2012 Cambridge Business & Economics Conference
ISBN : 9780974211428
Should Companies Increase Size or Improve their CSR Profile in order to Improve their
Performance?
Evidence from UK listed Firms
Iordanis Kalaitzoglou
Coventry University, Business School, Priory Street, Coventry CV1 5FB
e-mail: iordanis.kalaitzoglou@coventry.ac.uk, Tel: (+)44 (24) 7688 8418
Lanre Fagbe
Coventry University, Business School, Priory Street, Coventry CV1 5FB
e-mail: fagbel@coventry.ac.uk
Jacek Niklewski
Coventry University, Business School, Priory Street, Coventry CV1 5FB
e-mail: J.Niklewski@coventry.ac.uk, Tel: (+)44 (24) 7688 7187
June 27-28, 2012
Cambridge, UK
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2012 Cambridge Business & Economics Conference
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Should Companies Increase Size or Improve their CSR Profile in order to Improve their
Performance?
Evidence from UK listed Firms
ABSTRACT
This study proposes an empirical model to investigate the inter-relations among Corporate
Social Responsibility (CSR), Corporate Financial Performance (CFP) and size, while
accounting for liquidity and exposure to financial default risk. The analytical focus lies on
potential endogeneity issues, which are investigated employing a system of equations,
estimated using the Generalized Method of Moments (GMM) technique. A non-industryspecific sample is employed, consisting of 233 companies included in FTSE 250, from 2003 to
2010. In addition, a broad measure of CSR is employed to allow for generalization. The main
findings strongly indicate that UK market is sensitive to firms’ social profiles, which are found
to be endogenous related to both size and performance. CSR is asymmetrically related to
performance, where excessive or no investment in CSR are the most rewarding financially.
Larger and more profitable companies are more likely to invest in CSR, which in turns
contributes to further to both, probably due to increased visibility. Finally, higher borrowing
improves the financial profile, the size and the profitability of the companies in the pre-crisis
period, while it has a rather decreasing impact in the post-crisis period.
Key words: Corporate Social Responsibility (CSR), Corporate Financial Performance (CFP),
Generalized Method of Moments (GMM), Endogeneity, Simultaneous Equations.
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1. INTRODUCTION
After the introduction of Corporate Social Responsibility (CSR) scheme in 2000 and its
disclosure requirement, the impact of corporate profile on the value of listed companies is
undeniable.i A positively regarded company, at least compared to its competitors, significantly
increases its intangible assets. Cohen et al. (2011) emphasizes the importance of non-financial
information on the long term. However, this definitely comes at a cost, either in the form of
excessive liabilities or as a decrease in other assets. In addition, the funds required to create and
sustain a socially responsible corporate profile are deprived from other investments that could
create economic value. Therefore, the question that arises naturally in the literature (Barney
(1991) & Barney et al. (2001) & Bragdon and Marlin (1972) & Moskowitz (1972) & Vance
(1975)) is whether the value of these intangible assets is marginally higher than the marginal cost
of their acquisition in the form of opportunity costs in order to enhance performance and create
value.
Naturally, the interest has focused on the sign and the direction of the relationship between CSR
and performance.ii Several studies have empirically tested this relationship, providing conflicting
results, both in terms of the sign and direction of causality, as well as in terms of market stylized
facts. A significant branch of literature (Anderson and Frankle (1980) & Belkaoui (1976) &
Bowman (1978) & Fry and Hock (1976) & Preston (1978)) reports that CSR has a positive
impact on performance. According to Solomon and Hanson (1985), the cost of CSR is
outweighed by the benefit from employee morale and productivity. On the contrary, several
studies (Aupperle et al. (1985) & Freedman and Jaggi (1982) & Ingram and Frazier (1980))
argue that the marginal costs associated with CSR are higher than the benefits and therefore
reduce firm’s performance.
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Brammer and Millington (2008) maintain that these contradictions arise because of a non linear
relationship. They purport that the positive association between CSR and performance follows
diminishing and decreasing returns. Consequently, if the scope of social responsibility
participation strays beyond the management in addressing the social concerns (e.g., with little or
no impact with relation to stakeholders of the firm), the net effect is declining financial
performance. They also argue that the correlation between CSR and performance is highest at the
extremes, showing that financial performance is high at both very high and very low CSR levels.
This is consistent with Porter (1980) who supports that low cost or differentiated strategies
outperform mediocre approaches, as well as with Bhattacharya and Sen (2004) who show that
price sensitive customers are not ready to pay the higher prices of more socially active
responsible firms. However, these studies, although they partially explain variations in CSRperformance relationship, they do not examine potential endogeneity issues.
Bragdon and Marlin (1972) Bowman and Haire (1975) and Heinz (1976) report that companies
with higher performance invest more in CSR. More precisely, Bowman and Haire (1975)
maintain that the U-shaped relationship holds on the opposite direction as well. However, their
results are industry specific and they focus only on particular aspects of CSR, such as pollution
or environmental index.iii According to Dean (1998) and Waddock and Graves (1997) there are
two main approaches in describing the direction of causality between CSR and CFP. iv The slack
resource hypothesis states that a company ought to maintain and sustain its financial
performance first, before thinking of engaging into social responsibilities. The good management
hypothesis supports that a company that is perceived to have good reputation and better
association with its non financial stakeholders will find it easier to perform well. This is
confirmed by Jackson and Apostolakou (2010) who show that CSR is now used as a strategic
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tool to boost performance in Western Europe. This argument focuses on a marketing perspective,
considering CSR as an intangible asset that impacts and enhances performance (Hillman and
Keim (2001) & Orlitzky et al., (2003)). Surroca et al. (2010) provide evidence of endogeneity
between CSR and performance, but it is rather direct through intangible assets of innovation,
human capital, reputation and culture. This is a key idea further examined in this paper. In
contrast, Makni et al. (2009) provide evidence of no significant association. Waddock and
Graves (1997) maintain that financial performance and CSR are synergetic and that their interrelation is rather an empirical issue.
