Does it pay off to ‘buy’ well?

Empirical Evidence from an M&A

Perspective

By

J.J. VAN ESSEN

S2377942

ABSTRACT Mergers and acquisitions (M&As) offer a framework to shed a new light on whether corporate

social responsibility (CSR) performance enhances corporate financial performance (CFP).

Using ASSET4 data as a measurement of CSR performance in a sample of worldwide deals for

the period 2004-2017, I find evidence that the environmental performance of target firms

enhances acquirers’ shareholder wealth. No influence is found for different value implications

in different institutional contexts. Additionally, shareholders reward (disvalue) acquirers even

stronger if the target is outperforming (underperforming) the acquirer in environmental

performance. These findings suggest that shareholders reward the acquirer for making

environmental investments and support the stakeholder view, which indicates that fulfilling

stakeholder interests can be combined with shareholder wealth creation.

Keywords: Corporate social responsibility (CSR), M&As, stakeholder view, institutional

frameworks, abnormal announcement returns.

DD MSc International Financial Management (UoG/UU)

Faculty of Economics and Business

University of Groningen

Supervisor: Prof. dr. C.L.M. Hermes

Co-Assessor: Prof. dr. M. Ararat

JEL classification: G340

12 January 2018

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1. INTRODUCTION

An increasing number of firms worldwide are integrating corporate social responsibility

(CSR) activities into various aspects of their businesses. Recent research demonstrates that

firms increasingly invest a growing amount in CSR activities to satisfy market demands

(Bhandari and Javakhadze, 2017). According to the Global Sustainable Investment Review

(2016), assets in socially responsibility investing (SRI) have grown with 25.2% since 2014. In

2016, there were $22.89 trillion of assets in SRI portfolios, which is 26.3% of all assets under

management. Given all these resources allocated to CSR activities, it is important to get a better

understanding of the effects of CSR on corporate financial performance (CFP). Although there

is recognition of the importance of CSR, a clear consensus in the current debate on the impact

of CSR on CFP is missing (Margolis and Walsh, 2003). Some studies show a negative or non-

existent relation (see, e.g., Griffin and Mahon, 1997; Waddock and Graves, 1997; Harrison and

Freeman, 1999), while others show a positive relation between CSR and CFP (see, e.g., Cochran

and Wood, 1984, Roman, Hayibor, and Agle, 1999; Brammer and Millington, 2005). These

mixed empirical results are mainly based on the theoretical foundations of the opposing

classical shareholder expense and stakeholder view. In conclusion to the overall literature,

meta-analyses and literature reviews indicate a slightly positive overall effect of CSR on CFP

(see, e.g., Orlitzky, Schmidt, and Rynes, 2003; Margolis, Elfenbein, and Walsh, 2009).1 These

studies usually try to answer whether firms do well by doing good.

In view of this contradictory evidence, the question whether CSR performance is beneficial

or detrimental for CFP remains largely open. Therefore, this study takes a different approach

by trying to answer whether firms do well in terms of shareholder wealth2 by ‘buying’ well.

More specifically, this research conducts an analysis based on mergers and acquisitions (M&As)

to shed light on the shareholder value implications of CSR. It analyses the role of target firms

CSR performance and the difference in acquirer’s and target’s CSR performance (ATCSRD)

on acquirers’ short-term announcement return. In doing so, it aims to find the answer to the

question whether it pays off for firms to acquire other firms which perform well on CSR. A

unique M&A market framework is used, with acquirer and target measures of CSR performance,

for the following three reasons. First, M&As are important strategic investment decisions with

a significant effect on CFP (Healy, Palepu, and Ruback, 1992), and specifically shareholders’

wealth (see, e.g., Doukas and Travlos, 1988; Agrawal, Jaffe, and Mandelker, 1992; Masulis,

1 Margolis et al. (2009) analysed 167 studies, of which only 22 use non-U.S. data. Of these 22 studies, only 3

studies use a multiple country sample with firm-level measures. 2 The terms CFP and shareholder wealth are used interchangeably.

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Wang, and Xie, 2007). Furthermore, M&A deals involve the support and challenges of various

stakeholders in the approval and post-deal integration process between the acquirer and target

firm (Deng, Kang, and Low, 2013). Finally, prior studies investigating the relation between

CSR and CFP had some problems with reverse causality in both variables. Consequently, the

question remains whether firms do good by doing well or do well by doing good (Waddock and

Graves, 1997; McWilliams and Siegel, 2001). This omnipresent reverse causality issue can be

alleviated by the M&A framework, as M&A deals are namely largely unanticipated events. By

using short-term announcement returns the direct influence of CSR investments to shareholder

wealth can be captured (Krüger, 2015). Additionally, the short-term market-based abnormal

returns used in this study give better insights in the risk-adjusted discounted future cash flows

in comparison with accounting-based measures, which measure historical performance and are

highly sensitive for differences in accounting procedures and managerial manipulation

(McGuire, Sundgren, and Schneeweis, 1988; Brammer and Millington, 2008).

Using a sample of global public firms and 309 completed deals, this study finds strong

evidence of a positive effect of targets’ environmental performance on acquirers’ abnormal

returns. The targets’ social and combined CSR performance has no impact on the acquirers’

abnormal returns. These findings suggest that acquirers are rewarded for environmental

investments, but not for social investments. Moreover, shareholders reward (disvalue) acquirers

even stronger if the target is outperforming (underperforming) the acquirer in environmental

performance. Overall, the results provide further evidence that environmental specific CSR

investments are value creating for shareholders.

