Bachelor of Business Science-Finance at Strathmore University
School of Finance and Applied Economics
Strathmore University
Nairobi, Kenya
November, 2015
This Research Project is available for Library use on the understanding that it is
copyright material and that no quotation from the Research Project may be published
without proper acknowledgement.
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DECLARATION
I declare that this work has not been previously submitted and approved for the award
of a degree by this or any other University. To the best of my knowledge and belief, the
Research Project contains no material previously published or written by another
person except where due reference is made in the Research Project itself.
© No part of this Research Project may be reproduced without the permission of the
author and Strathmore University
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This Research Project has been submitted for examination with my approval as the
Supervisor.
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... ..... .. ... .. .... .... ..........~ [Signature]
........ .........1..6. ...I.L.) ~'O..L>..~ [Date]
School of Finance and Applied Economics
Strathmore University
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ABSTRACT
The collapse of the financial system in 2008 brought into light the strong impact that executive
remuneration had in the management of credit risk in banks is the United States. The relationship
of agency looks at executive pay as a mode of linking the interests of shareholders to that of
management. This study attempts to reveal the relationship between the measures of credit risk
and executive remuneration and give an overall assessment of the impact of executive
remuneration on credit risk in Kenyan banks. It will enable shareholders be able to know to what
'extent they can use executive remuneration to control credit risk inbanks. It can also be used by
the government to ensure proper credit risk management in banks for the sound health of the
financial system. A panel data from eleven listed commercial banks in Kenya covering a seven
year period (2008-2014) was analyzed within the random effects framework. The results from
this study find a positive but insignificant relationship between credit risk and executive
remuneration. The study can be extended to include the structure of executive remuneration
especially with the introduction of a derivatives market in Kenya and the possibility of the
inclusion of share options in the pay structure of management.
Key words: Credit risk, Executive remuneration, Agency, Banks, Kenya,
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TABLE OF CONTENTS
1. Introduction - 5 -
1.1 Background - 5 -
1.2 Problem Statement - 7 -
1.3 Research Objectives - 8 -
1.4 Research Questions - 8-
1.5 Significance of the Study , - 8 -r
2. Literature Review - 9 -
2.1 The Agency Problem - 9-
2.2 The Executive Remuneration Solution - 9 -
2.3 Empirical Studies - 12 -
2.4 Credit Analysis Measures -16 -
2.5 The Knowledge Gap -17-
2.6 Conceptual Framework -17 -
3. Methodology - 18 -
3.1 Research Design - 18 -
3.2 Population and Sample - 18 -
3.3 Data Collection Methods - 19 -
3.4 Data Analysis -19 -
4. Data Analysis and Findings - 22 -
4.1 Descriptive Statistics - 22-
4.2 Correlation Analysis - 24 -
4.3 Regression Analysis - 24-
4.3.1 Loans and Advances - 25 -
4.3.2 Non Performing Loans - 26 -
4.3.3 Net Charge Off - 27 -
5. Discussion, Summary and Conclusion - 29 -
5.1 Discussion - 29 -
5.2 Conclusion - 31 -
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References - 32-
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LIST OF TABLES
Table I: Descriptive statistics of the main variables - 22 -
Table 2: Correlational matrix - 24 -
Table 3: Loans and advances regression table - 26 -
Table 4: Non performing loans regression table - 27 -
, , , ,
Table 5: Net charge off regression table - 28 -
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CHAPTER ONE
1 INTRODUCTION
1.1 Ba ckground
It is in the wake of the 2008 financial crisis that the link between executive remuneration and
credit risk management became more visible. A survey by KPMG in 2009 found that 52% of
senior managers at large financial institutions believed that incentives and remuneration
contributed the most to the credit crisis that was the mark of the financial crisis of 2007-2008
(KPMG, 2009).
Banks like other firms have managers that run the day to day activities of the company on behalf
of its owners. This makes the managers agents of the shareholders. The relationship of agency is
one of the oldest and commonest codified modes of social interaction. (Ross S. , 1973).
Jensen and Meckling describe the agency relationship as a contract under which the principal
engages the agent to perform some service on their behalf (Jensen & Meckling, 1976). They
noted that the agent does not always work to maximize the welfare of the owners. It is difficult
and very expensive for the principal to monitor what the agent is doing. Eisenhardt later came up
with a solution to solve the problem on control-the principal could reward the agent based on
outcomes such as profitability (Eisenhardt, 1985). In comes executive remuneration where
executive pay was structured initially to create value for owners by enhancing credit risk taking
in managers. (Shapiro, Mehran, & Morrison, 2011).
Due to the nature of the business of the banking industry, Banks in Kenya face a myriad of risks
in their operations more than other companies in the country. They face significant liquidity risk
as they need to offer their customers instant access to their deposits-they should be able to
withstand the pressure from having many customers asking for their money back at the same
time . They also face interest rate risks as their profit margin is largely dependent on the
movement of interest rates in the country. Additionally, they face significant exchange rate risk
as a significant number of banks are multinational corporations hence exposing themselves to
exchange rate movements that may adversely affect them especially with the weakening shilling.
The banks also face business risk due to the nature of their business and operational risk that has
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damaging effects should any operational processes fail. Credit risk is one among the many risks
that banks face.
Credit risk is the oldest form of risk in financial markets that dates back as far as 1800 BC
(Caouette, Altman, & Narayanan, 1998).Credit risk is the risk of an economic loss from a failure
of a counterparty(the borrower in the case of a bank) to fulfill its contractual obligations (Jorion,
2003) . Provision of credit remains the primary business of every bank in the world. Ineffective
credit management is a major cause of serious banking problems. Credit quality is for this reason
. . . .
considered to be a primary indicator of financial soundness and health of banks. (Boahene,
Dasah, & Agyei, 2012) .
