The CSP-CFP missing link: complementarity
between environmental, social and governancepractices? ∗Sandra Cavaco†and Patricia Crifo‡
September 18, 2009

Abstract
This article analyzes the relationship between corporate social performance(CSP) and corporate financial performance (CFP) by proposing a theoreticalmodel and by testing its main predictions through an econometric study on amatched CSP-CFP panel data for the biggest European listed firms over the
2002-2007 period. Our matched micro-economic dataset gathers two sourcesof information: environmental, social and governance ratings from the Vigeo database and economic and financial performance data from the Orbis
database. Using panel data technique, we examine how the complementarity
between various corporate social responsibility practices is likely to increase
performance, as predicted by our theoretical model.
Keywords: Corporate social responsibility, firm performance, econometrics, panel data.
Journal of Economic Literature: M14 , L21, C33.

∗

We would like to thank Vanina Forget and participants at the 2009 ICQME (ENSAE, Paris)
conference and at the seminar of the chair for Sustainable finance and responsible investment for
their helpful remarks and suggestions. We are grateful to Vigeo for granting us access to their dataon environmental, social and governance ratings. Patricia Crifo acknowledges the support of the
Toulouse-Idei and Ecole Polytechnique chair for Sustainable finance and responsible investment,
as well as the Ecole Polytechnique chair for Business Economics.
†
LEM, University Paris II Pantheon-Assas and CPP, Aarhus School of Business.
sandra.cavaco@u-paris2.fr.
‡
Ecole Polytechnique, University Paris X Nanterre and IRES, Catholic University of Louvain.
patricia.crifo@polytechnique.edu.

11Introduction

In all OECD countries, firms are making a lot of effort to be, or at least to appear,socially responsible. In 2005 for instance, 52% of the top 100 corporations in the
16 more industrialized countries published a report on their corporate and socially
responsible (CSR) activities (Becchetti et al. 2005). In the US, 1 dollar out of 9invested on financial markets in 2007 embedded a dimension of CSR (11%), 3% in
France and nearly 4% in Europe (EFAMA 2008 and Euro SIF 2008).
Being socially responsible means that, beyond legal constraints, firms accept tobear the cost of a more ethical behavior by voluntarily committing for instance toimprove employment conditions, ban child labor and countries that do not respecthuman rights, protect the environment and invest in abatement equipment to reducecarbon footprint, develop partnerships with NGOs, provide funds to charity etc.
Many reasons in the literature are invoked to explain the prevalence of CSR
strategies: the shrinking role of government, the society’s demands for greater disclosure, the increased customer interest in CSR, the growing investor pressure, thecompetition on labour markets for competent and motivated employees, the increasing risk associated with unethical behaviors, the importance of taking into accountrelationships with suppliers, external pressure from the civil society etc. CSR strategies would in fact allow firms maximizing value and minimizing risk in the long run,
responding to increased competitive pressure and market differentiation, and suchstrategies would more generally allow taking into account the growing demands oftheir stakeholders (customers, consumers, employees, savers). These arguments are
known as the so-called ’Porter hypothesis’ or ’doing well by doing good’ (see Porterand Kramer, 2002).
The empirical link between corporate social performance and corporate financial
performance has received considerable attention for 35 years but no consensus has
yet emerged (see for instance, Margolis and Walsh, 2003, or Margolis, Elfenbein
and Walsh, 2007). Recent research points at numerous biases and problems ofprevious work (eg: Elsayed and Paton, 2005 or McWilliams and Siegel, 2000) among
which: model misspecification (endogeneity); omitted variables in the determinantsof profitability; limited data (small samples, old periods); cross-sectional analysis
invalid in the presence of significant firm heterogeneity; problems of measurementof CSR; wide diversity of measures used to assess financial performance. Anotherproblem also lies in the direction and mechanisms of causation. Whether corporatesocial responsibility would lead (or not) to superior financial performance, or whether
financial performance would rather be a necessary condition for corporate social
performance is still a major stake to be investigated.
In this paper, we consider that the absence of consensus on the links between
corporate social responsibility and intangible assets in general, and corporate financial performance suggests that it should be a specific combination of firm policies
that would likely lead to superior corporate performance. By definition, two or
more practices are complements when using one more intensely increases the mar2

ginal benefit of using others more intensively (Milgrom and Roberts, 1995). During
the 1990s, this argument has proven a useful explanation of the Solow paradox,
whereby ”you can see the computer age except but in the productivity statistics”(Solow, 1987). Indeed, several researchers have shown that only those firms thathave adopted both computerization and complementary innovative HRM (humanresources management) practices (teamwork, multi-tasking, quality circles etc.) did
enjoy superior performance (see e.g. Ichniowski and Shaw 2003; Boucekkine andCrifo, 2008). By analogy, the apparently ambiguous link between corporate social
performance and financial performance could presumably be explained by taking
into account the complementarity between the multi-dimensional facets of corporate social responsibility and intangible assets. In fact, a simple analysis on the
Vigeo database (the French environmental and social rating agency) reveals thatthe distribution of correlation among environmental, social and governance scoresshows strong positive correlations between many environmental, social and governance factors. This pattern clearly is consistent with the idea that corporate social
responsibility or performance (labeled CSP), decomposed into environmental, socialand governance (labeled ESG) factors, would be complementary in raising corporate
financial performance (labeled CFP).
This article analyzes how the complementarity between various ESG factors affects corporate financial performance by proposing a theoretical model and by testing
its main predictions through an econometric study on a matched CSP-CFP panel
data for the biggest European listed firms over the 2002-2007 period. Our theoreticalmodel formalizes the CSR decision by considering that entrepreneurs (or managers)
have to allocate their available time between purely productive (non CSR) activities and CSR activities. A firm’s CSR policy therefore has n distinct dimensions
in our theroetical model. CSR activities are also evaluated by a rating agency, andobtaining a rating above sectoral average on one CSR dimension increases firm’s
expected revenues. Moreover, CSR factors are characterized by relative complementarity. We are able to distinguish between either weak or strong complementaroty.
Under weak complementarity, the marginal return from one CSR practice increases
when another CSR practice has already been implemented even if the rating over
the other practice is below the sectoral average. Under strong complementarity, themarginal return from one CSR practice increases when another CSR practice has
already been implemented if and only if the rating over the other practice is above
the sectoral average. Weak complementarity therefore is more rewarding for thefirm.
In this context, we show that firms invest in CSR provided that they are relatively high performing. Under strong complementarity, investment in CSR is morewidespread (more firms invest in CSR) and firms invest more in CSR, except for thevery high performing firms. In terms of performance, we also show that expected
profits are higher under weak complementarity because this form of complementarity
is more rewarding (compared to strong complementarity). Finally, our comparative
statics exercises show that an increase in the degree of complementarity between
practices or an increase in the probability of obtaining a score above sectoral aver3

