Behavioral Aspects of IT Employees towards Problems and Prospects of Activity...
Analysing the link between CEO remuneration and performance
1. 1
The University of Nottingham
Analysing the link between CEO remuneration and performance: A
stochastic frontier approach
Ahmed Mir
Finance and Investment MSc
2. 2
Abstract1
:
This study utilises an unbalanced panel data set of 41 U.K building societies from 1998 to
2011, examining the relationship between cost efficiency and CEO salary. Using maximum-
likelihood I estimate the parameters of a stochastic cost frontier under a transcendental
logarithmic functional form. The stochastic frontier analysis finds that on average building
societies in the sample operate with a mean cost inefficiency of 18%. The latter stages of
the investigation involve a regression analysis, which estimates the correlation between
the cost efficiency of a smaller sample of building societies and CEO salary. Ultimately, the
evidence presented in this paper finds that efficiency does not have a statistically
significant impact on CEO remuneration but firm size has a substantial influence on
executive pay levels.
1
I would like to thank Dev Vencappa, my supervisor for this project, for his support and
guidance; especially regarding the econometric analysis. In addition, I would like to thank
the University of Nottingham for providing the opportunity to complete the degree of
Finance and Investment MSc
3. 3
Contents Page
1. Introduction ..................................................................................................................................4
2. Review of the Literature ...............................................................................................................6
2.1 Data Envelopment Analysis and Stochastic Frontier Analysis ....................................................6
2.2 Executive Compensation .............................................................................................................8
2.3 Previous Studies Exploring Efficiency: Building Societies ..........................................................11
2.4 Other Mutual Organisations.....................................................................................................17
2.5 Banks.........................................................................................................................................18
2.6 Determinants of Executive Compensation................................................................................19
2.7 Remarks Regarding the Previous Litreature .............................................................................22
3. Methodology and Data ...............................................................................................................23
3.1 Stochastic Frontier Modelling ...................................................................................................24
3.2 The Cost Function......................................................................................................................25
3.3 Maximum-Likelihood Estimation and the Half Normal Model .................................................26
3.4 The Stochastic Cost Frontier......................................................................................................30
3.5 Input and Output Choice...........................................................................................................31
3.6 Cost Function Formulation........................................................................................................34
3.7 CEO Compensation and Efficiency ............................................................................................35
4. Empirical Results.........................................................................................................................39
4.1 Efficiency and CEO Remuneration.............................................................................................44
4.2 Discussion..................................................................................................................................46
5. Concluding Remarks....................................................................................................................47
7. Appendix .........................................................................................................................................50
8. Bibliography ................................................................................................................................54
4. 4
1. Introduction
For decades economists have queried the implications of inefficiency on profitability.
Competitive pressure across the financial services industry hand in hand with new and
more stringent economic regulation has reinforced the strategic requirement of efficiency.
Early on, Demsetz (1973) defined the relationship between efficiency and performance,
describing that more productively efficient firms hold a competitive advantage over their
rivals, often resulting in a higher level of profitability. More recently, academia has applied
various models to estimate efficiency on both a firm and industry level. However, the
majority of this research has still failed to analyse mutual firms such as building societies.
Conversely, the diverse level of research examining the compensation top level executives
has publicised the sometimes absurdly high pay packets of executive management. The
debate has been further amplified by economic crises such as the financial crisis of the last
decade and the now long running Eurozone crisis; spotlighting inflated director pay. For
years policymakers, practitioners and academics alike have questioned if the increasing
disparity between executive remuneration and the average wage are in line with owner
interests. Over the years theorists have attempted to explain the phenomenon of
executive remuneration, where classical economists favour the theory of profit
maximisation and on the other hand managerialists support the theory of corporate
growth (Ciscel & Carrol, 1980). Given that chief executive posts on average require high
levels of experience, and with many chief executive officers (CEOs) holding a professional
qualification to supplement their experience it is understandable that they are
compensated beyond the level of an average employee. However, current evidence
suggests that a CEOs in the FTSE 100 index (UK) can expect on average to receive a
5. 5
remuneration package of in excess £4 million in 2010; a growth of over 400 % since 1998
(The Economist, 2012). The implications of such exorbitant pay packages are not merely a
moral issue but can be considered an economic problem, with high levels of compensation
becoming common place it is important to examine if pay is at all linked to firm
performance. Although, the debate regarding the pay to performance relationship was
popularised over two decades ago, research tackling the relationship between efficiency
and executive compensation is relatively limited to banks and other large public limited
companies.
This paper seeks to add to the current literature by providing an in depth focus on the
relationship between cost efficiency and CEO compensation in the UK building society
sector. By using stochastic frontier analysis this paper will evaluate the cost efficiency of UK
building societies; employing a pooled unbalanced panel data set of up to 13 years on a
sample of 41 building societies; originating from bankscope data. Panel data will provide in
depth evidence in regards to performance, permitting for the tracking of efficiency across
different firms in different years; unlike cross sectional data which only provides a
snapshot (Kumbhakar and Lovell, 2000).
This paper is structured as follows. The next section reviews relevant literature, followed
by a section explaining the methodology used in this paper and discussing data
requirements. Thereafter the paper discusses the estimated cost efficiency scores found
by the econometric analysis and ultimately discussing relationship between cost efficiency
and CEO salary.
6. 6
2. Review of the Literature
Modern microeconomic theory treats producers as successful optimisers, producing the
maximum level of outputs from a given set of inputs. However, it is important to note that
many producers may attempt to minimise input to output ratios, but most do not succeed
in being completely efficient (Kumbhakar and Lovell, 2000). To understand inefficiency
and the theory of production there has been an inflow of methods analysing the
relationship between the inputs and outputs of a firm.
Academia has analysed the subject of executive compensation since the early 1990s with
Jensen and Murphy (1990a) providing one of the most influential studies at the time in
regard to CEO pay. Much of this work has focused on how total CEO compensation is
correlated to firm performance. There are various different proxies for performance,
financial ratios for example; such as return on assets, return on equity and net interest
margin. On the other hand, empirical applications to frontier techniques analysing the
efficiency of firms can also be considered as performance measurement (Baten and Kamil,
2010).
2.1 Data Envelopment Analysis and Stochastic Frontier Analysis
The most popular methods used in efficiency analysis today are extensions of the work
carried out by Farrell (1957), where his ground-breaking research employed linear
programming methods generating a measure for productive efficiency, in addition to
providing a definition for a production frontier. The work by Farrell (1957) was the
foundation upon which Charnes et al (1978) formed the non-parametric technique, Data
Envelopment Analysis (DEA). This technique uses the ratio of inputs to outputs to plot an
7. 7
efficient frontier, which illustrates the most efficient allocation of x inputs to produce y
outputs; where the closer an observation is to the frontier the more efficient it is.
However, DEA fails to observe random events which can impact on the efficiency of a firm,
for example insufficient rain in the right season can reduce a paddy farmers harvest; this is
out of the producers control and not as a result of inefficiency. However, DEA does not
recognise the difference between random noise and inefficiency as it is a deterministic
frontier method and as such any deviation from the efficient frontier is considered as
inefficiency.