This research contributes to the existing empirical work on the effect of CSR performance

on shareholder wealth in multiple ways. First, prior studies focused on the empirical

examination of the correlation between CSR performance and firm value (see, e.g., Jo and

Harjoto, 2011; Servaes and Tamayo, 2013) or on the effect of CSR on CFP measures (see, e.g.,

Griffin and Mahon, 1997; Margolis and Walsh, 2003). This study, however, examines the

causal link between both targets’ CSR performance and ATCSRD on acquirers’ short-term

abnormal returns controlling for reverse causality. Hereby, a clear channel through which CSR

performance can potentially influence shareholders wealth can be clearly identified. Moreover,

to the best of my knowledge this is the first study that explicitly looks at the ATCSRD and

hence gives an interesting opening in the CSR-M&A field. Next, prior empirical evidence on

the CSR-CFP relation comes mainly from the U.S. (Margolis et al., 2009). However, several

recent scholars emphasize the major importance of country-level differences to analyse the

effect of CSR on CFP (El Ghoul, Guedhami, and Kim, 2017). Therefore, a large international

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M&A sample, which includes 36 different acquirer nations to targets based in 33 nations is used

in this paper. This makes this study, the first to examine cross-country variation in shareholder

wealth as a result of CSR investments in the M&A field.

The remainder of this paper is structured as follows. The next section presents the

theoretical and empirical foundations for this study. Section 3 discusses the data and the

empirical methodology. Subsequently, section 4 presents and discusses the main empirical

results of the univariate, multivariate regression, and portfolio analyses. The final section

concludes, discusses implications and provides ideas for further research.

2. THEORETICAL BACKGROUND AND HYPOTHESES

2.1. CSR and CFP: A theoretical framework

Two opposing fundamental perspectives give an insight on the relation between CSR and

CFP. The classical shareholder expense view sees CSR as costly and therefore value decreasing

for firms. Accordingly, firms should focus on maximizing shareholders’ wealth and leave social

responsibility decisions to shareholders themselves (Friedman, 1970). 3 In contrast, the

stakeholder view argues that the interest of shareholders should not be the only concern of firms.

According to this view, firms should conduct CSR activities due their responsibility to any

entity or person that is affected by their activities (Freeman, 1984; Donaldson and Preston,

1995). This view therefore emphasizes a firm’s societal role. More specifically, the stakeholder

view holds that firms benefit from developing stakeholder trust through reduced transaction

costs (Williamson, 1989; Jones, 1995). This reasoning implies that CSR satisfies the interests

of stakeholders and accordingly their willingness to support the firm (Donaldson and Preston,

1995). Hence, more CSR investments can be beneficial for all stakeholders, shareholders

included. In the context of this study, deals including targets with high CSR performance

(hereafter, high CSR targets) resulting in higher acquirers’ abnormal returns are in line with the

stakeholder view due to the value creation for shareholders.

The stakeholder view is in alignment with the contract theory, which views a firm as a

network of contracts between the owners of the firm (shareholders) and other stakeholders

(Jensen and Meckling, 1976; Cornell and Shapiro, 1987). Cornell and Shapiro (1987) state that

stakeholders support the firm with critical resources in exchange for explicit and implicit claims.

Unlike explicit claims, implicit claims (such as job satisfaction and pollution reduction) are

3 Among others Jensen (1986) and Jensen and Meckling (1976) built upon this by stating that CSR is an inevitable

outcome of agency conflicts between managers and shareholders.

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imprecise and have no juridical standing. As such, the prices of implicit claims depend on

stakeholders’ expectations about a firm fulfilling these claims (Cornell and Shapiro, 1987).4

Acquiring firms which invest in CSR, by taking over high CSR targets, have a strong reputation

for fulfilling implicit claims (Deng et al., 2013). This kind of acquisition can be seen as a signal

to learn from the CSR performance of the target firm (Aktas, De Bodt, and Cousin, 2011).

Stakeholders of these acquirers are therefore more willing to support the firm with critical

resources (Russo and Fouts, 1997), which is beneficial for shareholders. Studies such as Shane

and Spicer (1983), Fombrun and Shanley (1990), and Orlitzky et al. (2003) assert that CSR

investments help to build a more positive reputation for firms. More specifically, the relation

between valuable intangible resources of target firms and CFP is researched by Betton and

Eckbo (2000). They report that one of the most important determinants of the acquirers’

abnormal announcement returns is the target’s reputation.

Adding the resource-based view (RBV) of Barney (1991) to this line of reasoning gives a

comprehensive understanding of the potential value enhancement of acquiring high(er) CSR

targets. Barney (1991) argues that resources and capabilities can be a source of sustainable

competitive advantage if they are rare, valuable, inimitable, and non-substitutable. These

criteria are often met by critical intangible resources such as human capital and firm reputation

(Hall, 1992). These resources are respectively closely linked to the social and environmental

dimension of CSR performance used in this study. Hart (1995) was among the first who linked

the RBV framework to the CSR field by addressing the fact that CSR activities, in particular

environmental performance, can constitute a critical resource that leads to a sustainable

competitive advantage. M&As are a good opportunity for firms to take over or to develop these

critical resources which can achieve and sustain competitive advantage (Cochran and Wood,

1984; Waddock and Graves, 1997). Among others, Wickert, Vaccaro, and Cornelissen (2017)

researches this reasoning in practice and describes that Procter & Gamble’s CFP is creditable

to their CSR behavior and reputation. They state that once gained, a pro-CSR reputation is a

valuable inimitable resource. For firms, it is difficult to make or replicate these valuable

resources in the short-term. Therefore, ‘buying’ such resources is a growing trend (Kearins and

Collins, 2012) among firms to enhance their own CSR performance from targets (Mirvis, 2008).