Another branch of literature focuses on the relation between size and CSR. Chih et al., (2010)
purports that firms with larger size tend to invest more in CSR, especially where there is an
intrinsic value that increases their competitive advantage. Dierkes and Coppock (1978), Fombrun
and Shanley (1990), Trotman and Bradley (1981) provide evidence of positive relationship
between size and CSR, corporate reputation index. Watts and Zimmerman (1986) postulate that
large firms are likely to be more affected by CSR performance impacts due to increased
visibility. Adams et al. (1998), Clarke and Gibson-Sweet (1999), Gray et al. (1995) and Ness and
Mirza (1991) argue that size related costs reduce by CSR disclosure, or that CSR disclosure is
the outcome of increased social pressure (Aguilera et al. 2007). In addition, Orlitzky (2001)
investigates whether companies that embrace CSR increase in size, suggesting an endogenous
relationship between size, performance and CSR. In contrast, Stanwick and Stanwick (1998)
maintain that size is exogenous to both CSR and performance. Furthermore, Artiach et al. (2010)
investigate whether leverage is related to CSR and report that firms will prioritize financial
stakeholder claims over social stakeholders. Therefore, highly leveraged firms should be less
likely to improve their CSR profiles, even though it would further decrease their tax liability.
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Most of the studies have been industry specific and that has also contributed to the
inconsistencies in the relationship between corporate social responsibility and performance
(Rowley and Berman (2000)). Empirical studies (e.g., Gray et al. (1995), (2001) & Hackston and
Milne (1996)) have shown that CSR disclosure activism varies across companies, industries, and
time. CSR is determined by the industry and firm characteristics (Reverte (2009)), while firms
operating in the same industry develop common practices. In addition, Higgins and Currie
(2004) argue that corporate social responsibility can be driven by ethical and legal matters that
cannot be accurately measured.v Chih et al. (2010) and Margolis and Walsh (2003) suggest a
parsimonious and an extended model, respectively. They provide contradicting results,
confirming Ullmann (1985), who suggests that the relationship between CSR and performance of
the firm is so complex and there is currently no true model that exhibits this relationship.
Consequently, this study does not aim at an exhaustive investigation of the concept of CSR and
its impact on financial performance. The analytical focus lies on the empirical investigation of
the inter-relations between CSR, financial performance and size, on a non-industry-specific
setting, defining CSR multi-dimensionally.vi In more detail, an empirical model of simultaneous
equations is proposed, to empirically examine a non-asymmetric impact of CSR on performance.
An endogenous relationship between CSR and performance is also investigated, taking into
account variant effects due to size of the company. The sample selected, consists of 233
companies, included in FTSE 250 from 2003 till 2010. This constitutes a non-industry-specific
setting that allows for generalization. Finally, a wider measure of CSR is employed in order to
avoid sector-bias.
The findings strongly indicate that UK market is sensitive to companies’ social profiles, to cost
efficiency, and that CSR activities, performance and size are endogenously related. Companies
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that are highly ranked in CSR index and companies that do not embrace CSR policies,
consistently perform better than companies that are in between. This is further enhanced when
companies are already performing well, probably because their liquidity allows them to invest
more in CSR activities. In addition, large companies are found to invest more in CSR and they
further increase their size because of a positive CSR impact. Large companies enjoy higher
visibility and they are found to be more influenced by their social profiles. Finally, higher
exposure to default risk seems to have a negative impact on CSR investments and consequently
on profitability. Excessive borrowing, prioritize financial stakeholders’ needs over social
stakeholders’ interests, and this seems to be a crucial factor in the UK, which is found to be
socially sensitive market.
The remainder of this paper is organized as follows. The following section presents the data
collection process and the methodology employed. Empirical findings are discussed in Section 3,
while Section 4 concludes.
2. METHODOLOGY
2.1. Data
The aim of this study is to investigate the relationship between CSR, performance, size and
leverage in UK companies. According to the literature, these inter-relations are more profound in
large companies, due to increased visibility. Therefore, the sample employed, consist of all
companies included in the FTSE 250 in the years from 2003 to 2010, subjected to availability of
data and continuity of operation. The data set consist of 1762 observations. The variables under
investigation are Return on Equity, Total Assets, liquidity, Debt to equity ratio and the CSR
ranking on annual basis.vii
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2.2. Model
Foundations of the model
Literature poses five key issues that are relevant to this study. First, the impact of CSR on CFP is
extensively investigated, and empirical evidence suggests that the importance depends on
country specific factors (see inter alia, Anderson and Frankle (1980) & Belkaoui (1976) &
Bowman (1978) & Fry and Hock (1976) & Preston (1978)). Second, CSR might asymmetrically
affect CFP (Bowman and Haire (1975)). Profitability is higher in cost efficient companies that do
not invest in CSR activity, or in companies that score high in CSR indexes. In contrast, a
mediocre approach seems is rather ineffective. Third, there is no clear indication on the direction
of the relation, which again is found to be sample depended (Surroca et al. (2010)). Fourth, the
impact of size on CSR and the direction of their relationship is not extensively incorporated and
has not yet been studied simultaneously (Chih et al. (2010)). Finally, the relationship between
financial exposure (i.e., excessive lending) should further be investigated (Artiach et al. (2010)).
The model below, proposes an easy and flexible way to investigate the above issues. It provides
the framework for an empirical investigation of the sign, the direction and the inter-relation of
key variables related to CSR. It can also be further extended by introducing other factors that
affect each individual relationship, or factors that might be endogenously related. In addition, it
can be applied in different settings, either more specific (e.g., industry, sector, country), or more
general (e.g., comparison between sectors, countries).