Aigner et al, 1977; Meeusen et al 1977 proposed an alternative technique, Stochastic
Frontier Analysis (SFA). This method has the ability to differentiate random errors from
inefficiency is now widely accepted as the benchmarked econometric alternative. It
consists of the estimation of a stochastic frontier, where output is bound by a function of
known inputs, inefficiency and a random error; this is usually in order to plot a cost or
production function (Battese & Coelli, 1995). Similarly to DEA the objective is for firms to
reside as close to or on the given frontier as possible. Although, whilst SFA can separate
genuine inefficiency from random noise DEA has the advantage of requiring no functional
form to estimate the frontier, mitigating the possibility that a functional form may be
unwarranted or too simple. However, the deterministic nature of DEA consequently results
in the failure to allow for statistical inference. Nonetheless, DEA is still a popular method
of efficiency measurement in management science (Kumbhakar and Lovell, 2000).
Frontier functions estimate the maximum possible output given a set of inputs, or the
minimum cost of a set out outputs; usually to estimate production or cost functions. From
the perspective of producers this is crucial to estimate how much output can be produced
8. 8
given an amount of inputs; where production functions mathematically define this
relationship in order to specify the various technical possibilities which are open to
producers (Heathfield & Wibe, 1987). It is recognised that efficiency measures which
employ frontier techniques have advantages over the alternative of accounting ratios
(Beccalli et al, 2006). For example, financial ratios do not consider inputs and the
combination of outputs (Berger & Humphrey, 1992). In addition, a frontier method
provides a concise and clear numerical score complete with ranking (Berger & Humphrey,
1997). Nonetheless, these topics will be discussed in further detail in the methodology
section of this paper.
2.2 Executive Compensation
Academia has attempted to explain the concepts behind CEO remuneration by proposing
different theories and hypothesising various assumptions. This sub-section reviews some
of the many theories documented in the wider literature. These include neo-classical
theory of the firm, the principal agent model, managerial power theory, human capital
theory, tournament theory, social comparison models and information processing theory.
Although, in respect to performance the majority of research focuses on the pioneering
work of Jensen and Meckling (1976) which aggregated the issues identified by previous
researchers to suggest their theory of the firm by defining agency theory; also known as
the principal agent model.
According to neo-classical economic theory the primary objective of a firm is to profit
maximise, consequently maximising the return for the owners. However, due to
constraints in corporate governance often managers maximise their own utility rather than
that of owners (Williamson, 1986). In contrast, work by Festinger (1954) describes a
9. 9
theory of social comparison to explain the determination of executive pay. Where pay for
top level management is benchmarked to compensation levels for comparable positions at
other firms. This prospective utilises consistent peer group comparisons to ensure that pay
is kept in line with competitors. O'Rielly III et al (1988) argue that such relative comparison
is possible as some members of remuneration committees for one company may be
corporate executives of other companies. Their results are consistent with social
comparison theory as their research concludes that CEO compensation is positively related
to the pay of the members of the remuneration committee.
Another hypothesis is the corporate growth theory, which suggests that the size of the
company has a larger impact on executive salary than the profitability of that company.
Cosh (1975) supports this suggestion, documenting that size has a larger impact than
profitability for determining pay. In contrast, Meek and Whittington (1975) argue that
profitability and growth are equal determinants of executive remuneration. Conversely,
the human capital model (Becker, 1975) suggests that executive compensation is
determined by personal factors such as qualifications, age, experience and training. Under
this theory any differentials in pay between executives is down to observed differences in
their personal attributes which impact upon their ability (Shiwakoti et al, 2004).
Tournament theory proposed by Lazear and Rosen in 1981, suggests that the pay structure
of a CEO is not based not upon firm or individual performance but by the position of the
executive in the firms’ hierarchy. Suggesting that the compensation a CEO receives may
surpass the pay his marginal product warrants but still be considered economically
efficient (Main et al, 1993). Such a theory works on the premise that those lower down the
ladder command a lower wage than their marginal product of labour, this disparity in
10. 10
salary provides incentives for employees to compete for promotion to positions of higher
responsibility such as chief executive; which are seen as prizes for long term commitment
(Lazear and Rosen, 1981).
The last but probably most researched theory is the agency problem. The foundations of
agency theory are defined by a simple relationship between two parties, where one party
(the principal) delegates tasks to another party (the agent), who then carries out these
tasks. The principal agent model attempts to resolve conflicts of interest between these
two parties in terms of employment contracts (Jensen and Meckling, 1976). It seeks to
understand how to create a partnership between the two parties to ensure that the
objectives set by the principal are met by offering the agent incentives to ensure that the
agents personal objectives do not hinder the goals of the principal. For example, in public
limited companies it is a common problem that managerial objectives may vary from
shareholder objectives, creating a conflict of interest. The influential paper by Jensen and
Meckling (1976) discusses how ownership and control are separated; an issue which
underpins the classical agency model. In the words of Shleifer and Vishny (1997) managers
may use their power in order to personally benefit themselves, for example managers may
wish to build their own empire (a common topic in M&A) or follow entrenchment
strategies which do not maximise shareholder wealth. In order to ensure this is not the
case, the principal is required to set incentives which ensure the agent does not divert to
peruse goals which maximise his own utility rather than that of the owners. This is in the
form of a contract, designed to optimally maximise the combined utility of both the agent
and principal; this is by ensuring that the maximisation of shareholder objectives coincide
with incentives that will maximise the agents’ utility. Whatever the case, it is a
11. 11
requirement that the agent is monitored beyond simply reviewing performance targets,
but by setting restrictions on executable actions by the agent (Jensen and Meckling, 1976).
2.3 Previous Studies Exploring Efficiency: Building Societies
As mentioned earlier, the concept of efficiency measurement can be attributed to Farrell
(1957) where his non parametric approach defined the estimation of productive efficiency
as the calculation of the maximum amount of outputs a firm can produce from the least
amount of inputs (technical efficiency), and by using inputs in optimal proportions; in other
words producing to where the marginal benefit of a good equals the marginal cost of
production (allocative efficiency). Since Farrell (1957) a large variety of studies have
explored the efficiency of firms and industries, ranging from scale efficiency to economic
efficiency2
. However, examples of studies exploring the mutual organisations sector,
especially building societies are relatively limited. Nonetheless, table 1 provides a summary
of studies within this sector.
2
A firm is said to be economically efficient if it is both technically and allocatively efficient
(Kumbhakar and Lovell, 2000)
12. 12
Author(s) Research Title Sample Technique Type of
Efficiency
Measured
Inputs and Outputs Specified Research Conclusions
Field (1990) Production efficiency
of British building
societies.
71 U.K
building
societies,
cross
sectional
data – 1981.
DEA Technical
and scale
Full-time labour, equipment value
and the number of offices.
value of deposits and newly or
previously advanced mortgages
Significant negative relationship
between size and technical
efficiency. Estimated 14 percent of
firms in the sample productively
efficient and 61 percent of the
sample operating inefficiently as a
result of scale inefficiency.