4 Godfrey (2005) theoretically describes the relation between stakeholders’ expectations and shareholder wealth.

He states that CSR investments contribute to positive moral among stakeholders and this moral contributes

subsequently to shareholder wealth. Jiao (2010) tested this theory and find that shareholder wealth is enhanced if

a firm meets the expectation of all their stakeholders. He states that this positive effect is mainly driven by

environmental performance and employee welfare both representing intangible resources such as reputation and

human capital respectively.

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The acquisitions of Unilever–Ben & Jerry’s and L‘Oréal-The Body Shop are good

representations of acquirers buying CSR by taking over high CSR targets (Wickert et al., 2017).

2.2. CSR and CFP: Empirical evidence

Over the past decades, many scholars examined the relation between CSR and CFP, often

based upon the aforementioned shareholder expense and stakeholder view. Although the lack

of complete consensus, qualitative reviews (Margolis and Walsh, 2003) and meta-analyses

(Orlitzky et al., 2003; Allouche and Laroche, 2005; Margolis et al, 2009) 5 conclude a

statistically strong but economically modest positive effect of CSR on CFP. Several other

studies evaluate the CSR-CFP relation from an investment perspective, comparing SRI funds

with conventional funds. Among others, Anderson and Frankle (1980), Statman and Glushkov

(2009), and Derwall, Koedijk, and Ter Horst (2011) show that SRI funds outperform

conventional ones. Conversely, other researchers find results consistent with shareholders

paying a price for CSR (see, e.g., Renneboog, Ter Horst, and Zhang, 2008; Hong and

Kacperczyk, 2009; Borgers et al., 2015). Finally, some other studies find no performance

differences between SRI and conventional ones (see, e.g., Hamilton, Jo, and Statman, 1993;

Bauer, Koedijk, and Otten, 2005; Schröder, 2007).

It is widely argued in the literature that firms with high CSR performance have certain

benefits in the capital market, leading to better CFP. Taking an accounting approach, Watts and

Zimmerman (1979) argue that CSR investments lead to a higher supply of information. This

results in lower costs of obtaining information and consequently in lower cost of capital. For

firms, this lower cost of capital can be used for more positive net present value (NPV)

investments, which gives rise to higher shareholder wealth (Lamont, Polk, and Saaá-Requejo,

2001). For example, Cheng, Ioannou, and Serafeim (2014) use the environmental and social

dimension of the ASSET4 database and discover that U.S. firms with better CSR performance

have fewer capital constraints due to lower agency costs and less information asymmetry

through stakeholder engagement. This relation is mainly driven by the environmental

dimension of CSR performance. This is also the dimension where Chava (2014) focuses on. He

finds that firms with high environmental concerns have a higher cost of debt and their

shareholders require higher returns. Similarly, other studies show a significant positive

influence of more CSR investments and the cost of equity capital (Richardson and Welker,

5 For example, Margolis and Walsh (2003) review studies with CSR as the independent variable and conclude that

out of 109 studies, only 7 found a negative relation, 28 showed a non-significant relation, 20 reported mixed results

and 54 showed a positive relation.

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2001; Dhaliwal et al., 2011; El Ghoul et al., 2011). In addition, Goss and Roberts (2011) study

the cost of debt and report a U-shaped relation between CSR involvement and the cost of capital.

In accordance, Barnett and Salomon (2006) find a U-shaped relationship between CSR

performance and CFP.

2.3. Linking CSR and shareholder wealth

The extant event-study literature claims a strong positive association between CSR

involvement and short-term shareholder wealth as a measure of CFP. One of the first studies

which employed the event study methodology in the field of CSR was Davidson and Worrell

(1988). They use 131 announcements of corporate illegalities as a proxy for social

irresponsibility and report a significant negative effect on stock returns. Following this study,

Hall and Rieck (1998) investigate the effect of the announcement of voluntary positive CSR

actions, measured by recycling, donation, social policy, and environmental-friendly activities

on returns. They show no statistically significant returns for the whole sample, but a significant

positive influence is found for announcing donations and environmental-friendly activities. A

more direct relation of CSR events and shareholder wealth is researched by Krüger (2015). He

finds value creation effects of CSR investments. More specifically, he argues that shareholders

react negatively to negative related CSR news and concludes that positive CSR activities are in

the shareholders’ interests. A focus on environmental investments is taken by Klassen and

McLaughlin (1996) and Flammer (2013). They report that environmental responsible firms face

a significant stock price increase, whereas environmental irresponsible firms have a significant

decrease. A more social direction is investigated by Edmans (2011) who finds a positive relation

between job satisfaction and stock returns. He adds to this evidence that engaging in CSR

activities results in higher abnormal shareholder returns in the short-term. Overall, prior event

studies in the CSR field indicate a significant influence of CSR performance on shareholder

wealth. However, all studies use a U.S. sample. To draw generalized conclusions, it is essential

to shift the focus to new non-U.S. evidence.