The Model
The econometric model proposed, can be summarized in the following three equations:
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3
𝑅𝑂𝐸𝑖𝑡 = 𝛼0 + 𝛼1 𝑆𝐼𝑍𝐸𝑖𝑡 + ∑(𝛼2,𝑞 ∗ 𝐼𝑞,𝑖,𝑡 ) ∗ 𝐶𝑆𝑅𝑖𝑡 + 𝛼3 𝐿𝐸𝑉𝑖𝑡 + 𝛼4 𝐿𝑖𝑞𝑖𝑡 + 𝜀1𝑡
(1)
𝑞=1
𝐶𝑆𝑅𝑖𝑡 = 𝛽0 + 𝛽1 𝑅𝑂𝐸𝑖𝑡 + 𝛽2 𝑆𝐼𝑍𝐸𝑖𝑡 + 𝛽3 𝐿𝐸𝑉𝑖𝑡 + 𝛽4 𝐿𝑖𝑞𝑖𝑡 + 𝜀2𝑡
(2)
𝑆𝐼𝑍𝐸𝑖𝑡 = 𝜇0 + 𝜇1 𝑅𝑂𝐸𝑖𝑡 + 𝜇2 𝐶𝑆𝑅𝑖𝑡 + 𝜇3 𝐿𝐸𝑉𝑖𝑡 + 𝜇4 𝐿𝑖𝑞𝑖𝑡 + 𝜀3𝑡
(3)
where, α’s, β’s and μ’s are parameters to be estimated, 𝑖 = 1,2, … , 𝑛 refers to companies and t
refers to time. 𝐶𝑆𝑅𝑖𝑡 is the corporate social responsibility and is calculated as [
100−𝜆+1
100
], where 𝜆
is the rank number for the i-th firm at time t.viii 𝑅𝑂𝐸𝑖𝑡 is the return on equity, computed as net
income over total equity, 𝑆𝐼𝑍𝐸𝑖𝑡 is the natural logarithm of total assets, 𝐿𝑖𝑞𝑖𝑡 is liquidity,
measured as total assets turnover and 𝐿𝐸𝑉𝑖𝑡 is leverage, measured by the Debt to Equity ratio, for
firm i and at time t. 𝜀𝑝,𝑡 𝑝 = 1,2,3 is the error term. Then I is a dummy variable that
differentiates the level of CSR engagement and 𝑞 = 1,2,3
𝐼1,𝑖,𝑡 = {
𝐼2,𝑖,𝑡 = {
1
0
𝑤ℎ𝑒𝑛 𝐶𝑆𝑅𝑖,𝑡 = 0
𝑒𝑙𝑠𝑒𝑤ℎ𝑒𝑟𝑒
(4)
1 𝑤ℎ𝑒𝑛 0 < 𝐶𝑆𝑅𝑖,𝑡 ≤ 50%
0
𝑒𝑙𝑠𝑒𝑤ℎ𝑒𝑟𝑒
(5)
1 𝑤ℎ𝑒𝑛 𝐶𝑆𝑅𝑖,𝑡 > 50%
0
𝑒𝑙𝑠𝑒𝑤ℎ𝑒𝑟𝑒
(6)
𝐼3,𝑖,𝑡 = {
This model examines the following issues. First, Eq. (1) investigates a potential asymmetric
impact of CSR on performance. Coefficient 𝛼2,1 captures the impact of non CSR policies on
performance. A positive estimated would indicate that companies that do not engage on CSR
policies perform better because of decreased costs. Coefficient 𝛼2,2 captures the impact of CSR
on CFP when companies embrace CSR practices, but it is not central to their financial
management. A negative coefficient would indicate negative relation to CFP. Coefficient 𝛼2,3
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captures the impact of CSR on CFP when the social profile is a main strategic objective for the
company. In Eq. (1) and Eq.(2) Coefficients 𝛼2,𝑞 and 𝛽1 investigate whether there is any
endogenous relationship between CSR and CFP. Coefficients 𝛽2 and 𝛽3 capture the impact of
Size and Leverage on CSR. Eq. (3) examines potential endogeneity among CSR, CFP and Size,
captured in coefficients 𝜇1 and 𝜇2 .
The method chosen for estimation is the Generalized Method of Moments (GMM). In this
method, the selection of appropriate orthogonality conditions is essential. ix
3. EMPIRICAL RESULTS
Section 3 presents the empirical findings. Section 3.1 in particular focuses on non-parametric
analysis, while section 3.2 discusses the estimation results.
3.1. Non Parametric Analysis
UK is leading the initiative of incorporating CSR into strategic planning of the company, in order
to increase the value of intangible assets. The contribution of the country to the total CSR
activities is increasing over the years.
----------------------------Figure 1: about here
----------------------------Figure 1 presents the number of FTSE 250 companies that are included in the BITC index among
the best 100 companies in terms of CSR policies. The number of UK companies that are
included constantly (e.g., from 83 companies in 2003 to 91 companies in 2010) increases and it
dominates the list. This high figure is an initial indication that UK is a sensitive market towards
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corporate social profile. In addition, intangible assets appear to be an important part of corporate
investments and that social responsibility is an integral part of financial management strategy.
----------------------------Figure 2: about here
----------------------------Figure 2 shows the best performing sectors in the UK in terms of CSR. The individual bars
reveal that the best performing sectors are the Energy, Financial Services, Oil and gas and Real
estate, with a 13%, 13%, 8% and 8% respectively. Furthermore, this figure shows that companies
that are negatively regarded, due to various reasons, they try to compensate the negative impact
with socially responsible policies. This is better shown in Figure 3.
----------------------------Figure 3: about here
----------------------------Figure 3 dissects the CSR performance of each sector into Community, Environment,
Marketplace and Workplace activities. It is becoming obvious that each sector invests on
improving its negatively regarded side, or to enhance its competitive advantage. For example, the
Energy and the Mining sectors perform really well in Environment and Community. This way
these companies probably try to improve their profile towards their major drawback, which is
their environmental impact. In contrast, General retailers invest more on improving their
network. They try to improve on the Community and Marketplace aspects of their activity. That
could improve an aspect of their corporate activity that they already perform well.