Drake and
Weyman-
Jones
(1992)
Technical and scale
efficiency in UK
building societies.
76 U.K
building
societies,
cross
sectional
data – 1988.
DEA Technical
and scale
Average employee wage and
capital input price (expenditure of
equipment and buildings divided by
mean asset value), and the price of
leverage (interest paid divided by
the total leverage value).
Total assets (consumer and
commercial loans, mortgage sales,
mortgages and mortgage servicing)
and operating income
41 percent of firms scale efficient,
whereas 61 percent of sample
technically efficient.
Mckillop
and Glass
(1994)
A cost model of
Building Societies,
Producers of
Mortgage and Non-
Mortgage Products.
89 U.K
building
societies,
cross
sectional
data – 1991.
SFA Cost Capital input price (expenditure on
premises and equipment divided by
mean value of assets), Labour input
price (average employee wage) and
the price of borrowed funds
(interest expenses divided by value
of borrowed funds).
Outstanding mortgages and other
commercial assets.
Evidence of significant augmented
economies of scale for local and
national building societies. Whereas
constant returns to scale for
building societies operating
regionally. Overall, varying
deviations in cost efficiency across
mortgage and non-mortgage
products in local, regional and
national building societies.
Table 1: A Summary of Studies Analysing the Efficiency of Building Societies
13. 13
Author(s) Research Title Sample Technique Type of
Efficiency
Measured
Inputs and outputs Research Conclusions
Piesse and
Townsend
(1995)
The Measurement of
Productive efficiency
in UK building
societies.
57 U.K
building
societies,
cross
sectional
data – 1992.
DEA Productive Tangible fixed assets, management
expenses, number of branches,
number of full time equivalent
staff, interest paid on non-retail
capital and interest paid on retail
capital
Number of depositors, number of
borrowers, profit, interest earned
from liquid assets and interest
earned from mortgages
Diseconomies of scale present in
larger firms, with only six building
societies on the efficient frontier.
77 percent of sample operating
with diseconomies of scale.
Drake and
Weyman-
Jones
(1996)
Productive and
allocative
inefficiencies in U.K.
building societies: A
comparison of non-
parametric and
stochastic frontier
techniques.
48 U.K
building
societies,
cross
sectional
data – 1988.
DEA and
SFA
Productive
and
allocative
The price of labour, the value of
retail funds and deposits, the value
of non - retail funds and deposits,
the price of funds (interest
payments divided by the book
value of the sum of retail and
wholesale funds) and the price of
capital (administration and office
expenses divided by total assets).
Value of mortgage loans,
commercial assets and liquid asset
holdings beyond capital
requirements.
DEA estimated overall mean
inefficiency score of between 12
and 13 percent, where most of this
inefficiency consisted of allocative
inefficiencies. SFA score
supplemented the DEA score and
ultimately indicated that there was
a negative relationship between
size and technical and scale
efficiency.
14. 14
Author(s) Research Title Sample Technique Type of
Efficiency
Measured
Inputs and outputs Research Conclusions
Esho and
Sharpe
(1996)
X-efficiency of
Australian
permanent
building socities.
20 Australian
building
societies,
panel data
from 1974 to
1990.
SFA X-
Inefficiency
Cost of funds and a wage index
(similar to the price of labour).
Average house loans, average
deposits, total government and
other securities, other loans and
fixed assets.
High levels of estimated X –
inefficiency across the sample and
larger organisations exhibit cost
savings from economies of scale.
Ashton
(1997)
Cost efficiency and
UK building
societies. An
econometric
panel-data study
employing a
flexible Fourier
functional form.
99 U.K
building
societies,
panel data
from 1990 to
1995.
SFA Cost Price of labour (total wage divided
by the number of full time
employees), price of capital
(aggregation of property and
equipment rentals and
depreciation divided by the
quantity of physical capital), price
of deposits (total interest payable
divided by the quantity of deposits
including retain and non-retail
costs).
Mortgage loans and non-mortgage
advances.
Mean efficiency estimated at 76
percent using flexible Flourier form,
whereas 72.2 percent using translog
form.
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Author(s) Research Title Sample Technique Type of
Efficiency
Measured
Inputs and outputs Research Conclusions
Worthington
(1998)
Efficiency in
Australian building
societies: An
econometric cost
function approach
using panel data.
22 Australian
building
societies,
panel data
from 1992 to
1995.
SFA Cost Price of physical capital (sum of
physical capital expenditures
divided by the book value of net
total office premises and
equipment). Price of deposits (total
interest expense divided by total
deposits and other borrowings).
Price of labour (total expenditures
on employees divided by the
number of full-time employees).
Personal loans, property loans,
commercial loans and other
securities.
Mean inefficiency score of 21
percent. Branch or agency networks
have a large impact on overall
efficiency; where an extensive branch
network diminishes the ability of the
head office to ensure cost efficiency.
16. 16
Table one outlined studies which analyse the efficiency of building societies and although
each study focused on the same industry there was a variety of approaches taken to input
and output specification. Given the large variety of literature on the subject of efficiency it
is puzzling that there is no widely accepted consensus on input and output choice.
Previously, the majority of studies which focus on building society efficiency have adopted
the intermediation approach (Hardwick, 1990; Drake & Weyman-Jones, 1996; Ashton,
1997). The intermedation approach (Sealey & Lindley, 1976) views financial institutions as
the intermediatory in between the supply and demand of funds (Casu & Molyneux, 2003).
Here, inputs are usually labour and capital costs, interest expenses on total funds
(including customer accounts) and output is measured by loans and assets.
Although not as popular as the intermediation method, other approaches also exist. For
example, the production approach (Piesse and Townsend, 1995), where deposit taking
institutions are assumed to keep customer deposits, issue mortgages and other loans, in
addition to managing other financial assets and overseeing customer transactions (Berg et
al, 1993). Less empirically tested specifications, include the asset approach (Drake and
Weyman-Jones, 1992) and the value added approach. The asset approach is similar to the
intermediation approach but outputs are specified in terms of loan assets, the latter
identifies inputs and outputs in terms of their value added to the firm.
Ultimately, the intermediation approach dominates the non-bank financial institution
literature. This is usually the result of problems collecting accurate (sensitive) data, which
are associated with the other approaches (Worthington, 1999). Although in terms of
mutual organisations it is important to note that building societies which operate as a
mutual service provider the behavioural assumption of profit maximisation (which is
17. 17
associated with the intermediation approach) may no longer apply, as the objective of a
mutual can be recognised as maximising the services provided to its members (Fried et al,
1993)
2.4 Other Mutual Organisations
Other mutually run firms such as mutual credit unions and savings and loans associations
(S&Ls) have also been investigated for inefficiencies; although in this section we only
provide a limited summary (for a review of the current literature see Worthington, 2011).