2.4. CSR in M&A context

A limited amount of studies introduced M&As in the CSR debate. The empirical studies

of Aktas et al. (2011) and Deng et al. (2013) show a positive impact on shareholder returns

using the target and acquirer CSR performance as independent variable. Aktas et al. (2011)

utilize Innovest’s Intangible Value Assessment (IVA) ratings as a measure of CSR performance

to test a worldwide M&A sample in the period 1997-2007. Their small sample consisting of

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106 deals includes financial and utility firms and is highly concentrated in the last three years

(80.2%) and dominated by U.S. and U.K. acquirers (41.7%). The authors, controlling only for

deal-specific characteristics, find evidence of a significant positive influence of target firms’

environmental and social performance on the acquiring firm’s returns. Moreover, they try to

explain the source of the value creation by a learning view. Additionally, Deng et al. (2013)

contribute to the CSR on shareholder wealth debate by focusing on the acquirers’ CSR

performance. Using KLD data6 in a sample of 1,556 U.S. mergers in the period 1992-2007,

they compare low with high CSR acquirers. In comparison with low acquirers, high acquirers

have significant higher short-term stock returns, long-term stock returns, and long-term

operating performance. In addition to this, deals including high acquirers have faster completion

time and a higher probability to succeed. The findings of Deng et al. (2013) support the

stakeholder view and are inconsistent with the shareholder expense view. However, their results

are confined to U.S. mergers with similar market institutions, thereby neglecting cross-country

differences. This study empirically elaborates upon the findings of Aktas et al. (2011) and Deng

et al. (2013) by revisiting the small sample results of Aktas et al. (2011) through investigating

the influence of targets’ CSR performance. Additionally, this paper goes beyond research on

U.S. data and examines the impact of the ATCSRD on acquirers CFP.

As previously stated, investing in high CSR targets can be a direct manner to ‘buy’ critical

and difficult to replicate resources from the target. Next, a deal involving high CSR targets can

have an indirect impact by enhancing stakeholders’ expectations related to fulfilling implicit

claims (Cornell and Shapiro, 1987), which in turn lead to more willingness to support the

acquirer with critical resources (Hart, 1995; Russo and Fouts, 1997). Additionally, these

obtained critical resources, such as the reputation of the target (Fombrun and Shanley, 1990),

can act as a source of sustainable competitive advantage for the acquirer (Barney, 1991; Hart,

1995). Thus, it is a positive signal to stakeholders, including shareholders showing their

willingness to invest in CSR (Aktas et al., 2011). More CSR investments can also enhance the

acquirer’s access to capital (see, e.g., Cheng et al., 2014) making investments in positive NPV

projects easier. The overall empirical evidence, using accounting-based and market-based

measures, also demonstrates a slightly positive effect of CSR investments on CFP (Orlitzky et

al., 2003). Thus, the interests of stakeholders and shareholders are in greater alignment if

acquirers invest in high CSR targets and as a result these investments enhance acquirers’

shareholder wealth. Therefore, deals including a high CSR target are rewarded by shareholders

6 See Section 3.1. for differences between the IVA, KLD, and ASSET4 data.

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resulting in higher abnormal returns around the M&A announcement. Building on the

foundation of the empirical evidence of Aktas et al. (2011) and the aforementioned theories,

the following hypothesis is developed:

Hypothesis 1: Higher CSR performance of the target has a positive effect on the acquirers’

CFP.

Furthermore, it can be expected that this positive influence on acquirers’ CFP is higher for

larger ATCSRD. Investing in a higher CSR performing target can act as a positive signal to all

stakeholders showing the willingness to invest in CSR (Aktas et al., 2011). This signal of

willingness to invest can have more impact on all stakeholders if the ATCSRD is greater.

Moreover, greater ATCSRD can lead to a higher probability of improving its relationships with

target stakeholders. In addition, Aktas et al. (2011) find that the acquirer CSR rating increased

significantly after the deals, without considering the differences between CSR performances.

In this study, I adopt the view taken by Wang and Xie (2008), stating that greater differences

mean higher learning potential for the acquirer. Accordingly, shareholders will notice this deal

as a more wealth enhancing investment, leading to higher acquirer’ abnormal announcement

returns. Wang and Xie (2008) indicate that shareholder wealth creation in M&As increases with

a higher difference in corporate governance between the acquirer and the target. The

expectation is that these synergistic gains for acquirers also results from larger ATCSRD.

Moreover, the reputation effects for the acquirer can be more positive and therefore valuable in

the case of greater relatively differences between the target and acquiring firm. Therefore,

acquiring a relatively higher CSR performance target have a positive effect on the abnormal

returns of the acquirer and these synergies becomes larger in the case of greater differences.

This results in the following hypothesis:

Hypothesis 2: The larger the difference in CSR performance between acquirer and target, the

higher the acquirers’ CFP if the acquirer has a lower CSR performance relative to the target.