age decreases investment in CSR and increases expected profits. Intuitively, when
CSR investment is relatively more rewarding, there is a substitution effect in favorof purely productive activities. Nevertheless, expected profits are increased because
firms benefit from a higher comparative advantage in CSR investment.
Our contribution to the theoretical literature on the links between CSR investments and firm performance is twofold. On the one hand, we propose a model where
CSR decisions are made over n ≥ 2 dimensions, which enrich existing approaches
often focusing on one particular dimension of a firm’s CSR policy. In fact, an important theoretical literature considers CSR as a product differentiation strategy (seeamong others Baron (2007, 2008a,b), Graff Zivin and Small (2005), Becchetti et al.
(2005 a and b), Manasakis et al. (2007)). Other approaches, for instance Bagnoli
and Watts (2003), consider CSR as a private provision of a public good. Similarly,
Cespa and Cestone (2008) formalize CSR decision as stakeholder protection wheninvesting in uncertain technologies, and Benabou and Tirole (2006) consider CSR asa pro-social behavior. Our model formalizes CSR investments over multiple dimensions and may therefore be considered as complementary to these approaches. The
second originality of our model is to consider explicitly the role of rating agencies in
the CSR decision process and allows analyzing, within a simple partial equilibrium
model, the impact of extra-financial ratings on invetsment in CSR and expected
profits.
The main predictions of our model are then tested on a matched CSP-CFP panel
database of the biggest European listed firms over the 1998-2007 period. Moreprecisely, we examine whether CSR investment is positively correlated with firm
performance and how the complementarity between two CSR practices affects thisrelationship, given the chance of getting a rating above sectoral average. In our
database, the corporate social performance variables come from the Vigeo databaseand consist in detailed ratings attributed by the French environmental and social
rating agency Vigeo over 4 broad domains of firms’ intangible assets and corporatesocial responsibility (human resources, environment, customers and suppliers andcorporate governance). The corporate financial performance variables come fromthe Orbis database (Bureau Van Dijk) and consist in detailed information fromthe companies’ standardized annual accounts (Cash Flow, Employees, Total Assets,
Profitability ratios etc.).
We examine the impact of CSR ratings on firm performance by exploiting the
dynamic dimension of our dataset through the Generalized Method of Moments
technique (see Blundell and Bond, 1998), in order to analyze the dynamic impact ofcomplementary environmental, social and governance practices on firm performance.
Our empirical results show that the complementarity of environmental, social andgovernance policies matters in the long run relationship between CSP and CFP.
This paper is organized as follows. Section 2 presents the theoretical model.
Section 3 summarizes the main theoretical predictions to be tested. The empiricalstrategy and results are developed in section 4. Section 5 presents ongoing tests andresearch and section 6 concludes the article.
4

2

The theoretical model

2.1

Basic set-up and definitions

The economy is composed of both a continuum of risk-neutral entrepreneurs (ormanagers) who develop a project, and a rating agency. In our model, a firm’s CSR
policy has several dimensions as detailed in the following definition.
Definition 1 Corporate social responsibility has n ≥ 2 dimensions, among which:· A corporate governance component
This dimension refers for instance to the respect for shareholders’ rights, promotionof independent and competent administrators and auditors, transparent compensation policy of key executives.
· An environmental component
This dimension refers to the incorporation of environmental considerations into thedesign, manufacturing and distribution of products: pollution prevention and control, protection of water resources, biodervisty, waste management, management oflocal pollution, management of environmental impacts from transportation etc.
· A human resources (or social) component
This dimension refers to responsible human resources management: training andcareer development, employee participation, quality of working conditions etc.
· A business behavior component
This dimension refers to firms’ practices towards clients and suppliers: preventionof conflicts of interest, corruption or anti-competitive practices1 , product safety, information to consumers, integration of CSR in the supply chain etc.
· A community and human rights component
This dimension refers to contibutions to local and general interest causes, charitable
contributions, respect for human rights, elimination of child labor.
Decomposing CSR into n ≥ 2 dimensions is consistent with and complementary
to the existing literature. Theoretical models of CSR in fact focus on one broad
dimension of CSR, which might include several criteria, but which - for analytical
purpose - is meant to capture an overall CSR strategy at the firm level. For instance,
the firm’s CSR policy may be formalized as a product differentiation strategy (seee.g. Graff Zivin and Small, 2005 or Baron 2007, 2008a and b) or as a broader notionof stakeholder protection (see Cepsa and Cestone, 2007). Our definition of CSR
therefore provides a complementary analysis to such kinds of models in that we
decompose broad CSR into several sub-categories.
In the abundant empirical literature, many papers also focus on one broad dimension of firms’ CSR policy (see for instance the survey of Mercer and UnepFi,
2007) but recent papers tend to systematically focus on a wider set of dimensions of
1

Such practices are for instance encouraged by international organizations like United Nations,OECD, World trade organization.

5

CSR. For instance, in their meta-analysis of 167 studies on the links between CSR
and CFP, Margolis, Elfenbein and Walsh (2007) decompose CSR strategies into
9 categories : Charitable contributions, Corporate policies, Environmental performance, Revealed misdeeds, Transparency, and 4 other categories reflecting different
ways researchers attempt to capture firms CSR (Self-reported social performance,
Observers’ perceptions, Third-party audits and Screened mutual funds). Our approach is consistent with such types of studies, as we consider n possible categories
of CSR behaviors in our theoretical and empirical analysis.
Projects are developped by managers given an extra-financial evaluation of their
CSR policies by the rating agency. The rating agency produces scores or ratings
over governance, environmental and social decisions made by firms, which influence
the revenues and costs of the firm: if a positive amount of time x is devoted to
CSR, the probability that the firm gets a rating higher than the sectoral average2 isgiven by p(x) = µ.x, p ∈ [0, 1], and the probability that the firm gets a rating lower
than average is 1 − µ.x. The higher µ, the higher the chance of getting a rating
above average. Hence µ.x represents an index of the difficulty of being ’best in class’(above average) in a given sector.
We further consider that the different components of the firm’s CSR policy may
be relative complements (or substitutes). A group of CSR factors is complementary
if doing more of any subset of them increases the returns from doing more of any
subset of the remaining factors, in other words when ’the whole is more than thesum of its parts’. In the present model, we restrict the analysis to a 2 × 2 complementarity to keep the resolution analytically tractable. Given the evaluation of
the CSR decision by the rating agency and since the firm’s revenues depend on theprobability of attaining a rating above or below the sectoral average, we consider
the following definition of complementary among two CSR practices.
Definition 2 Two CSR practices are characterized by• weak complementarity (θ = w) when the marginal return from one CSR
practice increases when another CSR practice has already been implemented
even if the rating over the other practice is below the sectoral average.
• strong complementarity (θ = s) when the marginal return from one CSR
practice increases when another CSR practice has already been implemented if
and only if the rating over the other practice is above the sectoral average.
Both types of complementarity are characterized by super-modularity whereby
the marginal product of two or more practices jointly implemented is higher than
the sum of the marginal product of each practice implemented separately.
2

Given that we have a continuum of entrepreneurs and firms, the notion of sector matters only
for the empirical analysis.

6Note that in the case of substitutability between CSR practices, definition 2 issymmetric in the sense that substitutability implies that the marginal return fromone CSR practice woudl decrease when another CSR practice has been implemented.

2.2

Firm’s expected profits

The problem facing an entrepreneur is how to allocate her available time between n
CSR practices (governance, environment, human resources, business behavior etc.)
and other (purely productive) activities (non CSR). We denote by τi the time allocated to thei CSR practice, where i = 1..n, and normalize available time to 1
Ptypen
so that 1− i=1 τi denotes the time allocated to non-CSR activities (pure productive
tasks without concerns for CSR factors).
The problem facing an entrepreneur is how to allocate her available time between
exerting an effort over each type i CSR dimension and purely productive activities.
The firm’s effort over CSR practices are evaluated by the rating agency and theprobability of receiving a rating higher than the sectoral average pi = (µ.τi ) affects
the firm’s marginal revenues due to a pure rating effect and to a complementarity
effect (between CSR practices).
The firm’s expected profits have two components:
1. the marginal revenue (or product) of non-CSR (purely productive) activities,
labelled y > 0 in efficiency units.
P
The total productive return therefore is equal to y · (1 − ni=1 τi )2. the marginal revenue (or product) of CSR activities, composed of three elements:• the marginal CSR revenue from investing in i denoted by z > 0 in efficiency units. The return from the ith component therefore equals z · τi• the net benefit of a rating higher than the sectoral average, labelled b > 0
in efficiency units. The return from a rating above the sectoral averageover the ith dimension therefore equals b · τi• the additional benefit from investing in complementary CSR practices
labelled g θ > 0 in efficiency units where, according to definition 2, θ = w(resp. θ = s) denotes weak (resp. strong) complementarity.
With probability pi = (µ · τi ) the firm gets a rating higher than sectoral averageover the type i CSR dimension. The firm’s expected revenues from investing in the
ith CSR dimension are therefore such that: with probability (µ·τi ) the firm generates
a net revenue z · τi + b · τi + g θ and with probability (1 − µ · τi ) the firm generates a
net revenue z · τi .
The firm’s
Pn overall expected profits from investing τi in i = 1..n CSR dimensions,and (1 − i=1 τi ) in purely productive activities, hence write:
7