Due to the severe consequences of improper credit risk management Central bankers from
around the globe came up with a set of guidelines that would govern credit risk management in
banks by setting up minimal capital requirements. Capital in banks is used to shield the bank
from credit risk as it offers a buffer against defaults in loans .
Management of credit risk has been complicated by the existence of information asymmetry
between the borrower and the bank. The presence of information asymmetry leads to adverse
selection where potential bad credit risks are the ones who actively seek out the loans (Mishkin,
2010). This increases the bank's problem as it has to do rigorous screening in order to determine
which borrower to give credit and which borrower not to. Effective credit management is what
determines which bank will be successful in coping with adverse selection and which bank will
make losses.
The evolution of the financial markets has led to the creation of more advanced financial
instruments all in a bid to customize the needs of participants in the market. Banks have not been
left behind with regard to credit risk . Asset backed securities such as the Collateralized Debt
Obligation which are securities backed by a diversified pool of corporate bonds and loans have
enabled banks to repackage their debt obligations and transfer risk to other investors (Jorion,
2003) . While these have been great in the management of credit risk, mismanagement of the
financial instruments can have catastrophic events . A combination of mismanagement of CDOs
and adverse selection was one of the causes of the financial crisis.
Kenyan banking system was however poised to withstand the crisis. What we seek to find out is
what role executive remuneration plays with regards to credit risk and credit risk management in
banks listed on the Nairobi Securities Exchange.
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1.2 Problem Sta tement
Numerous studies have been carried out trying to link the relationship between executive pay and
firm performance globally and even in Kenya (Aduda, 2011), (Kipkorir, Aboko, & Bitange,
2014), (Miyienda, Ososro, & Miyogo, 2013). However, the influence that executive
compensation has on the attitude of managers towards risk is an area one worth exploring
further.
Unlike shareholders, managers are generally risk averse . Shareholders are able to diversify their
investments and as such they are risk seeking as they try to maximize their return . Managers on
the other hand cannot afford the luxury of taking up excessive risk as they are unable to diversify
their pay. This makes them risk averse creating conflict of interest between their wishes and
those of risk seeking shareholders (Eisenhardt, 1989)
To eliminate this conflict of interest, it has been observed that executive remuneration can be a
tool that shareholders use to encourage risk taking in managers (Diamond & Rajan, 2009)
(Shapiro, Mehran, & Morrison, 2011).This is done by linking executive remuneration to
company performance. This therefore means that for managers to increase their pay, they need to
increase company returns. Increasing company returns may require managers to take up more
credit risk than before. This has however made managers blinded by the idea of increased pay so
that they are unable to see clearly how much credit risk they are undertaking all in a bid to
increase company returns. Managers have been seen to relax credit risk management procedures
in banks as they try to attract more borrowers so as to earn higher interest income, improve short
term company performance which consequently translates to more pay. This was best observed
during the 2007-2008 financial crisis that saw managers of banks who were earning high fees not
bother to do a thorough credit analysis on the potential borrowers as they were blinded by the
high returns that translated to higher remuneration for them (Mishkin, 20 I0).
However, Ross (2004) disagrees with this and says that there is no incentive schedule that will
make agents more or less risk averse. Holmstrom (1979) too is of this view and goes ahead to
say that the notion that risk preference alignment between shareholders and managers is possible
via the use of incentives is impossible.
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The study explores this in the Kenyan context and tries to find out the relationship that exists
between the measures of credit risk and executive remuneration and therefore the impact of
executive remuneration on credit risk in banks listed on the Nairobi Securities Exchange.
1.3 Resea rch O bject ives
1. To establish relationship between credit risk measures and executive remuneration of
managers of listed banks in Kenya.
2. To analyze the impact of executive remuneration on credit risk in listed banks in Kenya.
1,4 Research Q uestions
1. What is the relationship between credit risk measures and executive remuneration of
managers of listed banks in Kenya?
2. What is the impact of executive remuneration on credit risk in listed banks in Kenya?
1.5 Significance of the Study
The first beneficiaries of this research are the owners of banks listed on the Nairobi Securities
Exchange. This is because they are the ones that determine the remuneration for managers
through the remuneration committee. As such they will be able to know to know what role the
level of remuneration they pay plays with regards to credit risk in their banks. As such they will
be able to adjust this appropriately in such a way as to optimize the level of risk that is
appropriate for their firms.
The Government of Kenya benefits from this study as they partially own many of the banks
listed on the Nairobi Securities Exchange through purchasing of a number of ordinary shares.
They do this because they realize that the banking industry is very sensitive and as such requires
close monitoring by the government. They would therefore be able to determine to what extent
remuneration affects credit risk in banks and use this ensure that banks maintain a healthy
amount of credit risk . The government is in charge of monetary policies and they are the ones
responsible for the smooth running of the financial system whose main players are banks and
their main challenge being credit risk. Good credit management translates to well adjusted banks
and subsequently a healthy financial market and a healthy economy at the end of the day.
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CHAPTER TWO
2. LITEHATURE REVIEW
2. 1 T he Ag en cy P ro b lem
Shareholders and executives have different attitudes with regards to risk. Shareholders are seen
to be risk seeking while executives are risk averse. The difference is brought about by the fact
that investors are able to diversify their risk by taking in diversified investments. To maximize
their return , shareholders will take up risky investments as the greater the risk, the greater the
return (Sharpe, 1970).
However, managers are not able to diversify their remuneration and as such cannot afford the
luxury of taking up excess risk in a bid to earn higher returns. Due to the fact that managers are
excluded from effectively diversifying employment and personal wealth risk , executives are said
to be risk averse (Jensen & Meckling, 1976)
Eisenhardt (1989) is also of the opinion that managers should be risk averse too. The inability of
managers to take up investments and diversify their risk is the greatest contributor towards their
risk aversion. (Eisenhardt, Agency Theory: An Assessment and Review, 1989)
2 .2 Th e Executive Remunera ti on Solut ion
Ideally, executive compensation should be an instrument for combating the agency problem
between managers and shareholders. This is the optimal contracting approach (Walker, Bebchuk,
& Fried, 2002)
Shapiro, Mehran, & Morrison (2011) in their paper state that conventional wisdom holds that
executive pay structure was designed to promote risk taking and create value for shareholders.