----------------------------Table 1: about here
--------------------------------------------------------Table 2: about here
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Furthermore, Table 1 and Table 2 report the basic descriptive statistics and the correlation of the
variables employed in this study, respectively. In more detail Table 1 presents the median, the
standard deviation, the mean, the quartiles and the minimum and maximum values of each
variable over the years 2003-2008. One of the most profound results is that ROE consistently
increases over the years, with an exemption of 2008 and 2009, when the collapse of Lehman
Brothers triggered the current financial crisis. The average value is 18.69% in 2003 and it
reaches 30.37% in 2010. However, this increase is followed by an increase (e.g., 54.50 in 2003
and 90.03 in 2010) in standard deviation as well. ROE appears to be over-dispersed (standard
deviation is larger than the mean), and this effect worsens in 2008 (standard deviation is 84.51,
compared to a mean of 9.34%).
D/E ratio appears to be over-dispersed as well, and this becomes more obvious after 2008. The
average value significantly increases over the years, especially after 2008. Given that financial
institutions have been more reluctant to lend, this increase has probably been caused by a shrink
of equity capital. Total assets consistently increase over the years and, similar to all other
variables, standard deviation is larger than the mean (e.g., mean £860,614 and std £1,058,515 in
2003 and mean £2,064,216 and std £7,977,155 in 2010). In contrast, Liquidity decreases over the
years, especially after 2008, while volatility does not significantly change (e.g., mean 1.92 std
5.58 in 2003 and mean 1.80 std 3.16 in 2010). Table 3 fails to report a significant correlation
(i.e., in a degree that would cause multicollinearity problems) between the variables employed.
3.2. Estimation Results
----------------------------Table 3: about here
-----------------------------
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Finally, Table 3 presents the estimation results for the simultaneous equations model in Eqs (1),
(2) and (3). The first panel reports the estimates, along with the associated t-statistics. The
bottom panel reports the Adj.-R2 and the J-stats, along with the associated p-values.
The estimates of Eq. (1), presented in the first column, reveal that CSR, Leverage and Liquidity
have a significant impact on performance, while size fails to provide statistically significant
results. In more detail, CSR appears to have an asymmetric effect on performance, similar to
Bhattacharya and Sen (2004), Bowman and Haire (1975) and Brammer and Millington (2008).
CSR appears to have a positive impact on performance when companies embrace social
responsibility in their core strategies (0.3548, (3.92)) and when they ignore it (0.8357, (4.78)). A
mediocre approach seems to have a negative (-0.1785), marginally significant (-1.97) impact on
CFP. In addition, High D/E ratios seem to have a negative (-0.0692, (-4.08)) impact on
performance. However, that can be compensated if they lead to higher liquidity, which is found
to positively (0.0673, (2.54)) affect performance.
The second column reports the estimates of Eq. (2). A profound finding here is the bidirectional
relationship between CSR and CFP, which is consistent with Surroca et al. (2010). Coefficient β1
is positive (0.6636) and statistically significant (4.59). This means that, not only CSR increases
CFP but, performance has a positive influence on CSR. Similar to Bowman and Haire (1975),
Bragdon and Marlin (1972) and Heinz (1976) the investment in CSR depends on the profitability
levels of the company. Firms that generate more cash can afford higher investments in CSR. This
is consistent with Liquidity as well. An estimate of 0.7868 (2.73) shows that higher liquidity
provides more opportunities for further investments in CSR. According to the findings from Eq.
(1), this results in better performance. Consequently, liquidity is found to have an direct (Eq.
(1)), as well as an indirect (Eq. (2)) positive impact on performance.
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In addition, larger firms tend to invest more in CSR. The positive estimate of 0.0566 (3.40)
shows that companies with higher total assets, consistently perform better in CSR and the score
higher in the comparative analysis. This is consistent with Chih et al. (2010), Dierkes and
Coppock (1978), Fombrun and Shanley (1990) and Trotman and Bradley (1981), who provide
evidence of positive relation and opposes to Stanwick and Stanwick (1998). However,
considering the empirical findings in Eq. (1), large companies invest more in CSR and this
increased investment further increases their profitability. Consequently, Size has an indirect
positive impact on performance, although no direct impact can be identified. This finding is to be
expected, since large companies have increased visibility, and according to Watts and
Zimmerman (1986) are affected more by CSR. Therefore, they need to actively improve their
social profile.
A closer inspection of Table 3, and especially on the third column, where the estimation results
for Eq. (3) are presented, reveals an endogenous relationship of Size with CSR and performance.
Similar to Orlitzky (2001), both CFP (0.4956, (5.13)) and CSR (4.22, (6.91)) are found to
increase the size of the company, but as Eq. (2) shows, size directly increases CSR and indirectly
CFP. This adds to Ullmann (1985) who argues that the relation between CSR and performance is
complex and it is rather an empirical issue. Consequently, large companies are more likely to
invest in CSR. This investment increases performance, which increases the size of the firm (CSR
increases Size both directly and indirectly through CFP). This way, firms that improve their
social profile, seem to create an intangible asset that increases the value of the company. This is
a strong indication of increased sensitivity of the market towards CSR.
However, that does not appear to hold when the firm is heavily financed using external
financing. Excessive borrowing seems to have a negative influence on both financial
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performance and CSR (-0.0213, (-2.83)). In consistence with Artiach et al. (2010) highly
leveraged firms should be less likely to improve their CSR profiles, even though it would further
decrease their tax liability. According to Pecking Order theory financial shareholders’ needs are
prioritized over social stakeholders’ interests. Consequently, excessive borrowing will decrease
the value of intangible assets and CFP and indirectly the size of the company.