For example, Mester (1993) applied SFA to analyse the efficiency of a large sample set of
over 1,000 S&Ls. This large study used the wage rate (labour expenses divided by number
of full time employees), price of deposits (interest expense divided by the total value of
deposits) and price of physical capital (office occupancy and equipment expense divided by
total office assets) as inputs. Output parameters were specified as mortgages, securities
and other investments, commercial and consumer loans. The study concluded that publicly
owned S&Ls are less efficient than mutually operated S&Ls. Additionally, Mester (1993)
notes that there is a correlation between a higher capital asset ratio and greater efficiency.
Similarly, Worthington (1999) utilised a stochastic frontier approach to analyse the
efficiency of 150 Australian credit unions. In this study inputs were defined as the price of
physical capital (total outlay on office and equipment divided by the book value of office
premises and equipment), and the price of labour (total outlay on employees divided by
the number of full time employees). Output was measured by deposit securities and other
investments, personal, property and commercial loans. His research indicated that a large,
financially stable credit union with a small number of branches is more efficient than
smaller credit unions. This result provides a stark contrast from previous research by
18. 18
Cebenoyan et al (1993) in which the coefficient for the number of branches was found to
be insignificant on efficiency.
2.5 Banks
Banks have received widespread criticism in regard to their recent handling of the financial
crisis and the high level of bonuses which are associated with the banking industry,
especially investment banks. As banks operate in the same market as building societies
but simply undertake more risky activity it is essential to provide some of the current
literature around the topic.
Since the single market initiative the European banking industry has undergone major
changes in an effort to conform to a more efficient benchmark (Altunbas et al, 2001).
Altunbas et al (2001) used a stochastic frontier model to investigate the efficiency of the
European banking industry, their research focused on estimating scale economies, X-
inefficiencies and technical change from 1989 to 1997 for a large sample of European
banks. This study used the price of labour, price of funds and the price of physical capital as
banking inputs. As is the usual method with other similar studies which use Bankscope for
data collection this research used a proxy for the number of employees as total assets,
where the cost of labour was measured as total personnel expenses divided by total assets.
Their analysis concluded that typically, scale economies are between the 5% to 7% mark,
whereas X-inefficiencies are much larger, between the 20% and 25% mark. Although, X-
inefficiencies appear to vary with the size and market of the bank; suggesting that banks of
all sizes can achieve a more efficient standard through reducing the reported inefficiencies.
19. 19
The evaluation of efficiency has also proved popular among academics in the United
States. For example, Berger & Mester (1997) analysed the differences in the efficiencies
across the US banking industry, examining three concepts in efficiency; cost, alternative
profit and standard profit efficiency. Their research indicated that there are scale
economies for banks with a much larger size than indicated in the previous literature.
This section has only provided a limited summary in respect to banking efficiency, for a
further in depth analysis on banking efficiency see Berger et al, 1993, 2000; Resti, 1997 and
Vander Vennet, 2002.
2.6 Determinants of Executive Compensation
“There is a strong prima facie case that inappropriate incentive structures played a role in
encouraging behaviour which contributed to the financial crisis” (Turner, 2010, p. 80).
The issue of executive compensation is a controversial and a highly documented topic in
previous academic literature; with a variety of research based upon companies in the UK
(Cosh, 1975; Conyon, 1995, 1997, Ingham and Thompson, 1993; 1995 and McKnight,
1996). Across the wider literature there is a large focus on the sensitivity to firm
performance (for example Shiwakoti et al 2004;Gregg et al 2005; Ozkan, 2007; Nourayi and
Mintz, 2008). Even still, research examining the pay to performance relationship across
mutual organisations has been far from rigorous. Notably, two studies by Ingham and
Thompson (1993) and (1995) examined the determinants of CEO compensation in the UK
building society sector. The earlier paper employed a sample of 52 building societies across
two years, concluding that firm size (proxied by total assets) is the most influential
component determining CEO salary. Their more recent paper found evidence of only a
20. 20
weak positive relationship between performance and executive remuneration, suggesting
that age has a larger impact on pay. In addition, their work indicated a negative
relationship between the size of the mutual and performance; Ingham and Roberts (1995)
put this down to deregulation within the industry. Their research concluded that as a
mutual company who do not issue shares they face weaker market controls, which causes
a misalignment of owner and CEO interests.
Understanding the empirical relationship between pay to performance can be accredited
to research by Jensen and Murphy (1990a), their influential paper used a sample of over
2500 CEOs across a fourteen year time period; documented that the pay to performance
sensitivity3
(PPS) had weakened over the last sixty years or so. Jensen and Murphy (1990b)
argued that “in most publicly held companies, the compensation of top executives is
virtually independent of performance” (Jensen & Murphy, 1990b, p. 138). Their work
suggested that companies which exhibit a higher sensitivity of pay to performance perform
better overall than those with a lower elasticity of pay to performance (Jensen & Murphy,
1990b). More recent UK studies by Gregg et al (1993) and Conyon (1995) have reinforced
earlier findings by Jensen and Murphy (1990a). With Gregg et al (1993) concluding that
there is a weak positive link between executive remuneration and corporate performance.
In contrast, a UK study by McKnight (1996) suggested that the relationship between
executive compensation and firm performance is a positive one that is stronger than
implied by previous work.
3
The impact of a one dollar change in shareholder wealth on the wealth of the chief
executive officer (Jensen & Murphy, 1990a)
21. 21
This is now the wider consensus across academia, as evidence indicates that the
association between performance and executive pay is a weak one, where factors other
than performance are more highly correlated to pay. For example, Gomez-Mejia et al
(1987) document that after controlling for size, researchers have found the relationship
between CEO pay and performance to be weaker and less consistent than predicted by
economic theory. Nourayi and Mintz (2008) indicate that firm performance is a significant
determinant of executive cash compensation for the first three years as CEO, but when
tenure is longer than fifteen years than performance is not a significant determinant of
salary.
Stathopoulos et al (2005) provides an interesting summary of the issue:
“The overall impression one gains from this vast body of work is that a link between
executive pay (including stock option payoffs) and corporate performance does exist.
However, the link is quite weak, statistically significant, but far from compelling”
(Stathopoulos et al, 2005, p.91).
Researchers have noted that industry can enforce differentials in executive pay across top
level management (O'Rielly III et al , 1988). Possibly, because of the variations in the level
of strategic compeition and consequently the need for innovative dominance flunctuates
across industires; where employees in more competitive and innovative sectors could
demand higher salaries.
Various scholars have attempted to explain the relationship between firm size and
executive pay (Simon, 1957; Lydall, 1968, Rosen, 1990, Boyd, 1994 and Schaefer, 1998).
Simon (1957) finds that executive pay and size are positively correlated, finding that the
22. 22
pay of senior managers such as CEOs can be written as a function of the number of
employees they supervise, either directly or indirectly (Kubo, 2000). Research by Boyd
(1994) found a weak relationship between CEO pay and firm size4
, although some studies
have documeted a strong relationship between executive pay and firm size5
(Deckop,
1998; Jones & Kato, 1996). Rossen (1990) disputed that in equilibrium, the most capable
executives occupy top positions in the largest firms, where their marginal productivity is
amplified across the people below the CEO. Schaefer (1998) discussed how the marginal
marginal productivity of executives flunctuates with firm size, with larger firms paying
more with an expectation of more effort . More recently, Chalmers et al (2006) concluded
that firm size, when measured by total assets is the strongest determinant of CEO
compensation; arguing that CEOs in larger firms usually have a higher skill set and
qualifications realitive to their counterparts in smaller organisations. Lastly, Nourayi and
Mintz (2008) argued that firm size is a signifcant indactor of salary regardless of the tenure
of a CEO.