However, they also add that this does not protect debt-holders.
In light of the above view , Bolton, Scheinkman, & Xiong ( 2006) have come up with a theory
that claims that shareholders have a short-termist view with regard to their investments. And as
such, they try to make managers take up a similar view by designing managerial compensation in
such a way as to induce CEOs to exploit future investors. This is so much so that the more
speculative the market, the more the shareholders align managerial remuneration to the
company's performance in that market so as to encourage managers to take up more risk.
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An issue comes up where most companies are owned by institutions and as such one may be led
to think that institutional shareholders have a long term view and not a short tenn view as they
claim in their theory. They disprove this assumption by citing a survey that was carried out
where they noticed that companies owned by many institutional shareholders were observed to
reduce their research and development costs and use this money to pump up their earnings.
Therefore both individual investors and institutional investors have a short term view to their
investments and wish to maximize their returns at the earliest opportunity (Bolton, Scheinkman,
& Xiong, 2006) .
The Department of Treasury agrees with them and state that compensation design unintentionally
encourage excessive risk taking, providing incentives that ultimately put the company in danger.
They advise that compensation committees should conduct and publish risk assessments of pay
packages to ensure that they do not encourage prudent risk taking.
The Department of Treasury also look at it from a different perspective where they say that some
of the decisions that contributed to the financial crisis happened when people were able to earn
immediate gains without their compensation reflecting the long term risks they were taking for
the shareholders. It made the managers short-termist. (Geithner, 2009)
Bebchuk & Spamann (2010) agree that executive pay is an incentive for managers to take up
excessive risk. One factor that induced excessive risk taking in banks is that firms' standard
payment arrangements reward executives for short term gains even when these gains are
subsequently reversed. Another reason that they give that gave managers motivation to take up
excessive risk was that these executives were not exposed to the potential negative effects that
large losses from excessive credit risk could have caused the shareholders. Such like
compensation agreements made them give insufficient weight to risks of large losses. They also
say that it is not sufficient to tie executive payments to long term results in order to curb
excessive risk taking but should look at monitoring and regulating compensation structures
instead (Bebchuk & Spamann, Regulating Bankers' Pay, 20 I0)
Diamond & Rajan (2009) address the banking industry in specific. They agree that compensation
does encourage risk taking among managers in banks. Due to competition among the banking
industry, managers are paid generously based on performance. Many of the compensation
schemes are based on short term performance. This gives traders an incentive to take risks not
recognized by the system so that the owners view them as having superior abilities and hence
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pay them more. The relationship between excessive risk taking and executive remuneration for
that reason is greater in the banking industry in comparison to other industries not in the financial
sector.
Culp (2000) links the agency problem to the risk attitude of investors. When monitoring is
costly, the manager will not have the incentive to maximize the shareholder's return. The
expected utility of a manager's wealth is a convex function of the firm. This will in tum
determine the degree to which the manager will fully hedge the firm or simply walk away from
risky projects.
Another paper that expounds this further is Coles, Daniel, & Naveen (2006) . This paper says that
managers are generally risk averse. To mitigate the effect of CEO risk aversion, then the
company should consider giving convex payoffs to them to mitigate the effect of CEO risk
aversion. However this will only work depending on the managerial utility function . Convexity
of the payoff structure can be more than offset by concavity of the utility function of the risk
averse manager
Santomero, (1997) is another paper that links the agency problem to risk management is
In his paper, he says that well designed incentive systems align the goals of managers with other
stakeholders in a most desirable way. Executive compensation when utilized properly can be
used as a risk management tool in itself. The absence of incentive compatibility according to him
has caused the most financial debacles. Aligning incentives to risks is less costly than putting up
other controls for risk management. The only problem with this he says is that these
compensation schemes have to be thoroughly evaluated to determine if they are accurate in
meeting the intended goal.
Sundaram & Yermack (2007) have a different approach and to them, the structure of the
compensation is what determines the risk attitude of the manager. When top executives receive
part of their compensation in debt and part in equity then they are expected to manage the firm in
such a way that considers the interests of both the debt and equity investors. Classical agency
problems related to risk shifting and excessive payouts should diminish in importance when
managers hold large pensions or deferred compensation.
Wiseman & Gomez-Mejia (1999) introduce a new term-risk bearing which refers to the
perceived risk to agent wealth that can result from employment risk or taking other thr eats to the
agent's wealth. Risk bearing irrcreases risk aversion by aggravating the overinvestment problem
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faced by managers. This means that when forecasts of firm performance are satisfactory then
executives expect a gain in their remuneration and consequently act conservatively with regards
to risk. When executive performance is insulated from finn performance, no incentive exists to
accept risk and executives should exhibit risk aversion
Edmans & Gabaix (2011) have a completely different view from the ones above. They feel that
the reaction to increased CEO compensation could go either way. If the CEO affects firm value
mainly by consuming perks then increasing his remuneration has little effect on risk and so
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incentives are weaker but if the CEO creates value by choosing risky projects, incentives are
stronger.
Holmstrom (1979) is of a complete contrarian view and in his paper he goes ahead to say that no
amount of incentives would make executives risk neutral with regards to the shareholders.
Therefore the notion that risk preference alignment between shareholders and managers is
possible via the use of incentives is impossible. The only thing that incentives can do is reduce
the extent of misalignment but complete alignment between managers and shareholders can
never be achieved
Another paper that holds a similar view to the one above is Ross (2004) . In his paper he says
that there is no incentive schedule that will make agents more or less risk averse. To make
managers more risk averse, there are some conditions that are necessary and have to be met.