Table 4 provides a further insight on the previous findings. The first panel reports the estimates
of the models for the period 2003-2007, which corresponds to the pre-crisis period. The second
panel reports the estimates for the post-crisis period, i.e. 2008-2010. One of the most profound
findings is the reverse relation among leverage, size and profitability before and after the
financial crisis following the collapse of Lehman Brothers in October 2008. A positive estimate
(0.0124 (2.05)) shows that size has an increasing impact on profitability before 2008, revealing
that larger companies could return higher profits to their shareholders. It appears that this was
backed up by higher D/E ratios, since leverage seems to have an increasing effect on profitability
(0.0161 (2.13)) and on size (0.0149 (2.85)). Companies appear to keep higher borrowing levels,
which assists them in increasing their size and their profitability. On the contrary, in the period
after 2008, leverage has a reverse impact. Companies that maintain higher D/E ratios appear to
have lower profitability (-0.0772 (-4.11)), probably because of higher default risk. Although
more profitable companies increase (0.4890 (4.99)) in size, larger companies do not appear to be
more profitable.
CSR policies appear to be affected as well. Following previous findings, before 2008, larger
(0.1051 (3.49)), more profitable (0.8076 (5.49)) companies invest more in CSR activities,
especially when they maintain high liquidity ratios (0.7764 (2.77)). Leverage further encourages
(0.0012 (2.03)) improvements in CSR profiles, which in turn have a significant, positive impact
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on size (4.77 (7.01)). Furthermore, the impact of CSR on performance appears to be non-linear
and companies with significant resources invested in CSR appear to be significantly more
profitable (0.6134 (4.17)). In contrast, companies with little or no engagement perform better
(0.6315 (2.88)) than companies with mediocre policies (-0.1707 (-1.84)). In the period following
the financial crisis, this relation weakens. Profitability is improved by significant CSR
investments, but the impact is considerably smaller (0.2745 (2.45)). Companies with high D/E
ratios reduce their investments in CSR (-0.1017 (-2.89)), probably due to higher financial
distress, while companies that base their operations in high Assets Turnover, such as retailers,
invest more (0.8011 (2.74), compared to 0.7764 (2.77)). Large (0.0531 (2.40)) and more
profitable (0.3963 (3.19)) companies continue to invest in CSR activities, but their engagement
seems to be constraint by higher financial costs. In contrast, companies that are not engaged in
CSR activities appear to significantly outperform (0.8736 (5.02)) their counterparts, probably
due to more cost efficient operations. However, higher investments in CSR still increases the size
of operations (2.9500 (4.44)), but not as much as it used to before 2008.
4. CONCLUSION
This study focuses on the potentially endogenous relationship among CSR, financial
performance and Size, on a general setting that includes companies from FTSE 250 for a period
of 8 years (i.e., 2003-2010). A broad measure of CSR is used to account for all major aspects of
social profiles. Further, the inter-relations are investigated using a system of equations that are
estimated using the Generalized Method of Moments (GMM) method. This way the model
addresses the following issues. First, the impact of CSR on CFP is examined. Second, the
direction of the relationship (if any) is investigated. Third, the model accounts for size, which is
allowed to endogenously affect both CSR and CFP. Finally, the impact of excessive borrowing
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on performance, CSR and Size is taken into account. The proposed model, suggests an easy and
flexible way to investigate the above issues. It provides the framework for an empirical
investigation of the sign, the direction and the inter-relation of key variables related to CSR. It
can also be further extended by introducing other factors that affect each individual relationship,
or factors that might be endogenously related. In addition, it can be applied in different settings,
either more specific (e.g., industry, sector, country), or more general (e.g., comparison between
sectors, countries).
The most profound finding is that UK market is very sensitive to CSR activities. Listed
companies appear to be exposed to public and slight changes in their social profiles can
significantly affect their performance and size, by creating or diminishing the value of intangible
assets. In more detail large, profitable companies are more likely to invest in CSR activities,
probably due to increased visibility, especially before the financial crisis in 2008. This
investment increases significantly financial performance and size, indicating that these figures
are endogenously related. This is a strong indication of market sensitivity towards CSR. This
sensitivity decreases in the post-crisis period, but it is still present. In contrast, companies that do
not engage in CSR activities seem to be more cost efficient and thus more profitable. This
relation is stronger in the post-crisis period. Mediocre policies appear to be rather insignificant
and negative. Finally, excessive borrowing is found to increase size, investment in CSR and
performance before 2008, but it negatively affects investments in CSR and performance in the
post crisis period. This indirectly results in size limitations, even if it reduces tax liabilities.