Π=y·

1−

n
X

!
τi

+

n
X



(µ · τi ) · z · τi + b · τi + g θ + (1 − µ · τi ) (z · τi )

i=1

i=1

Simplifying this expression leads to:

Π=y·

1−

n
X

!
τi

n
X


+
z · τi + µ · b · (τi )2 + (µ · τi ) · g θi=1

θ = w, s(1)

i=1

The complementarity gains g θ are earned with probability µ · τi and are specified
according to definition 2. Let g > 0 denotes the marginal gain from complementarity3 between two CSR practices i and j, such that i, j = 1..n, j 6= i.
Under weak complementarity (θ = w), the marginal return from investing in i increases when j has already been implemented even if the rating over j is below thesectoral average. The gains from weak complementarity between type i and type j
CSR practices therefore simply equals g w = g (these gains are obtained with probability µ · τi as explained above).
Under strong complementarity (θ = s), the marginal return from investing in i increases when j has already been implemented if and only the rating over j is above
the sectoral average, which occurs with probability µ · τj . Hence, the expected gains
from strong complementarity between types i and j CSR practices therefore equal
g s = (µ · τj ) · g .
In sum, the firm’s overall expected profits under both types of complementarity
write:Πw= y·Πs = y ·

1−
1−

n
X
i=1
n
X
i=1

where

!
τi

n
X


+
z · τi + µ · b · (τi )2 + (µ · τi ) · g

!
τi

+

i=1
n
X



z · τi + µ · b · (τi )2 + (µ · τi ) · (µ · τj ) · g

(2)
(3)

i=1

j 6= i.

Note that given g > 0, expected profits are super-modular: the joint implementation of two complementary CSR factors generates returns higher than thePn sum ofθ
returnsfrom each of those factors implemented separately: Π ≥ y · (1 − i=1 τi ) +Pn[z· τi + µ · b · (τi )2 ] θ = w, s.
i=1
We now turn to the optimal investment in CSR factors.
3

Note that our model allows analyzing in a completely symmetric manner the case of substitutability between CSR practices. In this case indeed, parameter g would be negative.

8

2.3

Equilibrium CSR decisions

The equilibrium investment level τi in the type i CSR dimension solves the following
firm’s profit maximization programs.
• Case i: Investment in CSR practices under weak complementarity
Given (2) , the firm’s optimization program writes
maxΠw = y · (1 −Pτi

i τi ) +2i [z · τi + µ · b · (τi ) + (µ · τi ) · g]P

The solution of this program writes:
τw =

y−z
g
−
2µb
2b

(4)

The firm’s expected profits under weak complementarity then write:EΠw = y −n(y − z − gµ)2
4bµ

(5)

• Case ii: Investment in CSR practices under strong complementarity
In this case, we shall distinguish among three types of CSR dimensions: type i
ans type j CSR dimensions are relative complements, and let denote by k the other,non complementary, CSR dimensions. Given (3), the firm’s optimization program
writesPP
max Πs = y · (1 − i τi ) + i [z · τi + µ · b · (τi )2 + (µ · τi ) · (µ · τj ) · g]τj ,τi ,τ kwhere j, k = 1..n j, k 6= i.
The solution of this program writes:τis = τjs =
τks =

y−z
µ(2b + gµ)

y−z
2bµ

(6)
(7)

The firm’s expected profits under strong complementarity then write:EΠs = y −(y − z)2 2nb + (n − 2)gµ4bµ
2b + gµ
9

(8)

2.4

Existence of equilibrium and comparative statics

To ensure that 0 < τ w < 1, 0 < τis = τjs < 1 and 0 < τks < 1, we make the following
assumption.
Assumtion 1. z + µg < y < 2bµ
We now analyze the equilibrium values of investments in CSR.
Result 1. Under assumption 1, firms invest in CSR provided that their productive
performance y is high. Under strong complementarity, investment in CSR is morewidespread (more firms invest in CSR) and more intensive (firms invest a higher
amount of time in complementary CSR dimensions) when productive performanceis below the threshold ȳ = z + gµ + 2b.
Proof. Under weak complementarity, we show that :
τw =

y−z
g
−
> 0 ⇔ y > z + gµ2µb2bUnder strong complementarity, we show that :τis = τjs =y−zy−z> 0 and τks =>0⇔y>zµ(2b + gµ)
2bµ

There is therefore a threshold level ỹ such that firms with productive performancey > ỹ choose to invest in CSR. The threshold level is lower under strong complementarity (ỹ s = z) compared to weak complementarity (ỹ w = z + gµ), therefore
more firms invest in CSR under strong complementarity.
Moreover, we have: τ w < τks and τ w > τis = τjs ⇔ y > z + gµ + 2b. Hence, firms
for which y ≤ ȳ = ỹ w + 2b = z + gµ + 2b invest more in relatively complementary
CSR dimensions under strong complementarity.
To analyze the expected profit levels given the equilibrium CSR investments,
some numerical simulations are needed to have a simple representation of profits
levels. Indeed, the profits functions are not linear in y and assumption 1 imposes
restrictions on y, therefore a numerical analysis is more straightforward. These simulations have be realized using the following parameters values (satisfying assumption
1):
Table 1: Parameters values for the simulation exercise
n z
10 2

g µ
1 0.5

10

b
10

The results of the simulation are reported in figures 1a, 1b and 2.
[Insert Figures 1a, 1b and 2]
Figures 1a and 1b illustrate result 1 and show that CSR investment is higer understrong complementarity (solid line) unless firm performance becomes very high, thatis above the threshold ȳ = z + gµ + 2b (see figure 1a). In this case, the difference(τ w − τis ) becomes positive and investment under weak complementarity becomes
higher than under strong complementarity (see figure 1b).
Figure 2 shows that expected profits are higher under weak complementarity(dashed line). This property is due to the fact that strong complementarity implies
lower expected revenues for firms investing in CSR: to benefit from complementarity
between two CSR practices, firms have to invest more and this is relatively lessrewarded. Weak complementarity therefore is the highest rewarding mechanism
when two CSR practices are relative complements.
We now analyze how the equilibrium values of investment in CSR factors andexpected profits vary with the degree of complementarity practices g.
Result 2. Under assumption 1, for both types of complementarity (weak andstrong), an increase in the degree of complementarity between practices g:• decreases investment in CSR• increases expected profits

Proof.
w

∂τ s

s

w

We have ∂τ∂g < 0, ∂gi < 0 , ∂EΠ> 0 , and under assumption 1: ∂EΠ
>0
∂g
∂g
w

Moreover, we have ∂τ∂µ < 0,
∂EΠw
>0
∂µ

∂τis
∂µ

s

< 0 , ∂EΠ
> 0 , and under assumption 1:∂µ

Result 3. Under assumption 1, for both types of complementarity (weak andstrong), an increase in the probability of obtaining a rating higher than sectoral
average µ:• decreases investment in CSR• increases expected profits

11

Proof.
w

∂τ s

s

w

We have ∂τ∂µ < 0, ∂µi < 0 , ∂EΠ> 0 , and under assumption 1: ∂EΠ>0∂µ∂µWhen firms invest in complementary CSR factors, a higher degree of complementarity or a higher chance of getting a rating above average increase the overall
benefit of CSR investment (see equation (1)). This decreases the incentives to invest
in each CSR dimension (see equations (4), (7) and (7)) because CSR investment isrelatively more rewarding and yields a substitution mechanism in favor of purely
productive activities. Expected profits are however increased because firms enjoy ahigher comparative advantage in investing in CSR (see equations (5) and (8)).
Note finally, that all our results have to be interpreted both in terms of complementarity or in terms of substitutability between CSR factors. In this latter case,our parameter g has a negative sign, and our results are opposite.