2 .3 Emp irica l Stu d ies O il Ex ecu ti v e Remuneratio n and Credit Risk
The willingness of managers to take up on risk depends on two things; the rewards for risk
taking and the costs management has to bear for any poor performance that results from these
risky activities. This means that the lower the manager turnover due to poor performance, the
more the incentive to take up risk.
An empirical study carried out using the largest US banking firms between 1980 and 1981 found
out that salaries paid to bank managers was less than those paid to non-banking finns. The lower
cash compensation could be explained by lower marginal productivity of managers in the
banking sector relative to other industries. They also found out that the marginal product of
executives is lower in the banking industry than in other industries due to regulation n the
banking industry that reduces the managers ' investment discretion. They claim that the lower
cash compensation is inconsistent with the moral hazard hypothesis. However, their results are
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consistent with the contracting hypothesis which predicts that firms with greater growth
opportunities will pay higher salaries and rely heavily on incentive compensation. They find out
that there is a highly significant and positive relationship between changes in stakeholder wealth
and executive compensation. They also found out that the cash compensation of bank executives
is more sensitive to stock market performance than firm performance. In the too big to fail banks,
the pay performance relationship there was weaker than other banks showing that compensation
may not be designed to promote risk taking . In conclusion, there is little evidence that
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compensation policies in banking are designed to encourage excessive risk taking. The moral
hazard problem in banks may not be that severe to the extent that managers are not provided with
incentives to engage in risky activities such as low manager turnover sue to poor performance.
Also, factors influencing compensation and turnover policies are similar to those influencing
such policies in other industries. Due to these results it therefore means that any efforts made by
regulators to control compensation in the banking industry to curb risk are likely to be
ineffective. (Houston & James , 1995)
Bloom & Milkovich (1995) carried out an empirical study where they found out that higher
levels of risk and higher variability in pay is associated with lower firm performance. Their
results are the direct opposite of their agency-based predictions. They had anticipated that higher
levels of business risk would be compensated with higher pay but this was not the case. They
found out instead that higher risk is associated with lower pay . They found out that generally
high risk firms which rely more heavily on incentive pay are more likely to experience poor
performance.
An empirical study carried out in the paper by Cheng, Hong , & Scheinkman (2010) shows' that
there is a positive relationship between residual compensation and risk. The relationship between
pay and risk are related to features of the optimal contract and investor demands. There is little
relationship between incentives and risk and probably what explains the relationship is that total
pay levels increase in the level of risk because agents must be given a large incentive to join high
risk firms. Any effort for regulation of pay should begin with an analysis of the wedge between
the interests of the finn and the taxpayer who will bear the loss from too-big-to-fail firms rather
than the wedge between shareholders and management.
Bolton, Mehran , & Shapiro (2010) carry out a study that leads them to believe that compensation
does indeed influence the risk attitude of the managers. They use a different approach where they
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look at ways to reduce a manager's love for excessive risk taking. They say that just as much as
linking compensation to stock price returns helps motivate managers to take up risk, debt based
compensation curbs the risk loving nature of managers. The market believes that debt like
compensation reduces risk taking. Credit Default Swap (CDS) based compensation is more
suitable as it is cheaper and easier to implement than other debt like instruments. Optimal risk
taking incentives will not be implemented by shareholders because they suffer from a
commitment problem that is exacerbated by either the renegotiation of compensation contracts,
deposit insurance or naive debt holders
The empirical study carried out in the paper by Tung (2011 )concurs with the one above. They
discover that paying bankers with debt may curb their appetite for risk, consistent with the
regulator's goal of assuring bank safety and soundness. However, for them, publicly traded
subordinate debt may be an ideal form of debt compensation for bankers because market pricing
of this debt will offer a continuing referendum on risk taking at the bank as it is more sensitive to
downward risk than equity is. This is a more reliable and direct inducement for banks to curb
excessive risk taking. This mode of remuneration aligns the managers' interests more closely
with relatively risk averse debt holders. It further aligns the regulators' interest in assuring bank
safety and soundness to that of the managers. This goes ahead to confirm empirically the
proposition by Sundaram & Yermack (2007) where they talk about the structure of executive
compensation being the important factor in determining the risk attitude of the manager.
The empirical study by Chen, Steiner, & Whyte (2006) also emphasizes the importance of the
structure of executive remuneration vis-a-vis risk attitudes of managers. They focus on option
based remuneration and discover that there indeed is a relationship between executive
remuneration and risk taking in managers. CEO's option based compensation induces risk taking
and that there is some evidence that it also enhances shareholder wealth thus aligning the
interests of shareholders and managers.
Another empirical research that emphasizes the importance of executive compensation structure
is the one by Balachandran, Kogut, & Hamal (2011). A research carried out after the financial
crisis found out that financial institutions led by executives whose remuneration was heavily
weighted in equity are more likely to be marked by extreme risk taking. The problem was that
many models of incentives and risk did not scale well to executive compensation. This reinforces
further the proposition brought forward in the paper by Santomero (1997) about the execution of
15
the remuneration being important. If compensation is to incentivize managers, then the large
incentive packages of CEOs of financial institutions conform to the belief that top managers
experience declines in marginal utility in income, requiring even greater incentives with
increasing level of income.
Balachandran, Kogut, & Hamal (2011) recommend that the most suitable way to design
compensation contracts would be through the use of debt and equity compensation so as to
encourage risk taking but also limit it to a certain extent by the debt compensation. Empirically
. . . .
the paper by Demestz, Saidenberg, & Strahan (1997) finds that the level of risk aversion in
managers is dependent on the tendency toward performance based compensation. This goes
ahead to confirm empirically the propagation in the paper by Coles, Daniel, & Naveen (2006) .