2.7 Remarks Regarding the Previous Litreature
Given the variety of previous studies it is clear that there is no definitive approach to
efficiency measurement or the evaluation of executive compensation. Surprisingly, even
forty years since its inception, there is still no widely accepted consensus in regard to the
input and output specification required for frontier analysis. Nonetheless, the succession
4
Log of net sales served as a proxy for size.
5
Number of sales is used as a proxy for size.
23. 23
of innovative research around the topic of efficiency over the last 30 years has ensured the
development of the paradigm beyond its original framework.
3. Methodology and Data
In this section we discuss cost efficiency and the stochastic frontier model. Defining how
the model employed in this paper measures inefficiency, originating from the firms cost
function and how the model distinguishes between random noise and inefficiency.
Consequently, we compute a maximum likelihood model which is used to estimate the
parameters of the stochastic frontier. Where the data used for this section of the paper
originates from the Bankscope database, which contains the balance sheets and income
statements of all the building societies in the sample.
In order to analyse the performance of CEOs in the UK building society sector we use
annual reports published by the firms in the sample, containing the total remuneration
package of its current chief executive. We use data covering 2010 to 2011 for a sample of
33 CEOs (66 observations) in combination with a scoring for efficiency obtained using
stochastic frontier analysis. As such we hypothesise an econometric function which will
provide the basis for understanding the relationship between efficiency and executive pay.
As discussed by Varian (1990) the principles of economic theory are bound by optimising
behaviour. This rests on the assumption that firms minimise costs, whereas consumers
maximise utility. Varian (1984) documents the Weak Axiom of Cost Minimisation (WACM),
where the cost of planned production must be less than or equal to any other production
plan which yields the same amount of output. Similarly, cost efficient firms minimise costs,
where relative cost efficiency can be defined as “the ratio between the minimum cost at
24. 24
which it is possible to attain a given volume of production and the cost actually incurred.”
(Maudos et al, 2002, p.7).
3.1 Stochastic Frontier Modelling
The stochastic frontier model uses a hypothesised function to estimate the relative
efficiencies of decision making units (DMUs), where a DMU can be anything in which
output is measurable (for example a person, firm or industry). It estimates the maxima or
minima of a dependent variable given explanatory variables, usually to estimate
production (maxima) or cost (minima) functions. Such a methodology assumes that the
production of a firm is limited by the sum of a parametric function of known inputs, and a
random error; which is associated with uncontrollable factors or model misspecification
(Worthington, 1998). Providing the necessary tools for a two component error structure,
where one measures random6
factors which affect output and the second measures
individual firm deviation from the efficient frontier which is within the organisations
control (Worthington, 1998). The relative efficiency of a DMU within the SFA framework is
defined as the ratio of multiple weighted inputs to multiple weighted outputs, where the
weight is chosen such that it maximises the efficiency for a DMU. These relative ratios
combine to create a production (cost) frontier, outlining the most efficient possibilities and
the closer a DMU is to this curve the more efficient it is. In the case of productive
efficiency all observations are found inside or on the stochastic production frontier, in
respect to cost efficiency, all observations lie outside or on the stochastic cost frontier.
6
Random shocks which are beyond the control of a firm can impact output, for example
weather (Kumbhakar and Lovell, 2000)
25. 25
Where any deviation from the frontier has two potential sources: input allocative
inefficiency and input-orientated technical inefficiency (Kumbhakar and Lovell, 2000).
3.2 The Cost Function
A review7
of the optimisation problem stochastic frontier analysis tackles in its most basic
form presents the following cost function:
(2.1)
Where describes the scalar total cost of firm I at time t and represents the
cost function for firm i at time t. Where is a vector of explanatory variables which
include input prices and measures of output for firm i at time t, whereas represents a
vector of unknown parameters yet to be estimated.
Now if we assume that firms are not completely efficient we have the following equation:
(2.2)
Where Describes the efficiency of firm i at time t, where must be between 0 and 1.
If then the firm is fully efficient and is producing the optimal amount of outputs
from the given inputs, whereas when firm i is said to be inefficient to some degree.
Under this specification the cost function contains is split into two: a deterministic element
which is common among all producers [ and an element for producer specific
random shocks which can impact the output of a firm [ ; such that
7
This section is provided using the detailed analysis carried out in Kumbhakar and Lovell
(2000)
26. 26
(2.3)
In order to interpret the inefficiency as a percentage deviation from the efficient frontier
we take the natural log of both sides:
{ } { } (2.4)
For simplicity we further assume that the cost function takes the log linear Cobb-Douglas
form and that there are k inputs, generating:
∑ ( ) (2.5)
For a detailed derivation see Kumbhakar and Lovell (2000).
3.3 Maximum-Likelihood Estimation and the Half Normal Model
Maximum-likelihood is a statistical technique of deriving estimates for the parameters of a
model given an observed set of data. Maximum-likelihood uses mathematical iteration to
estimate a maximised function where using some values of the parameters to be
maximised MLE adjusts the estimates iteratively until it estimates values of the parameters
which are considered to be the most likely maximum for a given set of data. Kumbhakar
and Lovell (2000) comment that if we can make distributional assumptions about and
MLE techniques should provide a more efficient prediction of the models parameters
than other estimation methods8
. This is supported by Greene (2011) who notes that in
respect to stochastic frontier estimation, least square estimation is unbiased and
consistent, however the maximum likelihood estimator is non-linear and is more efficient
than least squares.
8
GLS or LSDV for more see Kumbhakar and Lovell (2000)
27. 27
The results obtained from equation (1.1) are highly dependent on the assumed variables
and assumptions underlying the model. The majority of studies exploring the stochastic
frontier model have made various distributional assumptions of the error term following
either a half normal, exponential or gamma distribution. In the following section we
discuss one of the most commonly assumed (and the one assumed in this paper)
distributions (half-normal).
If we assume that is half normal we have the following assumptions:
)
(ii)
(iii) and are distributed independent of each other and the regressors.
Assume we wanted to estimate +
Given the independence of the error terms and as follows a half normal
distribution then the density function of is given by:
√
exp (1.2)
Whereas the density function of is given by:
√
exp - (1.3)
Given assumption (iii) the joint density function of and is given by the product of (1.2)
and (1.3) such that:
28. 28
√
exp - - (1.4)
Given that obtain:
exp - - (1.5)
In order to obtain the marginal density function of we must integrate out of
generating:
∫
= . [ ( )].exp -
= . ϕ ( ). ( )
For a stochastic cost frontier
. ϕ ( ). ( ) (1.6)
Where:
√ , ⁄ , standard normal cumulative distribution
function and standard normal density function.