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APPENDIX
Table 1: Basic statistics
2010
Median
Std
Average
First quartile
Second quartile
Third quartile
Lower Limit
Upper Limit
ROE
13.55
90.03
30.37
4.30
13.55
27.65
-27.10
991.34
D/E
48.94
116.02
83.16
16.42
48.94
100.23
0.62
872.14
TA
947,859
7,977,155
2,064,216
509,150
947,859
1,758,587
50,743
118,057,000
Liq
1.00
3.16
1.80
0.68
1.00
1.65
0.03
38.34
ROE
D/E
TA
Liq
Median
Std
Average
First quartile
Second quartile
Third quartile
Lower Limit
Upper Limit
10.62
84.51
9.34
-14.23
10.62
29.29
-540.01
556.99
64.40
144.64
109.96
23.36
64.40
136.70
0.00
949.26
764,900
3,167,243
1,640,586
423,275
764,900
1,731,553
40,090
37,614,000
1.04
7.52
2.93
0.69
1.04
1.70
0.04
80.23
2006
Median
Std
Average
First quartile
Second quartile
Third quartile
Lower Limit
Upper Limit
ROE
23.45
65.79
33.69
14.60
23.45
32.89
-64.64
564.29
D/E
51.79
123.25
93.44
15.75
51.79
114.52
0.05
702.66
TA
663,198
1,761,452
1,212,301
295,190
663,198
1,430,750
0
13,434,000
2004
Median
Std
Average
First quartile
Second quartile
Third quartile
Lower Limit
Upper Limit
ROE
18.32
36.56
18.50
9.15
18.32
27.58
-274.60
198.26
D/E
43.88
108.23
82.72
14.88
43.88
114.60
0.08
725.69
TA
539,148
1,130,223
902,247
210,738
539,148
1,176,678
0
7,913,600
2008
2009
Median
Std
Average
First quartile
Second quartile
Third quartile
Lower Limit
Upper Limit
ROE
14.98
31.56
16.30
3.28
14.98
26.84
-132.40
190.96
D/E
51.37
147.31
104.88
18.43
51.37
122.26
0.07
995.86
TA
809,165
5,970,027
1,829,543
444,947
809,165
1,720,718
40,500
86,194,000
Liq
1.03
7.80
2.84
0.66
1.03
1.80
0.03
83.19
ROE
D/E
TA
Liq
Median
Std
Average
First quartile
Second quartile
Third quartile
Lower Limit
Upper Limit
21.22
67.51
25.24
9.51
21.22
34.48
-692.60
354.23
56.96
129.83
96.11
16.40
56.96
115.33
0.04
815.28
747,007
2,310,039
1,483,071
401,279
747,007
1,496,850
34,221
19,553,000
1.04
8.33
2.81
0.70
1.04
1.64
0.12
76.88
Liq
1.10
6.56
2.37
0.73
1.10
1.72
0.03
78.26
2005
Median
Std
Average
First quartile
Second quartile
Third quartile
Lower Limit
Upper Limit
ROE
21.94
37.19
25.20
12.42
21.94
31.76
-182.24
289.19
D/E
64.69
132.88
106.11
17.41
64.69
140.73
0.02
659.20
TA
630,910
1,572,833
1,094,005
228,590
630,910
1,418,300
0
12,825,000
Liq
1.09
5.93
2.39
0.80
1.09
1.81
0.06
66.43
Liq
1.16
6.31
2.03
0.71
1.16
1.64
0.04
81.18
2003
Median
Std
Average
First quartile
Second quartile
Third quartile
Lower Limit
Upper Limit
ROE
18.62
54.50
18.96
6.09
18.62
29.31
-157.74
617.78
D/E
50.81
104.76
79.71
13.10
50.81
106.11
0.13
812.16
TA
501,906
1,058,515
860,614
205,395
501,906
1,064,900
4,979
6,651,700
Liq
1.07
5.58
1.92
0.67
1.07
1.53
0.10
67.47
2007
Table 1 presents the basic statistics for the variables employed. In more detail, the median, the standard
deviation, the mean the quartiles, the maximum and the minimum values are reported. The table is
dissected in 8 sections, one for each year.
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Table 2: Correlation matrix
Lev
1
ROE
0.473
1
Size
0.0036
0.0799
1
Liq
0.1304
0.231
-0.3019
1
Lev
ROE
Size
Liq
Table 3: Regression results-full sample
Full Sample
ROE
α0
size
α1
csr
α 2,1
α 2,2
α 2,3
lev
α3
liq
α4
J-stats
0.0512
(0.17)
-0.0072
(-0.55)
0.8357
(4.78)
-0.1785
(-1.97)
0.3548
(3.92)
-0.0692
(-4.08)
0.0673
(2.54)
8.32
(0.22)
CSR
β0
roe
β1
size
β2
lev
β3
liq
β4
0.0768
(2.01)
0.6636
(4.59)
0.0566
(3.40)
-0.0213
(-2.83)
0.7868
(2.73)
SIZE
μ0
roe
μ1
csr
μ2
lev
μ3
liq
μ4
2.4551
(3.66)
0.4956
(5.13)
4.22
(6.91)
-0.0482
(-0.29)
0.3008
(0.83)
Table 3 presents the estimation results for the model in Eqs (1), (2) and (3) for the full sample. The first
section presents the estimates for the parameters. The values in parentheses are t-statistics. The bottom
section reports the Adjusted-R-squared and the J-statistics. The values in parentheses are the associated pvalues.
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Table 4: Regression results-before and after 2008
Before 2008
ROE
α0
size
α1
csr
α 2,1
α 2,2
α 2,3
lev
α3
liq
α4
J-stats
0.0491
(1.94)
0.0124
(2.05)
0.6315
(2.88)
-0.1707
(-1.84)
0.6134
(4.17)
0.0161
(2.13)
0.0709
(2.58)
8.41
(0.21)
CSR
β0
roe
β1
size
β2
lev
β3
liq
β4
0.0809
(2.11)
0.8076
(5.49)
0.1051
(3.49)
0.0012
(2.03)
0.7764
(2.77)
SIZE
μ0
roe
μ1
csr
μ2
lev
μ3
liq
μ4
2.0755
(3.05)
0.5017
(5.01)
4.77
(7.01)
0.0149
(2.85)
0.2930
(0.88)
After 2008
ROE
α0
size
α1
csr
α 2,1
α 2,2
α 2,3
lev
α3
liq
α4
J-stats
0.0542
(0.10)
-0.0047
(-0.51)
0.8736
(5.02)
-0.1815
(-1.98)
0.2745
(2.32)
-0.0772
(-4.11)
0.0666
(3.15)
8.30
(0.22)
CSR
β0
roe
β1
size
β2
lev
β3
liq
β4
0.0692
(1.81)
0.3963
(3.19)
0.0531
(2.40)
-0.1017
(-2.89)
0.8011
(2.74)
SIZE
μ0
roe
μ1
csr
μ2
lev
μ3
liq
μ4
2.6146
(3.84)
0.4890
(4.99)
2.9500
(4.44)
-0.0242
(-0.22)
0.3534
(0.99)
Table 4 presents the estimation results for the model in Eqs (1), (2) and (3) for two sub-samples. The first
panel (Before 2008) reports the estimates of the models for the period 2003-2007, while the second panel
for the period 2008-2010). The first section of each panel presents the estimates for the parameters. The
values in parentheses are t-statistics. The bottom section reports the Adjusted-R-squared and the Jstatistics. The values in parentheses are the associated p-values.