2.5. The role of institutional frameworks

Institutions, also known as ‘the rules of the game’, support the effective functioning of

market mechanisms (Meyers et al., 2009) in a way that shareholders and firms can participate

in market transactions without disproportionate transaction costs (North, 1990). Moreover,

Bevan, Estrin, and Meyer (2004) report that institutions lower the costs of transactions. Prior

research indicates that the quality of institutions differs between countries (Khanna, Palepu, and

Sinha, 2005). Hence, it is important to consider the quality of institutions in the relation between

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acquirer’ and target’ CSR performance and shareholder wealth for the constituted international

sample. Institutional frameworks are considered weak if they fail to ensure effective markets

and strong if they support the voluntary exchange which acts as a foundation for an effective

market mechanism (Meyers et al., 2009). As a result of weak institutional frameworks, market

failures occur. Firms have to search for strategic ways to overcome these market failures

(Khanna and Palepu, 1997). El Ghoul et al. (2017) state that CSR involvement can be a solution

to overcome these failures. They use ASSET4 data in a sample of 11,672 observations and

2,445 firms grounded in 53 countries over the 2003-2010 period and report a positive relation

between CSR performance and firm value, measured by Tobin’s q. This study, by using a

similar approach integrates the market-supporting institutions in the CSR-M&A framework.

In this paper, institutions include business regulations, legal systems and property rights,

and capital markets. A lower quality of these institutions can result in certain market failures.

First, underdeveloped stock and credit markets make it difficult for firms to finance investments.

Additionally, a low quality of financial information intermediaries (such as analysts, press,

investment banks) result in increasing information asymmetry between managers and

shareholders, which in turn, leads to higher transaction costs and thus less access to capital

(Meyers et al., 2009). However, CSR investments can act as a substitute for these market

failures. As described in Section 2.3, Cheng et al. (2014) and Dhaliwal et al. (2011) provide

evidence that CSR investments can increase the access to capital by reducing information

asymmetry and subsequently transaction costs (North, 1990). Second, business regulations and

legal systems and property rights affect firms in doing business. For instance, low legal

enforcement of explicit claims will result in inefficiencies on product markets (Khanna and

Palepu, 1997). Consequently, firms should search for other ways to ensure that other parties

hold their part of the bargain (El Ghoul et al., 2017). Also, state intervention results in

uncertainties for doing business and inefficient markets. CSR investments can substitute this

market failure of lower legal enforcement relating explicit claims, by improving the

stakeholders’ expectations and value of implicit claims (Cornell and Shapiro, 1987). This helps

building long-term relationships with important stakeholders, which can support the firm with

more voluntary exchanges of critical resources (Barney, 1991). All in all, the high(er) CSR

performance of targets can be seen as a non-market mechanism which can help overcome

market failures in the acquirer country. The potential value of these CSR investments is likely

to be higher in acquirer countries with weaker institutional frameworks. As mentioned above,

certain market failures resulting from weak market-supporting institutions (such as

underdeveloped capital markets, business freedom, and legal system and property rights) can

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be substituted by CSR investments. Therefore, similarly to El Ghoul et al. (2017), this study

argues that the value of CSR investments differs for different country-level institutions. In

specific, it is suggested that the influence of the targets’ CSR performance on acquirers’

shareholder wealth is strengthened by weaker institutional frameworks. Combining these

arguments, it can be expected that:

Hypothesis 3: The CSR performance of target firms is valued more in countries with weaker

institutional frameworks, which results in higher acquirers’ CFP.

3. DATA AND METHODOLOGY

3.1. CSR measurement

CSR is operationalized by taking an equal-weighted average of the environmental (ENV)

and social (SOC) scores, which results in an overall CSR score- namely the corporate social

performance score (CSP). Detailed definitions and specific computation methods of all the

variables used in this study are described in Appendix A. The both measures are derived from

the ASSET4 ESG Database provided by Thomson Reuters. This is in accordance with recent

prior CSR studies (Ioannou and Serafeim, 2012; Cheng et al., 2014; El Ghoul et al., 2017), but

in contrast to prior CSR empirical research in the context of M&As. Deng et al. (2013) obtained

their aggregated absolute CSR rating from Kinder, Lydenberg and Domini (KLD) Research

and Analytics Inc. STATS database. This data set contains negative (concerns) and positive

(strengths) ES performance indicators and is one of the most comprehensive ES data time series

available, but only contains U.S. firms. Subsequently, a major disadvantage of the KLD data,

is the lack of adjustable weights for all the individual strengths and concerns (McGuire et al.,

1988). Hence, the assumption of equal importance of the strength and concerns scores is

inappropriate, because they are both conceptually and empirically different constructs

(Mattingly and Berman, 2006). Next, Aktas et al. (2011) use the discrete IVA provided by

Innovest as a measurement for CSR. This database links managerial ability of ES related risks

and opportunities to long-term outperformance. IVA research combines 120 performance

indicators under four pillars: environment, human capital, stakeholder capital and strategic

governance. Companies are rated on a seven-point scale (‘AAA’-‘CCC’) relative to their

industry peers.7

ASSET4 gathers ES data on around 5000 global companies during the period 2002-2017.

The ASSET4 framework compares and rates companies against over 750 publicly available

7 KLD STATS database and IVA data are now transitioned to the MSCI ESG indices.

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data points. These data are accumulated into 280 key performance indicators (KPIs), which

serve as subcomponents of 18 categories (Thomson Reuters, 2013). The categories are grouped

into four main pillars reflecting sustainability: economic, environmental, social and corporate

governance. The pillar scores are calculated by equally weighting and z-scoring all data points.