3

Theoretical predictions to be tested

Our comparative statics results lead to the following testable predictions.
Result 1 has two implications. First, CSR investments appear to be positively
correlated with firm performance since only high performing firms invest a positive
amount in CSR. Second, when CSR practices are strongly complementary, firms’
investment in CSR is lower when producitve performance is very high (above thethreshold ȳ, therefore the positive correlation between CSR investment and firm performance tends to diminish (or disappear) above the thershold ȳ. These thereoticalresults lead to the following testable predictions.
Prediction 1. CSR investment is positively correlated with firm performance.
Prediction 2. Under strong complementarity (substitutability) between CSR practices, the correlation between CSR investment and firm performance is not linear
and may increase or decrease with productive performance.
Results 2 and 3 have two implications. The higher the degree of complementarity
between practices or the higher the chance of getting a rating above average, thelower the firm’s investment in CSR. There is a trade-off in the determinants of
CSR invetsments and profits between investing in purely productive activities or in
CSR activities, and as CSR practices are more rewarding, there is a substitution
mechanism towards purely productive (non CSR) investment in order to raise profits.
These results yield the following predictions.
Prediction 3. When high performing firms invest in complementary (resp. substitutable) CSR practices, a higher degree of complementarity between practices or a12higher chance of obtaining a rating above average decreases (resp. increases) firms’
investment in CSR.
Prediction 4. When high performing firms invest in complementary (resp. substitutable) CSR practices, a higher degree of complementarity between practices or ahigher chance of obtaining a rating above average increases (resp. decreases) firm’s
expected profits.
We now turn to the empirical analysis given these theoretical predictions.

4

Empirical strategy and results

Our purpose is to examine the scope of synergy effects on firm performance thatarise when firms combine several CSR activities using panel data for European countries. We consider that two CSR activities are complementary whenever their joint
execution will produce synergy effects for a given performance measure. Besides,
we take into account dynamics and correct for endogeneity in order to specificallyexamine the relationship between CSR practices and firm performance. A plausiblemodel of the CFP-CSR process should in fact exploit the panel structure of our data
and allow for dynamics in which past CFP helps to explain current performance.

4.1

Empirical set-up

The main issue is that when we investigate the relationship between CSR investments and firm performance, current firm performance is likely to be correlatedwith both the observable and unobservable factors (i.e. observable and unobservable heterogeneity) that also determine CSR strategies. Moreover, causality may
run in both directions, that is from CSR to firm performance and from performance
to CSR.
We thus estimate the relationship between firm performance, labelled Πit , its
lagged value, Πi−t , the CSR scores, CSRit and a set of control variables, labelled
Xit , as follows:Πt = β1 Πit−1 + β2 CSRit + β3 Xit + ηi + εit(9)where i refers to individual firm and t to time dimension, ηit represents the unobservable time-invariant heterogeneity and εit is the error term. Xit are potentiallypredetermined firm-level time-variant control variables
13

With respect to equation (9), past realizations of our dependent variable areendogenous to the fixed effect in the error term. Ordinary Least Squares (OLS)
estimates that ignore the unobserved heterogeneity will be biased if unobservablefactors that determines performance are also correlated with CSR strategies. This
inconsistency can be mitigated by using instrumental variables regressions (IV) but
it is in general difficult to identify valid appropriate instruments that are strictly
exogenous. Fixed effects regressions can be estimated in order to eliminate theunobservable heterogeneity. However, fixed effects models can be biased if the explicative variables are correlated with past realizations of the dependent variable.
Indeed, we argue that this is the case with CSR practises and firm performance.
The estimates of coefficients provided by OLS are upward biased and those comingfrom the Within-group estimator are downward biased (Bond, 2002)4 .
Thus, to obtain consistent and unbiased results, we estimate the relationshipbetween CSR investments and firm performance using the dynamic Generalised
Method of Moments (GMM) estimator, called system GMM. The GMM estimator
was first introduced by Holz-Eakin et al.(1988) and Arellano and Bond (1991) andthen further developed both in Arellano and Bover (1995) and Blundell and Bond(1998). The estimation procedure exploits the dynamic endogeneity inherent in our
variables.
The system GMM estimator extends the GMM in difference by estimation ofa system of first-differenced equations and the equations in levels in which more
instruments can be exploited. In the first-differenced equations we use the lagged
level values of the variables as instruments as in the GMM difference estimator(Arellano and Bond, 1991); in the levels equations we use differences as instruments.
Indeed, the use of a new set of intruments in differences improves efficiency as it
directly deals with the problem of weak instruments of difference GMM (Arellano
and Bond, 1991) in persistent series. Besides, another advantage of system GMM
method is that it allows the other regressors to be predetermined (explained by theirpast realizations) or endogenous (explained by current and past realizations of othervariables and by their own autoregressive process).
The system GMM estimator enables us to obtain consistent estimates by controlling for unobserved heterogeneity, simultaneity and endogeneity and time-invariant
variables. However, two conditions are necessary for estimators to be consistent.
According to the first one, the error term has to exhibit no serial correlation. In
order to test if this condition is satisfied, we use the autocorrelation test on theresiduals proposed by Arellano and Bond (1991). The second condition imposes thevalidity of the instruments that are used. The overall validity of the instruments
can be corroborated by a test of over-identifying restrictions, the Hansen J statistic. The requirement for the validity of the instruments for the level equations is
4

Although OLS and fixed effects estimators are biased, they can be useful since they give aninterval in which a consistent estimation of the coefficients should lie.

14

that they have to be uncorrelated with the fixed effect. Athey and Stern (1998) havespecifically discussed the problem of identifying complementarities in cross-sectional
datasets. As pointed out by Leiponen (2005), the system GMM panel data approachcontrols for unobserved firm fixed effects that are the source of bias in many studies.
Finally, the system GMM framework flexibly accommodates unbalanced panels.

4.2

Data and variables

In this section we discuss the data, the performance and CRS measures and thecontrol variables we use in our empirical analysis.
4.2.1

Data description

The main predictions of the model are tested using a matched CSR-CFP panel
database.
The CSR variables come from the Vigeo database. Vigeo, the leading European
CSR rating agency, measures companies’ CSR performance and risk factors on Environmental, Social and Governance (ESG) criteria and supplies this information toinvestors and asset managers notably. The areas under review are: Human Rights,
Environment, Human Resources, Business Behaviour, Corporate Governance, Community Involvement. Out of these 6 broad domains, up to 40 criteria are covered,among which: Promotion of labour relations, Quality of remuneration systems, Improvement of health and safety conditions, Pollution prevention and control, Protection of water resources, Management of environmental impacts from the use anddisposal of products/services, Waste management, Development of ”Green” products and services, Integration of environmental and social factors in the supply chain,
Balance of power and functioning of board of directors, Remuneration of directors
and key executives etc. The evaluation is realized by Vigeo, and not by the firms
themselves. Each criterion is applied in relation to its sector relevance and is given
a consideration representing the relative weight of social responsibility objectives
relating to it (see appendix 7.1 for details). Vigeo provided detailed data on 595
European firms. The data are available from 1998 to 2007.
We supplement the information on CSR with financial data extracted from Orbis (Bureau Van Dijk). Orbis is a comprehensive database of companies aroundthe world containing information combined from nearly 100 sources (Datamonitor,
Zephyr, Coface etc.) filtered into various standard report formats. The information
has up to 25 data sections and 10 years of history, including detailed informationfrom the companies’ standardized5 annual accounts, consolidated and unconsolidated, together with financial profile (balance sheet, P & L account, financial ratios),
5

Orbis information is standardized information given the differences in accounting practices
among the different countries.