The lower the observable tendency toward performance based compensation then the higher the
risk aversion . They discover that there is an inverse relationship between the two.
Hall & Liebman (1998) say that one of the solutions to the agency problem is solved by a one-to-
one correspondence between firm value and CEO pay. This contract essentially sells the finn to
the CEO. This may not be reasonable for firms because firm performance is subject to great
variations whose standard deviations they found to be 32% or about $700million. Making the
CEO accept such variations is very costly and hence the CEO becomes risk averse and will not
take up any risky projects that will cause variability in returns
Hauswald & Senbet (2009). Carry out an empirical study that includes a different aspect to
compensation. They look at the timing of executive compensation as one of the factors that affect
the effectiveness of compensation as a tool for shareholders to use to align risk preferences.
. Shareholders should improve on contracting outcomes by conditioning their response on ex-ante
compensation schemes. They also incorporate bank regulation and say that the need for explicit
managerial incentives to induce excessively risky lending reveals information about the balance-
sheet quality which allows society to design collusion proof comprehensive regulatory schemes.
Another empirical paper that looks at executive compensation but also adds bank regulation into
their study is Huttenbrink, Kaserer, & Rapp (2014) .While there is a good outcome from bank
regulation found evidence that shareholders aim to jeopardize regulation by designing executive
remuneration policies with stronger emphasis on performance oriented pay structures that
incentivize managers to outperform competitors in a restricted business. This shows that
regulation seems to have little positive outcome with regards to curbing risk taking in banks.
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Shareholders turn to performance based remuneration to counter bank regulation that tries to
curb managers taking excessive risks.
Hilscher, Landskroner, & Raviv (2014)Find that an increase in executive ownership is a
necessary but not a sufficient condition for an increase in a financial institution's asset risk. If the
regulatory limit on asset risk is above the level which maximizes the public position then the
executive would choose that level. Executive ownership is greater than in the case where the
regulatory upper bound on asset risk is equal to the level which maximizes the public position.
, , ,
This paper empirically supports the view by Edmans & Gabaix (2011).
An empirical study carried out that has a contrarian view to the ones expressed above is the one
by Fahlenbrach & Stulz (2010). They discover that there is no evidence that banks with CEOs
whose incentives were not aligned to the interests of the shareholders performed worse during
the financial crisis. Banks where CEOs had better incentives performed significantly worse than
banks where CEOs had poorer incentives. Cash bonuses did not have an adverse effect on bank
performance during the crisis . If CEOs took up risks that were not in line with the wishes of the
shareholders then they would have sold their shares which they did not. They too suffered huge
losses as a result of the crisis. This empirical study supports the contrarian propagations made in
the papers by Holmstrom (1979) and Ross (2004).
2.4 C red it Analys is M easu rcs
Credit risk is traditionally measured as the expected loss on an asset. Expected loss in this case is
a function of three variables: the probability that the counterparty will default before the asset
matures, the potential exposure of the creditor to a default and the loss that a creditor incurs in
the event of a default. Mathematically, this can be expressed by
EL=DR x LIED x PCE
Where DR is the average default rate, LIED is the expected loss on the asset in the event of
default and PCE the expected potential credit exposure of the asset. (Culp, 2000) . However,
measuring the inputs to the formula may require some subjective aspects that may not make it an
unbiased measure of credit risk.
Credit risk could also be measured using the CAMEL rating system (Ogilo , 2012) . This is an
acronym that stands for Capital adequacy, Asset quality, Management quality, Earnings and
Liquidity. Federal regulators in the USA developed this rating system to help structure a bank's
17
examination process to measure the bank's overall financial health (Wirnkar & Tanko).
However, it measures overall soundness of a bank but does not address credit risk in particular.
The variables of interest here are executive remuneration and credit risk. The theory
underpinning this study is the agency theory as propagated by Jensen and Meckling (Jensen &
Meckling, 1976). The relationship between the variables is that executive remuneration has been
seen to be a tool that shareholders use to control credit risk. I would therefore like to examine if
this relationship holds in the Kenyan context.
2.5 The Kn ow ledge Ga p
So far in Kenya there have been a number of empirical studies carried out trying to link the
relationship between executive pay and firm performance (Aduda, 2011), (Kipkorir, Aboko, &
Bitange, 2014), (Miyienda, Ososro, & Miyogo, 2013). In light of all the literature from all over
. . . .
the world discussed thoroughly in the literature review above, the study of the relationship and
the influence that executive compensation has on the attitude of managers towards risk and
specifically in the Kenyan context, is an area worth exploring further.
There has not been much literature on this subject in the Kenyan context and an exploratory
study on the same would enable shareholders know how much power they have over credit risk
in banks via the use of executive remuneration.
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2 .6 Co n cep t u a l Pr amewo r k
Dependent Variables
~ Measures of Credit Risk
• Non-performing loans
• Total loans and
advances
• Net charge off
-=::::::~====
Independent Variables
>- Executive Remuneration
~ Control Variables
• Size
• Capital adequacy
• Bank performance
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CHAPTER THREE
3. METHODOLOGY
3.1 Research Design
This study adopts a correlational research design. This is because the study is examining the
relationship between executive compensation and credit risk in banks listed on the Nairobi
Securities Exchange. In this correlational research design, the research approach that is used is a
deductive one as it seeks to explain the causal relationship between executive remuneration and
credit risk. It also employs the use of quantitative data. The emphasis is on selecting a suitable
sample size and making a general conclusion on the nature of the Kenyan banking system with
regard to the relationship between credit risk and executive remuneration.
To answer the research questions posed in the first chapter, the study uses explanatory studies
whereby it tries to establish a causal relationship between the credit risk determinants and
executive remuneration.
The correlational research described in detail above helps in determining the relationship that
exists between executive remuneration and credit risk by determining whether the coefficient is
positive or negative. The second research question is answered by determining the probability
value which enables the strength of the relationship between these two main variables to be
determined.