By using a z-score, a pillar score reflects the performance of one company compared with the

average performance of all the companies included in the ASSET4 database. The resulting ES

pillar scores are therefore a relative measure of CSR performance, which is in line with the IVA

rating, but in contrast with the absolute KLD rating. The ES scores are presented as values

between zero and 100, making them more precise than the KLD and IVA ratings. To the best

of my knowledge, this is the first study using the environmental and social scores of ASSET4

as an explanatory variable in the context of M&A deals.

In addition to the aggregated CSP score, regressions are run on the disaggregated scores to

analyse the differences in influence on abnormal returns. All these individual pillars reflect the

generation of long-term shareholder value using best management practices and capturing

environmental and social opportunities. More specifically, the environmental pillar (ENV)

represents a firm’s influence on non-living and living natural systems, comprising water, soil,

air and complete ecosystems. This measure includes for example resources and emission

reduction, and beneficial product innovation for the environment. The social pillar (SOC)

focuses on evaluating a firm’s capacity in the generation of trust and loyalty with its customers,

society and employees. It displays the healthiness of a firm’s license to operate and its

reputation. For example, investments in employee training and development, health and safety,

diversity, human rights and customer/product responsibility are included in this measure

(Thomson Reuters, 2013).

3.2. Institutional framework measurement

As an index of the strength of institutional frameworks (IF) of the acquirers’ nation, this

study follows El Ghoul et al. (2017) and uses an equally weighted average of stock market

development (SMD), credit market development (CMD), business freedom (BF), and legal

system and property rights (LSPR). All the proxies are standardized to increase the

comparability. Both capital market proxies are obtained from the database World Development

Indicator (WCY), whereas business freedom and legal system and property rights are derived

from Fraser Institute’s Economic Freedom of the World (EFW). Both databases contain time-

series data from 2004 to 2017 for all the proxies. More specifically, stock market development

is measured by taking an equally weighted average of stock market capitalization over gross

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domestic product (GDP), total value of shares traded over GDP, and total value of shares traded

over market capitalization. This combined indicator of stock market development is used by,

among others, Pagano (1993). Credit market development is defined as the total volume of

credits provided by the financial sector divided by GDP, following Fauver, Houston, and

Naranjo (2003).

3.3. Control variables

To test the hypotheses, other factors than the firms’ CSR performances need to be controlled

for. In particular, this study includes firm-specific and deal-specific characteristics following

leading M&A research (Masulis et al, 2007; Deng et al., 2013). All acquirer and target

characteristics variables are obtained from Datastream, while all deal-specific control variables

are from the SDC database. Regarding firm-specific characteristics, five control variables are

considered. Large firms often overestimate potential synergy gains and overpay for targets

based on the hubris hypothesis (Rau and Vermaelen, 1998). Consistent with the hubris

hypothesis Moeller, Schlingemann, and Stulz (2004) find that large firms pay higher premiums

and enter deals with negative synergies, resulting in lower abnormal returns. Hence, I include

the natural logarithm of the market value of equity to control for acquirer size (ASIZE). Second,

the profitability of the acquirer, measured by the return on assets (AROA) is used as a control

variable in this study, in line with leading prior studies (Easton and Harris, 1991). Moreover,

Lang, Stulz, and Walkling (1991) find that acquirer returns are significantly negatively related

to higher free cash flows (AFCF). This finding is built upon Jensen’s free cash flow hypothesis

(1986), stating that managers of acquirers with large free cash flows are more likely to invest

in less beneficial or value destroying M&As rather than paying it out to shareholders. In order

to control for more profitable targets, I include the targets return on assets (TROA). This

measure influences the abnormal returns of the acquirer by making targets more attractive for

bidders and thus costlier (Shawver, 2002). Additionally, the target’s Tobin’s q (TTQ) is

positively related with acquirer returns (Lang et al., 1991; Servaes, 1991) and therefore included

as a control variable.

Next to firm-specific controls, I include deal-specific characteristics as well. Relative deal

size is an important determinant of the acquirer returns (RELDS), and works as a scaling

variable for bidder returns (Moeller et al., 2004). Moreover, I use an industry diversification

(INDDIV) dummy as certain studies find negative market reactions on diversifying deals.

Morck, Shleifer, and Vishny (1990) and Doukas, Holmen, and Travlos (2002) find value

destroying results for diversifying deals driven by managerial self-interest. Another driver of

14

abnormal returns is the number of bidders. Competition among bidders (COMP) increases the

bargaining power of targets, consequently drives up the premium and decreases the acquirer

returns (Bradley, Desai, and Kim, 1988; Moeller et al., 2004). Furthermore, whether the deal is

cross-border or domestic (DOM) has certain implications for the acquirer returns. Announcing

a cross-border M&A can be seen as an exploitation of foreign market distortions and is therefore

positively valued by shareholders (Eckbo, 1983; Doukas and Travlos, 1988). The last deal-

specific control variable used in this study is the method of payment (METHOD). Stock-

financed deals are known to have a negative influence on acquirer abnormal returns (Travlos,

1987; Servaes, 1991). These findings are generally attributed to the equity signaling hypothesis

of Myers and Majluf (1984), which state that stock payment by the acquirer signals

overvaluation of their equity by the market.