15

activities and ownership, like for instance: Cash Flow, Employees, Total Assets, Intangible Assets valuation, Shareholders Funds, Profitability ratios (Profit Margin,
Return on shareholders Funds, Return on Capital Employed, Solvency Ratio, Price
Earning Ratio), Operational and Structure ratios etc.
In order to avoid sample selection issue, we do not require balanced panel, i.e.
the number of firms differs from year to year and the estimations strategy uses
as many observations as available. Besides, as we introduce the lagged value forthe dependent variable, we have to observe firms over at least two years. Thenwe excluded from our sample firms that do not provide complete information on
financial data. Our final unbalanced panel sample comprises 1 094 observations(around 300 firms per year) belonging to about 15 countries over the period 20022007.
The control variables characterize both the operations of the firm (e.g. Researchand Development) and the financial structure and risk of the firm (e.g. debt ratio).
The variables are defined in Appendix ??. Table 1 provides descriptive statistics for
all variables.
[Insert Table 1]
Size is a relevant control variable since there is some evidence that smaller firms
may not invest in CSR strategies as much as do larger companies. Firm size ismeasured in terms of a firm’s total sales on a log scale. The leverage of the firm isalso an important control variable, and as a proxy for a risky behavior we include thelevel of debt held by the firm (long-term debt to total assets ratio). We introduce
a Research and Development intensity indicator measured by the Research andDevelopment expenses divided by total sales. To identify the non-reporting Researchand Development expenditures, a dummy variable with a value of 1 is included if
Research and Development expenses are not reported by firms. In order to control forsensitivity to the stock market variations, we introduce a dummy variable reporting
firms listed at the Dow Jones STOXX600 index. We then also control for countries’
differences by including countries dummies. Table 2 displays the average scores bycountry.
[Insert Table 2]
The best performances in terms of human scores are observed in France, Norway, the Netherlands and Germany. The best performances regarding environment
are observed in Norway, Germany and the United Kingdom. Finally, the United
Kingdom, the Netherlands, Finland and Norway report the highest scores for both
corporate governance and business behavior criteria. For both social and environmental domains, Greece and Ireland report the lowest scores. It is worth controlling16

for countries’ differences since the level of CSR is likely to depend on the legal
system.
Furthermore, clear differences in performance and CSR strategies may exist
among different industries. Table 3 reports the average industry CSR scores.
[Insert Table 3]
Table 3 shows that the sectors who have the best performances in terms of CSR
are also sectors which have to face a negative image in the public opinion regarding
the environmental or social responsibility such as automobile, transport or energy
industries. There are differences between industries with the lowest environmental
and social rated sectors being media and hotel around 20 (as explained in appendix
7.1 the scores spread out from 0 to 100). We can observe that in general, socialand environmental scores have the same magnitude and the same evidence can beobserved for both corporate governance and business behavior (towards customers
and suppliers). This descriptive analysis indicates the importance of controllingfor sectors in the assessment of the relationships between financial performanceand CSR. These evidence support our choice to introduce in the firm performance
regressions the dummies CSR variables taking into account the sector average score
performance, that is to rely on ratings rather than on continuous scores, as detailed
in appendix 7.1 (see section 5.2.2).
4.2.2

Measuring firm performance and CSR

One of the reasons for the ambiguous relationship between CSR and firm performance lies in the difficulty of measuring and in the variety of available measures of
CSR and CFP. Since CSR is integrated into business practices, it is by definition
complicated to estimate its effects separately. Indeed, there is little consensus on
the nature of variables that best measure both corporate performance and CSR.
Firm financial performance can be measured by different variables providing a
range of measures used to assess corporate financial performance by the investmentcommunity. Studies use in general two accounting variables (return on assets andreturn on equity) and the Tobin’s q indicator.
Accounting measures like return on assets, return on equity or return on sales,
capture the historical aspects of firm performance (Mcguire et al., 1986). Thesetypes of variables are however subject to bias from managerial manipulation anddifferences in accounting procedures across countries (Branch and Cole, 1983). The
Tobin’s q represents a measure of return based on the stock market and is moresensitive to variations that may be independent of the operations and social activities of firms such as macroeconomic shocks and political issues and which can affectmarket values. The Tobin’q is also more dependent on industry-specific factors such17

as rising or falling prices due to shifts in industry demand or restrictions on supply,
as in the case of oil or other raw materials. Both market and accounting measures
which represent different perspectives on how to evaluate firm financial performance,
have different implications and are both subject to specific biases. In other words,
accounting measures can suffer from biases from differences in accounting procedures. Market performance represents investors’ evaluation of the ability of a firm
to generate future economic earnings. However, market-based measure can be more
appropriate to capture the expected future impact of CSR on performance (Hillman and Keim, 2001) since such variables are forward-looking measures rather thanbackward-looking measures (such as accounting performance variables). Thus, inorder to control for such biases, first we introduce year dummies (year fixed effects)
in order to control for macroeconomic variations and business cycle fluctuations
homogeneous across individuals. Second, we include industry dummy variables tocapture differences across industries. Considering all these evidence, we consider as
our dependent variable, the Tobin’s q.
Although, CSR is a multidimensional construct, the measures used in empiricalstudies have mainly been one-dimensional and have been applied to small sampleof firms and cross sectional data. Each company differs in how it implements CSR.
The differences depend on factors such as the firm’s size, the particular industry
involved, the firms business culture and the stakeholder demands. Some companies
focus on a single dimension, which is either considered as the most important forthem or where they have the highest impact or vulnerability, while others aim tointegrate CSR in all aspects.
This paper proposes a multidimensional measure applied across a wide range ofsectors and countries. In order to test the main predictions of our model and todeal with the measurement problems quoted above, CSR is reported into four subcategories: corporate governance, business behavior component (practices towards
customers and suppliers), environmental criteria and social aspects6 . Vigeo provides
information on a rating for each item by sector. The score allocated to a company
in each of the domains is adjusted in order to take into account the characteristics
and risks related to the sector under review (see appendix 7.1. Here, given ourtheoretical model and in order to assess the strategic dimension of such corporate
investments, we choose to consider criteria according to the relative importance ofthe social responsibility objectives in the sector concerned and not the continuousscores. Thus, we construct four CSR dummies equal to 1 if the firm is really active,
i.e. if her respective score is equal or above the average score of the sector and equal
to 0 otherwise (i.e. if her score is below the average of the sector)7 .
6Although, Vigeo provides CSR criteria divided into six fields, for computation reasons we
restrict our analysis to these four practices which appear to be more complementary. Besides, weshould note that the human rights criterion is not specified for all firms as it is not considered asa major stake in all sectors by Vigeo.
7
The Vigeo rating variable has initially five modalities ranging from active to very inactive inthe criterion considered.

18

In order to assess synergies derived from the adoption of alternative CSR strategies, we first estimate the pair-wise correlation between the four CSR sub-categories
considered. Table 4 reports the estimation of unconditional correlations (e.g. Spearman rank correlation) between the four CSR dummies. Positive correlation points
to complementarity between the different CSR activities.
[Insert Table 4]
We observe that business behavior (towards customers and suppliers), environmental and social rates are highly positively correlated and that they are in a lower
proportion respectively correlated with the corporate governance score. The argument that complementarities exist among CSR practises is consistent with the viewthat they are highly correlated with each other. However, it should be quoted thatsince correlations might be induced by unobserved factors, we cannot conclude on atrue existence of complementarities from these first results.
Besides, the high degree of intercorrelation among CSR practises indicates that
empirical models that estimate the impact of any one CSR policy on firm performance will yield biased coeffcients due to the omission of the other CSR practises.
One possible solution to this problem would be to enter the entire set of potentially
important CSR variables into the firm performance equation. However, as pointed
out by Ichniowski et al. (1997), this approach is confounded by the severe collinearity among CSR practices, making any one coefficient uninterpretable, and would not
directly test whether combinations of CSR practises are the critical determinantsof firm performance. In order to examine the effects of highly correlated variables
sets, one should simultaneously estimate the effects of all the pair-wise interactions
among the practises. Once more, a complete set of interaction terms still would be
confounded by collinearity among practices, so finally we should identify commonclusters of practices.
Following this approach, we first introduce in the firm performance regression thepair-wise dummies CSR interaction in order to test for complementarity. Second,
we identify clusters of practises since each CSR activity is dichotomously measured.
We construct exclusive categories to represent the four CSR activities. Table 5
displays the definition for the different CSR states. In particular, we define 16
dummy variables by following the convention of binary algebra. They are equal
to one when the combination of CSR practices are adopted and zero otherwise.
It should be noted that coefficient β(0000) is normalized to zero in our empiricaldesign. Thus, we introduce 15 dummies variables for exclusive combinations of CSR
practises. Estimations will generate coefficients for mutually excluding dummies forthe different indicator combinations.
[Insert Table 5]