3.2 P op ula t io n and Sa m p le
The target sample comprises the 11 commercial banks listed on the Nairobi Securities Exchange.
These include; Barclays bank Ltd, CFC Stanbic Holdings Ltd, I&M Holdings Ltd, Diamond
Trust Bank Kenya Ltd, Housing Finance Co Ltd, Kenya Commercial Bank Ltd, National Bank
of Kenya Ltd, NIC Bank Ltd, Standard Chartered bank Ltd, Equity Bank Ltd and The Co-
operative Bank of Kenya Ltd (Nairobi Securities Exchange, 2015).
The sample banks chosen are both from the Tier I and Tier II peer rankings that whereby Tier 1
banks have a balance sheet of more than ksh ISO billion and Tier II have a balance sheet of less
than ksh 150 billion but more than ksh 50 billion. Due to the small number of banks in the
Kenyan market and the large balance sheets that the top and mid-tier banks hold, this sample of
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11 banks is representative of the population of 42 banks in the Kenyan banking industry and the
banking sector as a whole.
3 .3 Data C oll ect io n Methods
The study employs the use of secondary panel data obtained from the published financial
statements of the banks listed above for the years 2008-2014.
3 .4 Data Analysis
Credit risk here is a function of executive remuneration. To conclusively show the relationship
between executive remuneration and credit risk, three measures of credit risk are used . The
measures of credit risk in the analysis include; net charge off to total loans and advances, non-
performing loans to total loans and, loans and advances to total deposits (Olawale,
2012),(Boahene, Dasah, & Agyei , 2012) (Ganic, 2014).
As a control variable, the study uses Return on Assets given that the probability of paying out
bonuses largely depends on the profitability of the bank (Zori, 2010).
Capital adequacy which is measured by the bank's core capital expressed as a percentage of its
risk weighted assets is used as another control (Mai & Jaeger, 2011).
Differences in bank size is controlled by adding a variable that measures size as the total assets
of the bank .
To analyze this data, a panel data regression technique is used. The justification for this is that
the study uses data that spans across time and individuals and hence a panel data regression
technique will be the most suitable to capture the dynamic nature of this data.
Two panel data techniques are applicable in this case- the Fixed Effects Model (FEM) and the
Random Effects Model (REM). In FEM, the intercept in the regression model is allowed to differ
among individuals in recognition that each bank may have some special characteristics of its
own . It is appropriate when the bank specific intercept may be correlated with one or more
regressors.
In REM it is assumed that the intercept of an individual unit is a random drawing from a much
larger population with a constant mean value. This is mainly used when the intercept of each
cross sectional unit is uncorrelated with the regressors.
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To be able to determine which technique is suitable to use , A Haussman test is carri ed out. To
carry out a Haussman test , there is a null hypothesis stating that the FEM and REM estimators do
not differ substantially. If the null hypothesis is rejected then this means that the REM is not
appropriate because the random effects are probably correlated with on or more regressors.
To CatTY out the Haussman test and regression analysis, the study employs the use of a general-
purpose statistical software package which in this case will be Stata developed by StataCorp.
. .
The models used for the FEM and REM are;
Where
i=(number ofbanks)I,2 11
t=(number of years) 1, 2 5
VARIABLE DEFINITION
LA Credit Risk=Loans and Advances Total loans and advances
Total deposits
NPL . . . Non Performing LoansCredit Risk-Non performmg loans
Total loans and advances
NCO C d' R' k N Ch Off Net Charge Offre It IS = et arge
Total loans and advances
ER Executive Remuneration (i.e. salaries and short term employee benefits)
ROA Return on Assets Net income
Total assets
CA C . I Ad Bank capitalapita equacy=
Risk weighted assets
Size Bank size=Total Assets
£ Error term
A Haussman test is carried out to determine which of the two models is the most appropriate to
use .
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After determination of the correct model, the appropriate model is ran again and the results
stored.
This analysis is done thrice with each of the three determinants of credit risk above i.e loans and
advances, non-performing loans and net charge off.
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CHAPTER FOUR
4. DATA ANALYSIS AND FINDINGS
4.1 Descr ip t ive sta tis ti cs
Table 1: Descriptive statistics of the main variables
Variable Obs Mean . Std. Dev Min Max'
Loans and 77 0.69822 0.1443352 0.2452861 1.098478
advances
Non 77 0.0497095 0.0451209 0.0013455 0.27032207
performing
loans
Net charge off 77 0.0114166 0.00757522 0.0002528 0.0404469
Executive 77 160.2338 295.4569 14 2403
remuneration
Size 77 137608.7 87464.95 14294 490338
Return on 77 0.0428899 0.0151248 0.0136947 0.0735768
assets
Capital 77 22.39274 7.906101 12.62182 48.7
adequacy
Table 1 above gives information about the descriptive statistics of the dependent variables,
independent variable and control variables. On average, loans and advances take up almost 70%
of the total bank deposits over the seven years . This shows that most of the money the bank
receives is used to issue loans which is in line with their role of credit transformation, However,
out of these loans issued, close to 5% of them are non performing. This is depicted in the table
where the average of non performing loans to total loans and deposits comes to 4.97%. net
charge off is a lower percentage as only 1.14% loans and advances on average end up being
uncollectable at the end of the day.
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The risk as shown in the standard deviations of these three dependent variables is greatest in the
number of loans and advances issues with a standard deviation of 14.43%. This is seen to reduce
with the non performing loans having a standard deviation of 4.51%. This means that there is
inherently more risk involved in the number of loans and advances a bank issues in comparison
to the number of loans that are non performing within the seven years. This is a good sign as the
lower risk in non performing loans shows that the banks' credit risk management practices put in
place are quite effective. The standard deviation of net charge off as a proportion of total loans
. . . .
and advances is even lower at 0.76% . This means that banks are able to handle debts that go bad
by ensuring proper screening of customers before issuing loans. It also shows that they are bale
to collect most of the debt that goes bad from the collateral given emphasizing the strength of
their risk management policies.