Using equation (1.6) the log likelihood function for I producers is:
∑ ( ) ∑ (1.7)
29. 29
Equation (1.7) can be maximised with respect to the parameters to be estimated in order
to obtain maximum likelihood estimates for all parameters; where the estimates are
consistent as long as I tends to +
Now we have the estimated joint residuals , in order to derive the value of we use the
Jondrow et al (1982) estimator of individual technical efficiency, where:
Given that ⁄ ) is distributed as the mean or the mode can be used as a
point estimator for which is given by the following:
⁄ + [
⁄
⁄
]
= [ ( )] (2.0)
and
⁄ = (2.1)
Where = ⁄ and ⁄
Once the estimates in (2) or (2.1) are obtained individual producer technical efficiency is
calculated by:
̂ (2.2)
Where: ̂ is ⁄ or ⁄
Estimations of individual firm specific efficiency as per the Jondrow et al (1982) method
take a value between 0 and 1, where the closer to 1 the firm is the more efficient it is.
30. 30
Although, it is important to note that although the Jondrow et al (1982) point estimator is
unbiased but it does not consistently measure technical efficiency as
- (Coelli, 1995).
3.4 The Stochastic Cost Frontier
We express the frontier as a single equation cost function such that:
∑ i =1….N and t=1….N (3.1)
i =1….N and t=1….N (3.2)
Where ln denotes the natural logarithm of total cost for firm i at time t. is a (K x 1)
vector of input price ratios (P) and outputs (Q) for firm i at time t. The disturbance term
consists of two variables, where we assume the half normal assumptions outlined earlier in
the paper (assumption i to iii).
Following the conditional distribution approach outlined by Jondrow et al (1982) we
decompose the error term for a half normal distribution, providing an unbiased estimation
of cost inefficiency. As discussed in the previous section, we argued that using MLE was
more efficient than alternative methods. In order to obtain the estimates for equation (2)
we must first use MLE to obtain estimates of (3.1) for the stochastic cost frontier.
However, prior to obtaining estimates for (2) and (3.1) we need identify the vector of input
prices and outputs, in addition to specifying a cost function, these are the steps outlined in
the next two sections.
31. 31
3.5 Input and Output Choice
Apart from adopting a parametric or non-parametric approach researchers have previously
employed differing choices to input and output specification. We follow the production
approach as previously outlined earlier in the paper. Where we use input price ratios
consisting of the cost of physical capital9
measured by total non-interest expenses divided
by fixed assets and cost of labour10
, measured by personnel expenses divided by total
assets respectively. Outputs are measured by residential mortgage loans, total deposits
and other earning assets. A summary of the statistics in regard to variables used and a
description of the variables are provided in tables 2 and 3.
The input and output choice is illustrated below:
9
As per the specification used by Maudos et al (2002)
10
Ideally, we would like to measure the cost of labour by total personnel expenses divided
by the number of employees. However, due to the lack of data in bankscope for the
number of employees of a firm we follow a method employed by other researchers using
Bankscope and use total assets as a proxy for the number of employees (Molyneux et al,
1994; Bikker and Groeneveld, 2000)
Building Society
OEA
RML
Cost of Capital
Cost of Labour
Total Deposits
Cost of Funds
INPUTS OUTPUTS
32. 32
Where RML = Residential mortgage loans, Total Deposits = all deposits including customer,
retail and non-retail deposits. OEA = Other earning assets (assets which earn a return apart
from deposits and loans),
Whilst there is no widely accepted consensus on input and output specification we have
decided to peruse the production approach as it provides a method commonly overlooked
in the literature and as such exploring this approach may provide a different outlook on
the efficiency of mutual organisations (Worthington, 1998)
33. 33
Table 3: Descriptive Statistics
Variable Mean Std. Dev. Minimum Maximum
Total Cost (000’s) £246,000 £1,053,000 £1,000 £9,744,000
Cost of Capital (ratio) £2.1 £2 £0.52 £24.5
Cost of Labour (ratio) £0.0053 £0.0024 £0.0003 £0.0221
Cost of Funds (ratio) £0.0127 £0.0130 £0.0043 £0.1597
Total Deposits (000’s) £4,575,000 £20,052,000 £14,400 £169,866,000
Residential Mortgage Loans
(000’s)
£4,033,000 £19,324,000 £15,400 £155,939,000
Other Earning Assets (000’s) £4,367,000 £4,596,000 £5,600 £155,469,000
Variable Variable Name Description (for firm i at time t)
C Total Cost Total non-interest expense + total interest expenses
Cost of Labour Total personnel expenses divided by total assets
Cost of Capital Total non-interest expenses divided by fixed assets
Cost of Funds Total interest expense divided by total deposits
Total Deposits Total customer, retail and other deposits
Residential
Mortgages
Total residential mortgages
Other Earning
Assets
Other assets which are not loans which earn a return
Table 2: Dependent Variable, Inputs and Outputs
34. 34
3.6 Cost Function Formulation
In the previous literature there are two dominating cost functions, these are the Cobb-
Douglas and the translog (transcendental logarithmic) functional form. The Cobb-Douglas
is a linear in logs model, which is easy to estimate and requires few parameters. However
being a simplistic functional form it assumes that each firm has the same production
elasticity and an elasticity of substitution equal to 1. In contrast, translog is quadratic in
logs, providing a much more flexible functional form, applying firstly no a priori restrictions
on substitution or production elasticities, and secondly, permitting economies of scale to
vary across firms (Worthington, 1998).
Consequently, in this paper we adopt the translog specification, where the error term
is composed of two components, and . We assume this error term follows a normal-
half normal distribution where ), , in addition and
are distributed independent of each other and the regressors. In order to estimate
equation (3.1) we specify the following multi output translog cost equation:
( ) ( ) ( ) +
( ) + ( ) + + +
+ + +
+ + + (4.1)
35. 35
In order to ensure linear homogeneity in input prices we follow Worthington (1998) and
Fiorentino et al (2006) by normalising total costs and all input prices by one selected input
price (in this case the cost of capital), as such the cost of capital disappears as the cost of
capital divided by the cost of capital equals zero.
In respect to equation (4.1): lnTC denotes the natural logarithm of total cost11
(total non-
interest expense + total interest expenses + personnel expenses) divided by the cost of
capital. refers to the natural logarithm of outputs (total deposits, residential
mortgage loans and other earning assets). is the natural logarithm of input prices
normalised by dividing against the cost of labour (the cost of capital divided by the cost of
labour and the cost of funds divided by the cost of labour).Furthermore as described
earlier following the execution of the translog cost frontier (4.1) we estimate the
conditional expectation of given as per the Jondrow et al (1982) firm specific
efficiency specification (2.1).