19

4.3

Results

Table 6 displays the regression analysis for the system GMM estimations8 . In all
the specifications the set of instruments is composed of the dependent variable,
the CSR scores and the control variables, all in lag two. We use robust standard
errors and valid the two previously presented standard tests on misspecification.
The Arellano and Bond test on autocorrelation supports the overall validity of themodel by providing evidence of first order autocorrelation (AR1) and the absence of
second order autocorrelation (AR2) while the Hansen test supports the consistencyof the GMM instruments. Thus, our estimation controls properly for potential
correlation between unobserved factors and the regressors, which is a critical issue
in the empirical literature on complementarities (Leiponen, 2005). Finally, Blundell
and Bond (1998) recommend the one-step results instead of the efficient two-stepones, which deflate standard errors and may produce ”too” significant coefficients
particularly if the data are heteroskedastic.
[Insert Table 6]
The positive and significant coefficient of the lagged dependent variable confirmsthat firm performance is persistent. Firm performance depends substantially on its
own past realizations. Regarding control variables determining firm performance, wefind that the majority of the estimates have the expected signs. Firm performanceis negatively related to debt-to-asset ratio. Table 6 illustrates that both sales andResearch and Development intensity have a positive impact on firm performance.
We then test for the sign of the relationship between CSR and firm performance.
In line with the literature, the sign may imply negative, neutral or positive linkages
between firm performance and CSR. A negative sign implies that socially responsible firms have a competitive disadvantage because they incur costs that reduce
profits, while these costs could be avoided or borne by individuals or the government. However, many empirical results reveal no significant relationship between
CSR and financial performance. The number of variables at play and the measurement biases may prevent to test properly for such a relation. Finally, a positive
sign implies that the actual costs of CSR are covered by the benefits since socially
responsible companies have less risk of negative events (fines, costly lawsuits etc.).
In order to accept complementarity, the coefficient of the interaction term has
to be significantly larger than zero. A significant and negative coefficient would bea sign for submodularity and thus for the two CSR activities to be substitutes.
First, we observe that only performing actively business behavior (towards customers and suppliers) activity raises firm performance, but the other CSR activities
8

Estimations were carried out using the Stata module Xtabond2 developed by D. Roodman(2006).

20

are non significant. Hence, our first prediction is confirmed for the business behavior
component of CSR.
However, joint implementation of business behavior and human resources practises has a positive and significant impact on firm performance. We find also strong
interaction effects associated with the adoption of environment and governance activities. Not only the returns of the environment policy is higher when it is adopted,
but they are higher when corporate governance practise is implemented. In contrast, human resources and corporate governance on the one hand and environmentand business behavior practices on the other hand seem to be rather substitutes as
the significant and negative interaction term shows. Hence, our second prediction
is partially confirmed, with complementarity between business behavior and human resources and between environment and governance, but with substitutability
between human resources and governance and between environment and businessbehavior.
Table 7 displays the results with the CSR clusters which, in contrast to the firstestimation with pair-wise CSR practices, have the advantage to test simultaneously
for the four practices combinations. The coefficients on the different states convey
little information. However, the test is inconclusive for most of the states. The
corporate governance practice implemented alone has a positive impact on firm
performance. As pointed out before, we do find complementarity for State1001(between HR and CS). Finally, State1101 and State0111 coefficients show a strong
interaction effect on firm performance. Thus, we can say that the coefficients forstates with multiple practices are higher than those for individual practises thus
suggesting that complementarity applies. In our theoretical model, under weakcomplementarity, the marginal return from one CSR practice increases when another
CSR practice has already been implemented even if the rating over the other practiceis below the sectoral average. Hence our prediction 4 is partially confirmed.
[Insert Table 7]
It is important to note that these empirical results only partially support ourtheoretical predictions since predictions 1 and 4 are only predicted for some specificcombinations. Moreover, we still have to test more specifically predictions 2 and 3of the model.

5

Ongoing tests and research

Several alternative empirical testing procedures have been derived to formally examine complementarity among business practices (see Athey and Stern, 1998 for an
overview). More specifically, the productivity approach has been implemented in21

various specifications in the innovation literature (Mohnen and Roller, 2005; Leiponen, 2005; Belderbos et al., 2006) with a precise examination of multiple complementarities. As pointed out by Mohnen and Roller (2005), the individual significanceand signs of the coefficients on the obstacles do not directly reveal whether practices
are complementary or substitutable for two reasons. First, complementarity involves
testing linear restrictions of several coefficients. Second, complementarity requires
testing the joint distribution of several of these linear restrictions. For both reasons,
it is possible that all coefficients are statistically insignificant, even though the joint
hypothesis for supermodularity is accepted.
In order to test the robustness of our results, we will use the productivity approach which is a direct test for supermodularity. Following Mohnen and Roller(2005), we will first obtain consistent and efficient estimates of the states coefficients as displayed in Table 7, and second CSR pair-wise practises will be tested
using methods developed by Kodde and Palm (1986). This additional empiricalstrategy is under progress.

22

6Conclusion

This article proposes a theoretical and empirical analysis of the interactions between
firm CSR practices and performance. Our model shows that firms invest in CSR
provided that they are relatively high performing and that the complementarity
between CSR practices increases expected profits. The ongoing empirical debate
on whether CSR activities do have or not a positive impact on firm performance
reveals the difficulties to provide unambiguous evidence on the existence of positivesynergies effects. Our empirical analysis examines the joint implementation of CSR
practices measured by the interaction terms of the respective CSR practices. The
results of our analysis are twofold. First, the empirical evidence suggests that there
are significant complementarities between specific combinations of CSR practices.
However, a straightforward conclusion cannot be drawn at this stage. Our empirical results on complementarity highlight their sensitivity to model specification andmeasurement. Hence, to extend our analysis and provide robust evidence, a productivity approach allowing for a direct proper test for supermodularity is needed andunder progress.