The average executive remuneration is approximately ksh 160 million with a standard deviation
of ksh 295 million. There is a huge discrepancy where the lowest value is ksh 14 million and the
highest ksh 2.4 billion. This shows that executive remuneration is a highly volatile variable. The
high volatility is despite the sample comprising top tier and middle tier banks which makes it
relatively homogenous. The volatility could be brought about by changing economic and
banking sector conditions in the last seven years.
The average size of the banks measured by the total assets is ksh 137 billion. This shows that the
banks listed have a wide asset base . The standard deviation is much smaller that the average
unlike was the case in executive remuneration. This goes ahead to reinforce the complexity that
is in the huge variance in executive remuneration that is not in tandem with the size if the banks
which is relatively homogenous. This paradox is probably explainable by factors outside the
control of the bank.
The return on assets as expressed by the ratio of net income to total assets is 4.3% . the standard
deviation is 1.5%. It is interesting to note that this is much higher than the volatility of the net
charge off. This means that there is more risk in the income of the banks on average than there is
in the number of loans that are unrecoverable. This volatility in the net income of banks should
be a source of worry for the financial system.
The average capital adequacy comes to 22% with a standard deviation of 7.9%. this shows that
banks are generally holding enough capital as a buffer from the risky assets that they have. This
is probably due to the strict regulations that banks face as a result of the Basel Accords that
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require them to hold sufficient capital as a buffer for their risky assets . This brings out the issue
of regulation being an important factor with regards to bank risk. Regulation has enabled banks
reduce their credit risk as shown by the capital adequacy average in table 1 below. This is in
contrast to the high volatility seen earlier on in the net income in comparison to the net charge
off. This shows that banks, if left on their own to manage credit risk with no regulation would
take up more risk in their balance sheets than they should. This reinforces the importance of
banking regulation in the maintenance of a sound and healthy financial system whose main
players are banks.
4- .2 Corr elation analys is
The Pearson's coefficient was used to verify the extent of correlation among the independent
variables. The results are shown in table 2 below. The links between these variables is low
showing little evidence of multi-collinearity, This means that we can safely incorporate these
variables in the regression analysis.
Table 2: Correlational matrix
CA ROA SIZE ER
CA 1.0000
ROA 0.0031 1.0000
SIZE -0.2092 0.4444 1.0000
ER 0.1404 0.4214 0.3029 1.0000
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4 .3 Regr ession ana ly s is
Three measures of credit risk were used to determine the relationship between executive
remuneration and credit risk.
A combination of fixed effects model and random effects model was used on the three
determinants depending on the results of the Haussmann test carried out that determined which
of the two models was to be used.
4.3.1 Loa ns and Ad va nces
The regression equation used is
LAil=P I i+ERitP2+ROAitp3+Sizeitp4+ CAitpS+Cit
The coefficients pli, P2, P3, P4 and ps were used to measure the sensitivity of the loans and
advances to changes in executive remuneration and the three control variables.
Table 3 below shows the results of the regression analysis. Following the Hausman test, the
random effects model was determined to be the more appropriate model to use.
The results from table 3 below show a positive relationship between executive remuneration and
the amount of loans and advances issued by listed banks in Kenya.
The strength of this positive relationship is however not significant as shown by the probability
value of 32.1% that is higher than 5%.
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Table 3: Loans and advances regression table
Fixed effects model Random effects model
Variable Coefficient Probability Variable Coefficient Probability
ER 0.0000416 0.357 ER 0.0000428 0.321
SIZE 3.32xlOe-70.078 SIZE 2.94xl0e-7 0.097
ROA -1.13054 0.0377- ROA - -1.099411 0:391
CA -0.0009661 0.59 CA 0.0008342 0.622
CONS 0.6769668 0.000 CONS 0.6793763 0.000
R-squared 0.0265 R-squared 0.0238
Prob (F-statistic) 0.3176 Prob (Chi-square) 0.3457
Wald Chi-square 4.47
Hausman Test
Chi-square 0.07
Prob (Chi-square) 0.9658
4 .3 .2 No n Performing Loans
The regression equation used for this section was
NPLit=~ Ii+ERit~2+ROAit~3+Sizeit~4+CAit~5+£it
The coefficients ~I i , ~2, ~3 , P4 and ~5 were used to measure the sensitivity of the non performing
loans to changes in executive remuneration and the three control variables.
Table 4 below shows the results of the regression analysis. Following the Hausman test, the
random effects model was determined to be the more appropriate model to use.
The results from table 4 below show a positive relationship between executive remuneration and
the amount of non performing loans in listed banks in Kenya .
The strength of this positive relationship is however very weak as shown by the probability value
of 86% that is higher than the 5% threshold.
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Table 4: Non performing loans regression table
Fixed effects model Random effects model
Variable Coefficient Probability Variable Coefficient Probability
ER 3.89xlOe·6 0.868 ER -3.2xlOe·6 0.86
SIZE 9.31x I Oe·8O.337 SIZE 1.38x 1Oe-7 0.030
ROA -1.7703020.016 ROA -1.171725 0.002
CA -0.0013626 0.146 CA 0.0020409 0.001
CONS 0.08169670.006 CONS 0.035744 0.091
R-squared 0.3662 R-squared 0.5825
Prob (F-statistic) 0.1891 Prob (Chi-square) 0.0006
Wald Chi-square 19.59
Hausman Test
Chi-square 0.07
Prob (Chi-square) 0.9658
4 .3 .3 Net Ch ar ge Off
The regression equation used here was;
The coefficients ~I i, ~2, ~3, ~4 and ~5 were used to measure the sensitivity of net charge off to
changes in executive remuneration and the three control variables.