3.7 CEO Compensation and Efficiency
As the aim of this paper is not to solely examine efficiency but to understand the impact of
changes in CEO salary in tandem with efficiency scoring we must regress the efficiency
scores provided by the translog cost frontier in the previous section to the CEO salary for
each executive; providing us with a hypothesised function, which will serve to predict the
relationship between CEO salary and firm efficiency. This is by regressing firm level CEO
compensation levels for a smaller sample set of 33 building societies across two years
(2010 and 2011) against the efficiency score found by the frontier analysis. In addition, we
11
We follow the same definition of total costs as Casu & Molyneux (2010)
36. 36
carry out two separate cross sectional regressions (for the years 2010 and 2011) to
understand any yearly changes in cost efficiency and the impact of this on yearly CEO
salary. We will use the following pooled model12
for the regressions:
(5.1)
Where:
= Total compensation by CEO of firm i in year t
= Total assets13
of firm i at time t
, Coefficients yet to be estimated
= SFA score of CEO of firm i in year t
= error term
We hypothesise that more efficient CEOs gain a higher level of compensation as such we
expect > 0 (positive relationship). Moreover, we suspect that CEOs who are more
efficient are rewarded with a higher total compensation (ceteris paribus), consequently we
expect as we expect compensation to be positively related to the size of the firm.
Lastly, as we suspect that that increases in compensation and firm size are non-linear
(compensation does not increase proportional to firm size) we include a square term of
12
We use a similar model to Chen et al (2009) except we use SFA instead of DEA for the
efficiency analysis and the cross sectional regressions are simply without the label for time.
13
We use total assets as a proxy for size as used in previous empirical research (Chalmers
et al, 2006)
37. 37
total costs in the regression. Lastly, we hypothesise that there are decreasing returns to
scale in respect to compensation with firm size (ceteris paribus), thus we expect .
Table 5 provides a summary of statistics from the data concerning CEO salary, we note that
the standard deviation is higher than the mean for the year 2010 indicting that the data is
highly skewed, this is complimented by the fact the maximum value is much higher than
the mean.
Variable CEO Pay 2010 CEO Pay 2011
Mean 294 278
Standard Deviation 329 269
Min 80 71
Max 1884 1539
N = 66
Table 5: Summary CEO Salary Descriptive Statistics
39. 39
4. Empirical Results
In this section, we discuss the results obtained from the stochastic frontier analysis in
addition to the results from the regressions outlined in the previous section; analysing the
relationship between cost efficiency and CEO salary. The appendix provides individual
efficiency scores obtained for all years from the frontier analysis, however summary
efficiency statistics and a description of the regression coefficients are provided where
required.
Table 7 presents the maximum-likelihood estimates for the parameters of the normalised
translog cost frontier as described in equation (4.1). We reject the null hypothesis of joint
insignificance for the coefficients of the cost function ( = = 0) by using the
log-likelihood ratio test statistic with chi-square distribution. In addition we reject the null
hypothesis of no technical inefficiency ( = 0) as it is true that . We find that
both error terms are statistically significant, with the inefficiency component significant at
the 99% confidence level and the random noise component significant at the 99.9%
confidence level.
40. 40
Parameter Variable Coefficient Std.Error Parameter Variable Coefficient Std.Error
4.183
(1.91)
2.189 -0.109
(-0.24)
0.454
1.618
(1.05)
1.548 0.544
(0.44)
1.224
-0.298
(-0.19)
1.586 0.527
(-0.93)
1.526
-0.919
(0.45)
2.059 -0.992
(-0.65)
1.494
-0.198
(-0.19)
1.041 -0.582
(-0.39)
0.674
-0.198
(-0.07)
2.661 -0.759
(-1.13)
1.912
0.274
(0.84)
0.325 1.137
(0.59)
0.637
-0.085
(-0.13)
0.673 -0.832
(-1.31)
0.637
2.272
(1.06)
2.144 -1.443
(-0.55)
2.631
0.337
(1.08)
0.311 0.467
(0.59)
0.769
0.989
(0.30)
3.252 -2.743***
(-7.86)
0.349
-2.818**
(-2.69)
1.048
t statistics in parentheses: *p < 0.05, **
p < 0.01, ***
p < 0.001 : Log-likelihood = -56.31 N = 294
Where: Ln = Natural Logarithm, P1 = Cost of labour, P2 = Cost of funds, P3 = cost of capital, Y1= Residential
mortgage loans, Y2 = Other earning assets, Y3 = Total deposits, P1* = P1/P3, P2* = P2/P3, Variance of two
sided stochastic frontier, = Variance of inefficiency and Total error variance in the model .
Table 7 – Translog Cost Function
41. 41
Table 8 provides a summary of mean cost efficiency scores on a yearly basis for the whole
sample period. The scores outlined in table 8 each describe how far above the respective
efficient cost frontier each building society is estimated to be. The overall mean efficiency
score was estimated at roughly 0.82, indicating a mean cost inefficiency of 18% across the
sample. We find that the proportion of efficient CEOs has declined from 2003 to 2008 year
on year, interestingly the lowest mean inefficiency (year) score is in 2008, coinciding with
the worldwide financial crisis. The estimated cost efficiencies found in this paper are
consistent with the earlier findings of Worthington (1998), although his study focused on
Australian building societies. It is interesting to see that although we follow a production
approach the mean cost inefficiency score is close to that found by Worthington (1998)
(21%) who employed an intermediation approach.
42. 42
Table 9 summarises the mean firm specific cost efficiency scores estimated by the translog
cost frontier along with a value for average total assets for each mutual, we find that there
is no definitive given relationship between firm size and efficiency.
Year Observations Mean Std. Dev. Minimum Maximum
1998 2 0.8184736 0.0297145 0.7974623 0.8394849
1999 2 0.8451663 0.0080369 0.8394833 0.8394849
2000 3 0.8352573 0.0147830 0.8228537 0.8516154
2001 3 0.8498542 0.0075276 0.8419057 0.8568749
2002 3 0.8186927 0.0414188 0.7742882 0.8562799
2003 6 0.8565482 0.0170381 0.8304076 0.8836235
2004 25 0.8325011 0.0170381 0.8304076 0.8836235
2005 31 0.8051640 0.0511372 0.6971726 0.9164624
2006 31 0.7949433 0.0461719 0.6967176 0.8746896
2007 32 0.7722448 0.0584345 0.6440333 0.8750280
2008 39 0.7565842 0.0650992 0.5897666 0.8827966
2009 39 0.8487659 0.0466377 0.7238307 0.9229753
2010 39 0.8670900 0.0338448 0.7686022 0.9198319
2011 36 0.8710819 0.0303110 0.7943464 0.9176146
1998 - 2011 294 0.8200372 0.0615920 0.5897666 0.9229753
Note: For a detailed description of the efficiency scores associated with the translog cost
frontier (4.1) see Table 10 (appendix).
Table 8: Stochastic Cost Frontier Efficiency Summary
44. 44
Note: Average total assets (ATA) are used as a proxy for size, similar to table 9. ATA are
measured in millions of GBP (£) and cost efficiency scores can range between 0 and 1.
4.1 Efficiency and CEO Remuneration
We analysed the link between CEO salary and firm efficiency across two years, for
comparison we have provided three different sets of results, with one providing estimates
of a pooled OLS approach on regression analysis for the two year sample of 33 building
societies. Secondly, we split the regression into a cross sectional basis and analyse the
results from 2010 and 2011 separately in order to detect any changes to the relationship
between SFA score and CEO salary, in addition to being able to discuss and evaluate any
skewness in the results. Note that we have multiplied the estimated SFA scores by 100 in
order to be able to analyse a one unit change in efficiency against CEO salary (thus SFA
score in the regressions below is between 0 and 100 instead of 0 and 1).