3.4. Sample selection and distribution

The initial M&A sample is extracted from Thomson ONE (SDC Mergers and Acquisitions

database). The sample selection procedure and corresponding number of observations are

presented in Table 1. Initial bids announced between January 20028 and September 2017 are

selected according to the following criteria:

i. Completed merger or acquisition deals from public listed acquirers and targets to ensure

the availability of financial data;

ii. Deal value is at least $1million and acquirer has a majority ownership after transaction

to ensure the relevance of the data;

iii. The financial- (SIC codes 6000-6999) and utility (SIC codes 4900-4999) sectors are

excluded, because the applied special regulations and the differences in debt levels make

them hardly comparable.

These restrictions follow extant data criteria of M&A literature (Fuller, Netter, and

Stegemoller, 2002; Deng et al., 2013), and result in an initial sample of 6,044 completed M&A

transactions. Acquirers and targets which are not listed in the ASSET4 Database are excluded

from the sample. Merging the M&A deals from SDC with the ASSET4 data results in a sample

of 503 deals. From these 503 deals, both acquirer and target need to have ES data available

prior the announcement date, which is the case for 361 deals. Out of the 361 deals, abnormal

returns of 352 could be computed with stock prices obtained from Thompson Reuters

8 ASSET4 coverage starts from 2002. Therefore, I include deals from 2002 onwards.

15

Datastream. In the end, the full sample encompasses 309 different deals, with an average deal

value of $967.47 million, with all company-specific data available.

Table 1. Sample selection procedure.

Selection step Number of Obs. Number of missing Obs.

Acquirer public status 441,419 Initial sample 352

Target public status 100,128 AROA 3

Deal status complete 49,962 AFCF 35

Deal value minimal 1US$ million 39,030 TROA 13

Majority ownership after transaction 19,637 TTQ 12

Date effective between 2002-2017 9,522 IF 43

Excluding financial and utility acquirers 6,044

Acquirer and target in ASSET4 503

ES data available for acquirer and target 361

Actual returns acquirer 352 Final sample 309

Panel A through D of Table 2 gives a comprehensive overview of the breakdown in

countries, announcement years, and industries of both acquirers and targets in the full sample.

Panel A presents the country distributions and shows that the sample contains deals from 36

different nations to targets based in 33 nations. Most of the acquirers are from the U.S. (36.6%),

Japan (9.4%) and U.K. (8.4%).9 This distribution is comparable to the primary unrestricted

sample of 6,044 deals obtained from the SDC database10, which contains deals from the U.S.

(29.5%), Japan (20%), and U.K. (6%). The most frequent target nation is the U.S. (42.4%),

followed by U.K. (9.4%), Australia (9.1%), and Japan (6.2%). The initial sample has a

distribution in these countries of respectively 30%, 5.6%, 6.9%, and 18.5%. Thus, the sample

contains relatively a higher number of acquirers from the U.S. and U.K. in comparison with the

initial sample. A reason for this is the higher inclusion of U.S. and U.K. companies in the

ASSET4 database.

Panel B reports the distribution by year. The number of M&A deals increase gradually and

peak in 2015. A concentration of deals in the later years of the observation period can be

identified, around 68% of the deals are from the second halve of the sample period. In contrast,

the initial sample shows a constant number of deals during the 2002-2007 period. The main

reason is the availability of ASSET4 ES data. To be included in the database, the firms need to

have at least three years of history available, and most firms are covered from 2005 onwards.

9 This distribution is in accordance with the sample of Liang et al. (2017), who use a similar timeframe. 10 See Appendix B for the sample distribution of the initial sample.

16

Furthermore, the global financial crisis in 2008 could explain the small downfall of deals in the

years 2008 and 2009.

The industry distributions of the acquirer and target are presented in Panel C and Panel D

respectively. The acquirers and targets are classified on the two-digit SIC codes and distributed

into six and seven main industries. Panel C shows that the majority of the acquirers and targets

are active in the manufacturing industry (respectively 50.8%11 and 44.7%), while a relatively

small amount of acquirer and targets are from the construction (respectively 2.2% and 2.9%)

and wholesale and retail trade (respectively 5.8% and 8%) industry. Note, the industry

distribution of both acquirer and targets, except the 5% changes in manufacturing and wholesale

and retail trade, are quite similar.

11 In consensus with Deng et al. (2013), their sample consists of 57.2% manufacturing acquirers.

17

Table 2. Sample distribution. This table presents the sample distribution by country, year, and industry. The sample of the full

model consists of 309 observations from 36 acquirer countries to 33 target countries in 6 different industries over the 2004-2017

period. The following main two-digit SIC industry classification, obtained from SDC, is used: mining (10-14), construction (15-

17), manufacturing (20-39), transportation (40-49), wholesale and retail trade (50-59), real estate (65) (only targets), and services

(70-89). The sample is obtained from the Thomson ONE SDC Database. The selection criteria are described in Section 3.4.