Table 5 below shows the results of the regression analysis. Following the Hausman test, the
random effects model was determined to be the more appropriate model to use.
The results from table 5 below show a positive relationship between executive remuneration and
the amount of loans and advances issued by listed banks in Kenya.
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The strength of this positive relations hip is however not significant as shown by the probability
value of 93.6% that is higher significantly than 5%.
Table 5: Net charge off regression table.
Fixed effects model Random effects model
Variable Coefficient Probability Variable Coefficient Probability
ER -2.18x1Oe-6 0.527 ER -2.55x 1Oe-? 0.936
SIZE 8.27xlOe-1O 0.951 SIZE 1.01xl0e-8 0.424
ROA -1.491032 0.163 ROA -1.1518996 0.065
CA 0.0000426 0.756 CA 0.0001328 0.261
CONS 0.0170863 0.000 CONS 0.6793763 0.000
R-squared 0.0097 R-squared 0.1602
Prob (F-statistic) 0.4898 Prob (Chi-square) 0.4086
Wald Chi-square 3.98
Hausman Test
Chi-square 4.73
Prob (Chi-square) 0.0938
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CHAPTER 5
5.DISCUSSION
5. 1 The relatio ns hi p betwe e n cr e d it r isk measu r e s a n d execu t ive
r emun e r ation of managers of li s ted ban ks in Ke n ya .
The results from table 3 above show a positive relationship between executive remuneration and
the amount of loans and advances issued by listed banks in Kenya.
The positive relationship between executive remuneration and loans and advances issued is in
line with (Mishkin, 2010) who studied this relationship after the financial 2008 financial crisis .
They observed that the more the executives received in pay, the more loans they were willing to
give out. This was all in a bid to make more money for the bank in the short run which would
translate to increased bonuses for them. These decisions were beneficial in the short run but the
probability of default in the future by these uncreditworthy individuals posed a great risk for
banks. This greed was one of the many reasons that brought about the collapse of the financial
system in 2008.
The results from table 4 above on the non performing loans and advances show a negative
relationship between executive remuneration and non performing loans as a proportion of total
loans and advances. This is in line with the classical agency- principal relationship pioneered by
Jensen & Meckling (1976) (Jensen & Meckling, 1976). Traditionally shareholders would prefer
to invest in a bank where the non performing loans are a small percentage of the loans and
advances issued. In this case, this negative relationship between the two signifies that the more
the managers are paid , the less risky loans they take. This would ultimately mean that
shareholders would have been successful in using executive remuneration to curb the risk taking
nature of bank managers.
The results from table 5 above on the net charge off equation show a negative relationship
between executive remuneration and net charge off. This is in line with the classical relationship
described earlier in explaining the relationship between non performing loans and executive
relationship. This is quite in order as net charge off is as a result of non performing loans that
become uncollectable later on.
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2 .0 The im p a ct of executi ve remune r a ti o n on cred it risk in listed
bank s in Ke n ya .
The results from the regression table 3 above on loans and advances indicate a non significant
relationship between loans and advances and executive remuneration.
Executive remuneration has little explanatory power on the loans and advances indicated by the
probability value of 32.1% which is greater than the benchmark of 5%. This means that though
the relationship is positive, it is not strong enough to conclusively link executive remuneration to
the number of loans and advances issued by banks.
The same observation is made with regards to the impact of executive remuneration on non
performing loans. The strength of the negative relationship between non performing loans and
executive remuneration is quite low as shown by the probability value that is greater than the 5%
threshold.
It is interesting to note that the level of non performing loans is explained better by capital
adequacy and return on assets as indicated by probability values which are less than 5%.
The strong relationship between net income and non performing loans is in line with Boahene,
Dasah and Agyei (2012) (Boahene, Dasah, & Agyei, 2012) whose results from their study found
a significant relationship between credit risk and profitability of selected banks in Ghana.
The relationship between capital adequacy and non performing loans is however higher. This
indicates the success of banking regulation in Kenya in ensuring that banks have enough buffer
capital before taking up additional risk.
The impact of executive remuneration on net charge off is not significant as shown in table 5
above by the probability value that is greater than the 5%.
The weak relationship between the three measures of credit risk above is indicative of the little
impact that executive remuneration has on credit risk in general in listed banks in Kenya. This is
in line with the view held by Holmstrom (1979) where he says that risk preference alignment
between shareholders and managers via the use of incentives is impossible. This weak
relationship observed also supports the view by Ross S. A (2004) (Ross S. A., 2004) who states
that there is no incentive scheme that can be used to influence the risk attitude of managers.
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5.2 CONCLUSION
This study finds a negative non significant relationship between executive compensation and
credit risk in listed commercial banks in Kenya. The negative relationship is best brought out by
two determinants- non performing loans as a proportion of total loans and advances and net
charge off as a proportion of total loans and advances. This appears to suggest that increasing
executive remuneration will lead to banks having less non performing loans and bad debts. This
will be as a result of the better credit screening for customers before giving loans and generally
. . . .
better credit management systems in banks. This will be in line with agency theory where the
owners of banks are able to use remuneration to align their wishes to those of management. This
may however not have a large impact in as far as credit management is concerned due to the in
significance of this relationship.
A limitation was that the structure of the executive pay was not incorporated in the study. This is
despite numerous literature mentioned in the literature review above where pay structure had a
significant link to credit risk. With the introduction of a derivatives market in the country and
possible restructuring of executive pay to include share options, the study of the structure of
executive remuneration on credit risk is an area that can be studied further in Kenya.
An interesting observation made in this study is the strength of capital adequacy in explaining
level of non performing loans of listed banks in Kenya. The area of banking regulation in Kenya
and specifically the success of capital adequacy requirements in the control of credit risk in listed
banks in Kenya is another area worth exploring further.
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