23

References[1] Arellano, M. and S. Bond. 1991. Some Tests of Specification for Panel Data:
Monte Cralo Evidence and an application to employment equations. Review of
Economic Studies, 58, 277-297.
[2] Arellano, M. and O. Bover. 1995. Another Look at the Instrumental Variable
Estimation of Error-component Models. Journal of Econometrics, 68, 29-51.
[3] Athey, S. and S. Stern. 1998. An Empirical Framework for testing Theories
about Complementarity in Organizational Design. NBER Working Paper 6600.
[4] Bagnoli, M. and S. Watts. 2003. Selling to Socially Responsible Consumers:
Competition and the Private Provision of Public Goods. Journal of Economics
and Management Strategy, 12, 419-445.
[5] Baron, D. 2001. Private Politics, Corporate Social Responsibility and Integrated
Strategy. Journal of Economics and Management Strategy, 10, 7-45.
[6] Baron, D. 2006. A Positive Theory of Moral Management, Social Pressure, andCorporate Social Performance. Working Paper. Stanford University.
[7] Baron, D. 2007a. Corporate Social Responsibility and Social Entrepreneurship.
Journal of Economics and Management Strategy 16, 683-717.
[8] Baron, D. 2008. Managertial contracting and Corporate Social Responsibility.
Journal of Public Economics 92, 268-288.
[9] Baron, D., Harjoto, M. and H. Jo. 2008. The Economics and Politics of Corporate Social Performance. Stanford graduate school of business Research paper
N 1993.
[10] Becchetti, L., Giallonardo, L. and E. Tessitore. 2005a. Corporate Social Responsibility and Profit Maximizing Behaviour, Working Paper AICCON-Universita
di Bologna n.21.
[11] Becchetti, L., Giallonardo, L., and E. Tessitore. 2005b. The game of social responsibility: a product differentiation approach. Mimeo University Tor Vergata
Roma.
[12] Belderbos R., Carree, M. and Lokshin B. (2006). Complementarity in RD Cooperation Strategies. Review of Industrial Organization, 28, 401-426.
[13] Benabou, R. and J. Tirole. 2006. Incentives and Prosocial Behavior. American
Economic Review, 96, 1652-1678.
[14] Blundell, R. and S. Bond. 1998. Initial Conditions and Moment Restrictions in
Dynamic Panel Data Models. Journal of Econometrics, 87(1), 115-143.

24

[15] Bond, S. (2002). Dynamic Panel Data Models: A Guide to Micro Data Methods
and Practice. Portuguese Economic Journal, 1, 141-162.
[16] Boucekkine, R. and P. Crifo, 2008. Human Capital Accumulation and the Transition from Specialization to Multitasking.0 Macroeconomic dynamics. 12, 320344 .
[17] Bowen, H.R. 1953. Social responsibilities of the businessman. New York: Harperand Row.
[18] Branch, B. and B. Cole. 1983. Linking corporate stock price performance tostrategy formulation. Journal of BUsiness Strategy. 4, 40-50.
[19] Brekke, K., Nyborg, K. 2004. Moral hazard and moral motivation: Corporatesocial responsibility as labor market screening. University of Oslo working paper, memorandum 25/2004.
[20] Carroll, A. 1979. A three-dimensional conceptual model of corporate performance. Academy of Management Review 4 (4), pp.497-505.
[21] Cespa, G. and G. Cestone. 2007. Corporate Social Responsibility and Managerial Entrenchment. Journal of Economic Management and Strategy. 16(3),741-771.
[22] Crifo, P. and J.P. Ponssard. 2009. La Responsabilite Sociale et Environnementale des entreprises est-elle soluble dans la maximisation du profit ? Societal,
forthcoming.
[23] Davis, K. 1960. Can business afford to ignore corporate social responsibilities?
California Management Review 2, pp.70-76.
[24] EFAMA (2008). Annual asset management report: facts and figures. European
Fund Asset management Association . July 2008.
[25] Elsayed, K. and D. Paton. 2005. The Impact of Environmental Performanceon Firm Performance: Static and Dynamic Panel Data Evidence. Structural
Change and Economic Dynamics. 16 ,395-412.
[26] EUROSIF (2008). European SRI Study.
[27] Friedman M. 1970. The Social Responsibility of Business is to Increase its Profits. The New York Times Magazine, September 13, 1970.
[28] Garcia-Castro R. , Arino, M. and M. Canela. 2008. Does social performancereally lead to financial performance ? accounting for endogeneity. Mimeo IESE
Business School.
[29] Griffin, J. J., and J. F. Mahon 1997. The Corporate Social Performance andCorporate Financial Performance Debate: Twenty five years of Incomparable
Research. Business and Society, 36 (1), 5-31.
25

[30] Hillman, A.J. and J.D. Keim. 2001. Shareholder Value, Stakeholder Management and Social issues: Whats the Bottom Line? Strategic Management Journal, 22(2), 125-139.
[31] Holtz-Eakin, D, Newey, W. and H.S. Rosen. 1988. Etimating Vector Autoregressions with Panel Data. Econometrica, 56, 1371-1395.
[32] Ichniowski, C., Shaw, K. and G. Prennushi. 1997. The effects of Human resource Management Practises on Productivity: A Study of Steel FinishingLines. American Economic Review, 87, 291-313.
[33] Ichniowski, C. and K. Shaw . 2003. Beyond Incentive Pay: Insiders’ Estimates of
the Value of Complementary Human Resource Management Practices. Journalof Economic Perspectives . 17(1), 155-180.
[34] Kodde D. and Palm F. 1986. Wald Criteria for jointly testing Equality andInequality Restrictions. Econometrica, 54, 1243-1248.
[35] Leiponen, A. 2005. Skills and Innovation. International Journal of Industrial
Organization, 23(5-6), 303-323.
[36] Manasakis, C., Mitrokostas, E. and E. Petrakis. 2007. Corporate Social Responsibility in Oligopoly. BeNeTeCWorking Paper N. 2007-07.
[37] Margolis J. and J. Walsh. 2001. People and Profits? The Search for a Link between a Company’s Social and Financial Performance. Mahwah, NJ: Lawrence
Erlbaum.
[38] Margolis J., and J. Walsh. 2003. Misery loves companies: Rethinking social
initiatives by business. Administrative Science Quarterly, 48, 268-305.
[39] Margolis J., H. Elfenbein and J. Walsh. 2007. Does it pay to be good? A metaanalysis and redirection of research on the relationship between corporate socialand financial performance. Working paper Harvard University.
[40] Mcguire, J., Schneeweis, T. and J. Hill. 1986. An analysis of alternative meausres of strategic performance. in Lamb, R. and Stravasta P. (eds), Advances inStrategic Management, Vol. 4.
[41] McWilliams, A. and D. Siegel. 2001. Corporate Social Responsibility: A Theoryof the Firm Perspective. Academy of Management Review, 26, 117-127.
[42] McWilliams, A. and D. Siegel. 2000. Corporate social responsibility and Financial Performance: Correlation or Misspecification? Strategic Management
Journal, 21, 603-609.
[43] Milgrom, P. and J. Roberts. 1995. Complementarity and Fit: Strategy, Structure and Organizational Change in Manufacturing. Journal of accounting andeconomics.19 (2-3), 179-208.
26

[44] Milgrom, P. and J. Roberts. 1990. The Economics of Modern Manufacturing:Technology, Strategy, and Organization. American Economic Review 80(3),511-528.
[45] Mohnen, P. and L. Roller. 2005. Complementarities in Innovation Policy. European Economic Review, 49, 1431-1450.
[46] Nelling, E. and E. Webb. 2006. Corporate Social Responsibility and Financial
Performance. The Virtuous Cycle Revisited.
[47] Porter, M. and Kramer, M., 2002. The competitive advantage of corporate
philantropy. Harvard Business review., 80 (9), 48-58.
[48] Roodman, M. 2006. How to do Xtabond2: An Introduction to ”Difference” and”System” GMM in Stata. Working paper 103. Center for Global Development,
Washington.
[49] Scholtens, B. 2008. A note on the Interaction between Corporate Social Responsibility and Financial Performance. Ecological economics.
[50] Siegel,D. and D. Vitaliano. 2007. An Empirical Analysis of the Strategic use of
Corporate Social Responsibility. Journal of Economic Management and Strategy. 16(3), 773-792.
[51] Waddock, S. and S. Graves. 1997. The Corporate Social Performance - Financial
Performance Link. Strategic Management Journal, 18(4), 303-319.