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Figure 1: Number of FTSE 250 companies in the BITC index
Number of FTSE 250 companies in BITC index
100
90
80
70
60
50
40
30
20
10
0
2003
2004
2005
2006
2007
2008
2009
2010
Figure 1 shows the number of companies included in FTSE 250 that they are ranked among the first 100
companies that have embraced CSR practices, according to BITC index.
Figure 2: Top 10 sectors in CSR
Top 10 sectors in CSR
Level of participation
14%
12%
10%
8%
6%
4%
2%
0%
Sector
Figure 2 shows the 10 best performing sectors in terms of CSR performance, according to BITC index.
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Figure 3: Top 10 Sectors in CSR detailed
Performance
Top 10 Sectors-Detailed
20%
18%
16%
14%
12%
10%
8%
6%
4%
2%
0%
Community
Environment
Marketplace
Workplace
Figure 3 shows detailed CSR activities per sector, for the top 10 sectors in CSR, according to BITC index
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ISBN : 9780974211428
ENDNOTES

Corresponding author.
i
CSR is used to measure how a firm embraces economic, environmental, social and governance factors into its
operations and society at large. Godfrey and Hatch (2007) defines CSR as a concept of integrating social and
environmental concerns in the business operations and their stakeholders on a voluntary basis.
United Kingdom has been chosen because the European business network for CSR reports that 38% of CSR
Europe’s web users identified the United Kingdom as the most progressive country in terms of CSR initiatives
(followed by Sweden with 11%, the Netherlands with 7% and Denmark with only 6%) Source: <http://
www.csreurope.org/press_releases.php?action=show_press_release&press_id=5> [20.08.11]
ii
The majority of studies (Bragdon and Marlin (1972) & Heinz (1976) & Moskowitz (1972) & Parket and Eilbirt
(1975) & Vance (1975)) uses Return on Equity (ROE) to measure performance, as opposes to Tobin’s Q and Market
to Book ratio, which measure market value.
However, several studies, although they examine the relationship between social corporate responsibility and
performance (Coombs and Gilley (2005) & Griffin and Mahon (1997) & Hillman and Keim (2001) & McWilliams
and Siegel (2000), (2001) & Pava and Krausz (1996) & Roberts and Dowling (2002) & Rowley and Berman (2000)
& Simpson and Kohers (2002) & Waddock and Graves (1997) & Walsh et al. (2003)), Jones (1999) argues that they
fail to systematically address the fundamental determinants of social responsibility.
The index reported by “The Business in the Community” covers all aspects of social responsibility, such as
community investments and noise pollution, and it is not restricted in terms of industrial or geographical aspects.
iii
iv
Six main approaches have been developed. According to the Social Impact Hypothesis (Freeman (1984) & Jones
(1995)), if a firm satisfies the needs and demands of its corporate stakeholders, a favourable financial performance
will follow. According to the Slack Resource Hypothesis (Waddock and Graves (1997)), the direction is exactly the
opposite, because only a company that generates profits can further invest in its social profile improvement. On the
contrary according to the Trade-off Hypothesis increased CSR investments shrink investors wealth and therefore
result in limited performance. Similar, according to Managerial Opportunism Hypothesis (Weidenbaum and Vogt
(1987) & Willliamson (1967), (1985)), firms only invest in improving their social profile when they do not have
other valuable intangible assets. Therefore a high performance negatively affects CSR. On the contrary the Positive
(Allouche and Laroche (2005) & Waddock and Graves (1997)) and the Negative (Friedman (1962), (1970)) Synergy
hypothesis recognize the endogenous relationship between CSR and performance, without though stating the facts
that determine the positive or negative sign.
v
Jauch et al. (1980) and Tan and Litschert (1994) who assert that the institutional environment alone is becoming
broader and more close to the stakeholder concept. It involves components like competitors, customers, suppliers,
technology, regulatory bodies, economics, social-cultural and international.
vi
CSR is measured using the Business In The Community (BITC) index, which provides a ranking according to
company’s engagement in various aspects of social responsibility.
The data bases used are “FAME” and Business In The Community (BITC). BITC Corporate Responsibility Index
is the leading UK voluntary benchmark for corporate responsibility. BITC Index was established in 1982 and now
has 830 members. The index is a voluntary and self assessment survey providing an annual benchmark on the
management, measurement and reporting of firms’ corporate responsibility. The index uses ranking from 1 to 100
where 1 denotes the worst corporate social responsibility attribution and values close to 1 denotes lower levels of
CSR performance. A rank of 100 shows the best exhibition of corporate social responsibility.
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For example, a firm with a rank of 1, which is the best result, has a CSR of 100. The higher the ranking, the
higher is the CSR Index. All companies that are not in the BITC index, are treated as being the last in the list.
Consequently they have a score 𝐶𝑆𝑅 = 1.
June 27-28, 2012
Cambridge, UK
28
2012 Cambridge Business & Economics Conference
ix
ISBN : 9780974211428
′
First, let 𝛽 = (𝛼𝑚,𝑞 , 𝛽𝑚 , 𝜇𝑚 ) , where 𝑚 = 0, … ,4 and 𝑞 = 1,2, 3 in the special case of 𝑎2 , be a vector of the
𝜐
̂𝑡 , 𝐿𝑒𝑣
̂ 𝑡 , 𝐿𝑖𝑞
̂ 𝑡 , 𝐼𝑞,𝑡 ∗
parameters to be estimated, 𝜐𝜄,𝑡 a vector of all variables of company 𝑖 at time 𝑡, 𝑧1,𝑡
= (𝑆𝑖𝑧𝑒
′
′
𝜐
̂𝑡 , 𝐿𝑒𝑣
̂𝑡 ) is a vector of
̂ 𝑡 , 𝐿𝑖𝑞
̂ 𝑡 , 𝑅𝑂𝐸
𝐶𝑆𝑅𝑡 ) is a vector of the instrumental variables for Eq. (1), 𝑧2,𝑡
= (𝑆𝑖𝑧𝑒
𝜐
̂𝑡 , 𝐿𝑒𝑣
̂ 𝑡 , 𝐿𝑖𝑞
̂ 𝑡 , 𝐶𝑆𝑅𝑡 )′ is a vector of instrumental variables for Eq.