Examining the relationship between Cost Efficiency and CEO Salary
Variable Pooled 2010 - 2011 2011 Cross Section 2010 Cross Section
Constant 441
(1.45)
594.1
(1.40)
114.9608
(0.17)
SFA Score -2.9
(-0.83)
-4.712
(-0.96)
0.997
(0.13)
Total Assets 0.467***
(7.74)
0.0159**
(5.73)
0.00688**
(2.78)
-0.00000258
(-1.76)
-4.74e-08
(-3.25)
8.79e-09
(0.62)
Adjusted 0.8869 0.8634 0.8280
N 66 33 33
t statistics in parentheses: *p < 0.05, **
p < 0.01, ***
p < 0.001
Table 10: Efficiency and CEO Salary Regressions
45. 45
The regression analysis summarised in table 10 indicates that overall SFA score is
negatively correlated to CEO salary, the skewness of the results is visible when comparing
the pooled results to the result from the 2010 cross sectional regression. Nonetheless, it is
puzzling as to why the estimated SFA score decreases (on average) with increases to
executive pay; it seems that it is probably a simple issue of a requiring more data to
provide a more accurate description of the relationship between pay and efficiency. In
addition, the magnitude of an increase in efficiency on CEO salary varies across years, with
2011 indicating that the SFA score and CEO pay exhibit a strong negative relationship, on
the other hand the 2010 regression indicates a weak positive relationship between
efficiency and pay, where if we assume efficiency serves as a proxy for performance we
find a weak positive link between pay and performance, in line with previous research by
Jensen and Murphy (1990a) and Gregg et al (2003).
Interestingly, total assets is significant in each estimated regression, varying from being
statistically significant at the 99% confidence level in the cross sectional regressions and at
the 99.9% confidence level in the pooled regression. In all the regressions an increase in
size when measured by total assets has a statistically significant increase in CEO salary;
corresponding with the earlier findings of Chalmers et al (2006).
Earlier we hypothesised that the coefficient for SFA score > 0 as we suspected that more
efficient executives receive a higher level of compensation, given our results we fail to
reject the null hypothesis as we have two out of three regressions in which SFA score is less
than zero. We also suspected the coefficient for total assets would be > 0 as we expected
that CEO salary is positively correlated to firm size. Given the results in table 9 we fail to
reject the null hypothesis as the estimates provided by the regressions indicate that size
46. 46
and salary exhibit a significant positive relationship. Lastly we expected the coefficient of
total-assets² < 0 as we suspected that there are decreasing returns to scale for CEO salary
with respect to firm size, once again we fail to reject the null hypothesis of decreasing
returns to scale; as the regression for the year 2010 has a positive coefficient for total-
assets². Although, overall (pooled regression) there is evidence of decreasing returns to
scale.
4.2 Discussion
Overall we find that firm size is powerful determinant of CEO salary, especially when
compared to the efficiency score. Adjusted R² for all three regressions indicates that we
have explained over 80% of the variation in CEO compensation with the given variables.
The investigation of building society efficiency has yielded results which are in line with
previous empirical research, with a mean building society cost efficiency of 82% the firms
in the sample would have to increase outputs by 18% to be considered completely cost
efficient (ceteris paribus). The efficiency scores provide the basis for which improvements
can be made in order to not only be more cost efficient but as a result more profitable. As
noted earlier in the paper, the lowest mean year efficiency score is estimated for 2008,
around the time of the global financial crisis. We know that building societies given their
conservative attitude to risk were in many ways sheltered from the brunt of the financial
crisis; brought on by mortgage lending and later derivative trading gone into over drive.
However, the results indicate that whatever the reason for the inefficiencies in our sample
the timing of the lowest mean score indicates the financial crisis may have had an impact
on the efficiency of building societies. This is especially important when you consider that
this paper takes a production approach, which depends on deposits as outputs. If we make
47. 47
the assumption that the financial crisis reduced total bank deposits, this would lower
overall cost efficiency as output would decrease relative to previous years (ceteris paribus).
In the last section of the analysis the regressions indicate that there is clear evidence that
CEO salary is correlated to efficiency; however, that said the evidence presented indicates
it is not a statistically significant determinant of CEO salary.
5. Concluding Remarks
Corporate governance and the associated theories have met a recent media backlash as
financial institutions, primarily consisting of banks attempt to explain why executives are
paid such a high salary. This high level of public exposure has not only exposed in many
cases a weak pay to performance relationship but a relationship which by many empirical
accounts has been analysed as statistically non-significant. By utilising an approach usually
overlooked in previous literature the empirical analysis in this paper indicates that the
relationship between efficiency and CEO salary is not as strong as thought in theory and as
indicated in previous research. However, we do find similarities to previous empirical
research in respect of size being a strong determinant of CEO salary; but it would be
interesting to see an analysis of how size determines executive pay when a different proxy
is used other than total assets in combination with a stochastic cost frontier score.
We have established that a statistically significant positive relationship exists between size
and executive salary in the U.K building society sector. But the evidence concerning the
relationship between efficiency and CEO salary is not so clear, as the regressions provide
48. 48
conflicting results. As this paper is the first of its kind14
in respect to examining a stochastic
frontier score in tandem with executive salaries in the mutual organisation sector there are
various ways to extend the analysis in this paper. There is a distinct possibility that a larger
sample set could provide a better overview of the relationship between efficiency and
executive pay, in addition, using different proxies could possible yield differing results.
Moreover, applying a comparatively advanced frontier technique in addition to analysing
more variables in respect to the estimation of the determinants of CEO salary may provide
a different outlook on the subject of efficient CEOs. Lastly, using more consistent and
accurate data which incorporates sensitive data which is currently difficult to obtain such
as the number of employees year on year and the number of employees off sick in a year
may provide an alternative approach which has yet to be fully utilised.
In order to reduce the unexplained variation within the dependent variable a regression
analysis which employs an increased number of independent variables may provide a more
accurate and consistent indication of the correlation between CEO pay and efficiency. For
example, this could be by including variables which account for variations in CEO age,
degree level and if the executive has a family to support.
Ultimately, this study finds that the cost efficiency of U.K building societies is more of less
in line with previous research and that size is a more important determinant of CEO salary
than firm cost efficiency. Consequently, the evidence presented in this paper finds
similarities with Ingham and Thompson (1995), where size has a significant influence on
executive salary. In addition, there are similarities between this study and other that
carried out by Worthington (1998) in respect to cost efficiency estimates. However, the
14
To the knowledge of the author.
49. 49
results obtained from the regressions provide estimates for a relationship (in respect to
using an SFA score) yet to be empirically tackled in the literature. It is evident that a more
detailed evaluation of the subject is required in order to provide evidence which yields a
more accurate description of the correlation between efficiency and CEO salary.
54. 54
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