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7

Appendix

7.1

Vigeo’s rating methodology

Vigeo measures companies’ CSR performance on 6 broad domains: Human Rights,
Environment, Human Resources, Business Behaviour, Corporate Governance, Community Involvement. All of the 6 domains are not investigated for the whole sample
by Vigeo because before companies are rated, an analysis is done to identify the
key CSR issues within the business sector. This determines which criteria in eachof the 6 domains will be activated. A ’weight’ is then assigned to each criterion
on a scale from 1 to 3, based on: the nature of the impact of the CSR issue on
the sector’s stakeholders; the exposure of stakeholders to that impact and the risks(legal, operational, etc.) run by companies in the sector that do not manage this
impact adequately. Once the evaluation criteria have been customized for the sector,
Vigeo’s analysis focuses on how each company addresses each criterion in terms of
Leadership, Implementation, and Results through a series of detailed questions:• Leadership– Visibility: types of policies in place?
– Content: content of these policies?
– Ownership: responsibility for these policies?
• Implementation– Means and resources: programs and tools in place?
– Scope: aspects addresses by these tools?
– Coverage: parts of the company covered by these tools?
• Results– Indicators: quantitative data?
– Controversies : stakeholder information ?
Each of these questions is scored on a scale from 0 to 100, representing the levelof firm’s CSR engagement and management of associated risks:
Vigeo Scores
PointsLevel of firm’s CSR engagement and rsik management (RM)
0
Little evidence of commitment - Poor to very poor guarantee of RM
30
Commitment initiated - Poor to moderate guarantee of RM
65
Consolidated commitment - Reasonable guarantee of RM
100
Advanced commitment - Social responsibility objectives actively promoted
28

Points given for each question are consolidated through a system of weighted
averages to give an overall score for each criterion and each domain (out of 100).
The score allocated to a company in each of the 6 domains is compared against
the scores of all other companies in the sector.
This score leads to one of the 5 different ratings, depending on the distributionof scores within the sector, on that domain:
Vigeo Ratings depending on the distribution of scores within the sector-Company ranked as the least performing in the sector
Company ranked as below average performer in the sector=
Company ranked as an average performer in the sector+
Company ranked as an active performer in the sector++
Company ranked as a leading performer in the sector

29

Figure 1a: Investments in CSR under weak and strong complementarity
t weak, t strong

0.4

0.3

0.2

0.1

4

5

6

7

8

9

10

y

Note : the dashed line corresponds to CSR investment under weak complementarity and the solid line corresponds to
CSR investments under strong complementarity

Figure 1b: Difference between CSR investments under weak and strong complementarity
t weak - t strong

0.01

20

30

40

50

y

-0.01

-0.02

-0.03

Figure2: Expected profits under weak and strong complementarity
EP weak, EP strong
4

2

5

6

7

8

9

10

y

-2

-4

Note : the dashed line corresponds to expected profits under weak complementarity and the solid line corresponds toexpected profits under strong complementarity.

Table 1. Descriptive statistics
Variable
Tobinq
Ln_SalesLn_Assets
Debt_ratio
RD_ratio
RD_dummy
DJSTOXX600 dummy
HR_dummy
ENV_dummy
CG_dummy
C_S_dummy

Mean
1.28
15.64
15.98
0.20
0.02
0.58
0.86
0.66
0.67
0.68
0.64

SD
1.17
1.36
1.33
0.14
0.05
0.49
0.35
0.47
0.46
0.45
0.47

Min
0.11
11.64
11.97
0
0
0
0
0
0
0
0

Max
11.26
19.55
19.45
1.13
0.72
1
1
1
1
1
1

Table A1: Variables definitions and measures
Variable
Tobinq
Ln_SalesLn_Assets
Debt_ratio
RD_ratio
RD_dummy
HR_dummy
ENV_dummy
CG_dummy
C_S_dummy

Definition(Market value of common equity + preferred stock + total debt)/Total assets
Natural log of firm’s annual net sales
Natural log of firm’ annual total assets
Long term debt divided by total assets
Research and Development expenses divided by total sales
Dummy variable = 1 if firms do not have reported R&D expenses
Dummy variable = 1 if firms have a human resources score equal or above theaverage sector
Dummy variable = 1 if firms have a environment score equal or above theaverage sector
Dummy variable = 1 if firms have a corporate governance score equal or above
the average sector
Dummy variable = 1 if firms have a business behaviour score (towards
customers and suppliers) equal or above the average sector

Table 2. CSR average scores per country
Country
Belgium
Denmark
FinlandFranceGermany
GreeceIreland
Italy
NorwayPortugal
Spain
Sweeden
Switzerland
Netherlands
United Kingdom

HR_score
33
33
42
43
42
19
17
33
44
37
35
33
36
42
37

ENV_score
36
35
37
35
40
15
14
30
43
37
35
38
37
37
41

CG_score
35
34
50
40
42
27
42
31
48
28
39
40
43
49
61

C_S_score
26
35
38
40
40
21
25
34
39
33
31
41
40
43
42

Table 3. CSR average scores per sector
SectorCommerce
Consumption
Construction
EnergyEquipment
Finance
Hotel
IAAIntermediary
ITC
Media
Telecom
Transport

HR_score

Env_score

CG_score

C_S

28
28
32
43
28
42
23
28
39
31
24
42
35

29
25
34
43
20
35
19
27
39
24
21
38
40

48
44
39
49
41
50
51
49
47
41
46
49
47

40
37
34
40
38
45
35
40
40
37
33
40
40

Table 4. Correlation matrix for CSR rates
CSR dummies
HR
ENV
CG
C_S

HR

ENV

CG

C_S

1
0.45910.25050.425110.24610.439110.30721

Chi-2 statistic is 0.001 for all pairs

Table 5. CSR states definition
CSR activities
Low scores for the four criteria
Human resources only (HR)Environment only (ENV)
Corporate governance (CG)Business behaviour (towards customers & suppliers; CS)
HR & ENV
HR & CG
HR & CS
ENV & CG
ENV & CGCG & CS
HR, ENV & CG
HR, CG & CS
ENV, CG & CS
HE, ENV & CS
HR, ENV, CG & CS

Combinations(0000)(1000)(0100)(0010)(0001)(1100)(1010)(1001)(0110)(0101)(0011)(1110)(1011)(0111)(1101)(1111)

Table 6. GMM System estimation – CSR rates interaction
Variables
Lag Tobinq
HR
ENV
CG
CS
HR & ENV
HR & CG
HR & CS
ENV & CGCG & CS
ENV & CS
Ln_Assets
Ln_SalesRD_ratio
NoRD_dummy
Debt_ratio
DJSTOXX600 index
Constant
Year dummies
Sector dummies
Countries dummies
AR1
AR2
Hansen test*aTobinq
SE a
0.090
0.149
0.236
0.154
0.200
0.198
0.166
0.265
0.235
0.269
0.207
0.044
0.028
0.299
0.028
0.001
0.050
0.339

Coeff
0.669***
0.162
0.188
0.151
0.414**
0.052-0.332**0.823***
0.397*-0.333-0.482**-0.126***
0.051*
1.274***
0.013-0.0010.136***
0.914***
Yes
Yes
Yes
p = 0.007
p = 0.215
p = 0.528

Significant at 10%; ** significant at 5%; *** significant at 1%
Robust standard errors are reported

Table 7. GMM System estimation – CSR states
Variables
Lag Tobinq
state1111
state1110
state1100
state1000
state1011
state1001
state0111
state0011
state0010
state0100
state0110
state0101
state1010
state0001
state1101
Ln_Assets
Ln_SalesRD_ratio
NoRD_dummy
Debt_ratio
DJSTOXX600 index
Constant
Year dummies
Sector dummies
Countries dummies
AR1
AR2
Hansen test*aTobinq
SE a
0.084
0.154
0.201
0.167
0.150
0.216
0.723
0.172
0.211
0.112
0.313
0.504
0.178
0.170
0.261
0.187
0.042
0.017
0.302
0.028
0.000
0.053
0.345

Coeff
0.723***
0.128
0.247
0.009
0.136
0.133
1.651***
0.293*
0.101
0.199*
0.276
0.051
0.026
-0.072-0.0710.417***-0.112***0.044*
1.138***
0.014
0.000
0.130***
0.962***
Yes
Yes
Yes
p = 0.008
p = 0.238
p = 0.674

Significant at 10%; ** significant at 5%; *** significant at 1%
Robust standard errors are reported