instrumental variables for Eq. (2) and 𝑧3,𝑡
= (𝑅𝑂𝐸
(3). The ^ sign indicates industry averages. This variables are correlated to the regressors but they are expected to be
uncorrelated to the error term, since they are not company specific. 𝑒1,𝑡 = 𝑅𝑂𝐸𝑖,𝑡 − 𝐸[𝑅𝑂𝐸𝑖,𝑡 |𝐻𝑖,𝑡 ] is the error term
in Eq. (1), 𝑒2,𝑡 = 𝐶𝑆𝑅𝑖,𝑡 − 𝐸[𝐶𝑆𝑅𝑖,𝑡 |𝐻𝑖,𝑡 ] is the error term in Eq. (2) and 𝑒3,𝑡 = 𝑆𝑖𝑧𝑒𝑖,𝑡 − 𝐸[𝑆𝑖𝑧𝑒𝑖,𝑡 |𝐻𝑖,𝑡 ] is the error
term in Eq. (3). 𝐻𝑖,𝑡 is the available set of information. Then, the following moment conditions can be implied. First,
𝑖
the forecasting error, er,t , r = 1, 2, 3 in Eqs. (1), (2) and (3), is assumed to have a zero mean (i.e., 𝐸[𝑓𝑟,𝑡
(𝛽, 𝜐𝑖,𝑡 )] =
𝑐
𝐸[er,t ] = 0) and be uncorrelated (i.e., 𝐸[𝑓𝑟,𝑡 (𝛽, 𝜐𝑖,𝑡 )] = 𝐸[𝑒𝑟,𝑡 𝑒𝑟,𝑡−1 ] = 0). Second, all independent variables are
𝜐
𝜐
assumed to be uncorrelated with er,t (i.e., 𝐸[𝑓𝑟,𝑡
(𝛽, 𝜐𝑟,𝑡 )] = 𝐸[er,t ∗ 𝑧𝑟,𝑡
] = 0). Summarizing, the joint model is
estimated using the following moment conditions:
er,t
𝑒
𝑟,𝑡
𝐸 { 𝑒𝑟,𝑡−1 } = 0.
𝜐
er,t 𝑧𝑟,𝑡
′
′
′
𝑖
𝑐
𝜐
The GMM disturbances are then gathered in a vector (𝛽, 𝜐𝑖,𝑡 ) = [𝑓𝑟,𝑡
(𝛽, 𝜐𝑖,𝑡 ) , 𝑓𝑟,𝑡
(𝛽, 𝜐𝑖,𝑡 ) , 𝑓𝑟,𝑡
(𝛽, 𝜐𝑟,𝑡 ) ]′.
11 𝐼
𝑇
The sample means are defined as: 𝑔(𝛽; 𝑆𝐼,𝑇 ) = ∑𝑖=1 ∑𝑡=1 𝑓(𝛽, 𝜐𝑖,𝑡 ), where 𝑆𝐼,𝑇 contains the observations of
𝑇𝐼
𝜐𝑖,𝑡−𝑗 , 𝑗 = 1, … , 𝑇 of a sample T. The idea behind GMM is to choose parameter values for 𝛽, such that the sample
moments, 𝑔(𝛽; 𝑆𝐼,𝑇 ), closely approximate the population moments, 𝑓(𝛽, 𝜐𝑖,𝑡 ), or else to make 𝑔(𝛽; 𝑆𝐼,𝑇 ) as close to
zero as possible. By the “Law of Large Numbers” 𝑔(𝛽; 𝑆𝐼,𝑇 ) ≈ 𝑓(𝛽, 𝜐𝑖,𝑡 ) for large values of T, so an appropriate
estimate, 𝛽̂ , of the population parameter 𝛽 makes 𝑔(𝛽; 𝑆𝐼,𝑇 ) ≈ 0. When the number of moment conditions, K, is
larger than the number of parameters, L, then the GMM estimator can be written as:
′
̂𝑡 ∗ 𝑔(𝛽; 𝑆𝐼,𝑇 )) ,
𝛽̂ = 𝑎𝑟𝑔min (𝑔(𝛽; 𝑆𝐼,𝑇 ) ∗ 𝑊
𝛽
̂𝑡 is a 𝐾𝑥𝐾 semi-definite “weighting” matrix, such as that lim 𝑊
̂𝑡 → 𝑊 (population). The approach
where 𝑊
𝑇→∞
employed for the estimation of 𝛽̂ is the “iterative” GMM, with a heteroskedasticity consistent covariance matrix
̂𝑡 = Ω−1 (where Ω = lim 𝑇 ∗ 𝐸[𝑔(𝛽; 𝑆𝑇 ) ∗ 𝑔(𝛽; 𝑆𝑇 )′]).
(Newey and West, 1987), when computing an estimate for 𝑊
𝑇→∞
In the above specification, 𝐾 > 𝐿 and, therefore, the model is over-identified. Hansen (1982) proposes J-statistics to
test the validity of the model, i.e., whether the implied moment conditions fit the data well. 𝐻𝑜 is that they do. Jstatistic is asymptotically Chi-squared with 𝐾 − 𝐿 degrees of freedom.
′
2
̂𝑡 ∗ 𝑔(𝛽; 𝑆𝐼,𝑇 )) → 𝜒𝐾−𝐿
𝐽 ≡ (𝑔(𝛽; 𝑆𝐼,𝑇 ) ∗ 𝑊
.
June 27-28, 2012
Cambridge, UK
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