Panel A. Sample distribution by country Panel B. Sample distribution by year

Acquirer Target

Country N % Country N % Year N %

Australia 21 6.80 Australia 28 9.06 2004

2 0.65

Austria 2 0.65 Austria 3 0.97 2005

6 1.94

Bahrain 1 0.32 Bahrain 1 0.32 2006

17 5.5

Belgium 2 0.65 Belgium 1 0.32 2007

26 8.41

Brazil 3 0.97 Brazil 4 1.29 2008

12 3.88

Canada 19 6.15 Canada 18 5.83 2009

12 3.88

Chile 1 0.32 China 2 0.65 2010

25 8.09

China 1 0.32 France 10 3.24 2011

23 7.44

Denmark 1 0.32 Germany 5 1.62 2012

26 8.41

Finland 4 1.29 Gibraltar 1 0.32 2013

12 3.88

France 11 3.56 Greece 2 0.65 2014

36 11.65

Germany 19 6.15 Hong Kong 1 0.32 2015

54 17.48

Gibraltar 1 0.32 India 4 1.29 2016

51 16.5

Greece 2 0.65 Ireland-Rep 1 0.32 2017

7 2.27

Hong Kong 1 0.32 Italy 4 1.29 Total 309 100

India 3 0.97 Japan 19 6.15

Ireland-Rep 2 0.65 Kuwait 1 0.32

Isle of Man 1 0.32 Luxembourg 2 0.65 Panel C. Sample distribution by industry acquirer

Israel 1 0.32 Mexico 3 0.97 Industry N %

Italy 3 0.97 Morocco 1 0.32 Mining 42 13.59

Japan 29 9.39 Netherlands 7 2.27 Construction 7 2.26

Mexico 3 0.97 New Zealand 2 0.65 Manufacturing 157 50.79

Netherlands 10 3.24 Norway 3 0.97 Transportation 46 14.88

Norway 2 0.65 Papua N Guinea 1 0.32 Wholesale & Retail trade 18 5.82

Poland 1 0.32 Singapore 2 0.65 Services 39 12.61

Saudi Arabia 1 0.32 South Africa 6 1.94 Total 309 100

Singapore 2 0.65 South Korea 2 0.65

South Africa 3 0.97 Spain 2 0.65

South Korea 3 0.97 Sweden 3 0.97 Panel D. Sample distribution by industry target

Spain 4 1.29 Switzerland 7 2.27 Industry N %

Sweden 1 0.32 Thailand 3 0.97 Mining 40 12.95

Switzerland 7 2.27 United Kingdom 29 9.39 Construction 9 2.91

Thailand 3 0.97 United States 131 42.39 Manufacturing 138 44.66

United Kingdom 26 8.41 Transportation 39 12.61

United States 113 36.57 Real Estate 2 0.64

Utd Arab Em 2 0.65 Wholesale & Retail trade 25 8.09

Services 56 18.10

Total 309 100 309 100 Total 309 100

18

3.5. Abnormal stock returns

To isolate the effects of M&A announcements on the acquirers’ abnormal returns, a

standard event study methodology is applied (Fama et al., 1969; Brown and Warner, 1985). An

event study measures the impact of the different M&A announcements on the value of firms.

Assuming market efficiency, the effects of the announcements will be reflected in stock prices.

In the first step, a statistical market model is constructed to calculate normal returns, thereby

relating expected returns to the market portfolio when deal events are absent. The market model

for any security 𝑖 is defined as follows:

𝑅𝑖,𝑡 = 𝛼𝑖 + 𝛽𝑖𝑅𝑚,𝑡 + 𝜀𝑖,𝑡, (1)

where 𝑅𝑖,𝑡 is the expected daily return of stock 𝑖 on event day 𝑡, 𝑅𝑚,𝑡 is the return on the MSCI

World Index on event day 𝑡, 𝛼𝑖 and 𝛽𝑖are the OLS regression intercept and slope12, and 𝜀𝑖,𝑡 is

the zero-mean error term. In line with MacKinlay (1997), a broad stock index (MSCI World)

is used to proxy for the market portfolio. For each event the model parameters (𝛼𝑖 and 𝛽𝑖) are

estimated over the 250 trading days ending 10 days prior the announcement date, following

Aktas et al. (2011) and MacKinlay (1997). A gap is left between the event window and the

estimation period to prevent the anticipation of the announcement from having an effect on the

normal return measure.

In the second step, abnormal returns are calculated to assess the impact of the

announcement. The abnormal returns, 𝐴𝑅𝑖,𝑡, of stock 𝑖 on event day 𝑡 are calculated by taking

the difference between actual returns and the normal returns and is expressed as follows:

𝐴𝑅𝑖,𝑡 = 𝑅𝑖,𝑡 − (𝛼𝑖 + 𝛽𝑖𝑅𝑚,𝑡), (2)

To draw overall conclusions and capture the price effects of announcements, the daily

abnormal return of each firm is accumulated over the period from the event window to obtain

the cumulative abnormal return (𝐶𝐴𝑅𝑡) from day 𝑡. An eleven-day event window (-5, 5) is used,

which is in line with prior CSR related event studies (Deng et al., 2013).13 In addition, an

eleven-day event window is better in a worldwide sample where holidays and different time

12 Nonsynchronous trading effects, which possibly occur by taking ‘closing’ prices with different time intervals

induce biases in the moments of returns and thus into the intercepts and betas of the market model. This study,

does not use the Scholes and Williams (1997) adjusted beta and intercept to account for this problem, because

actively traded stocks are assumed in the sample. Therefore, the adjustments would be generally small and

meaningless according to MacKinlay (1997). 13 The three-days (-1,1) and five-days (-2,2) windows are analysed in the univariate analysis. In addition, certain

extra short-term event windows are tested as a robustness check, namely seven days (-3,3), twenty-one days (-

10,10) and thirty-one days (-15,15).

19

zones influence the absorption of information by shareholders (Campbell, Cowan, and Salotti,

2010). Thus, the 𝐶𝐴𝑅𝑡 is the sum of the included abnormal returns over the eleven-day period

and is expressed by the following:

𝐶𝐴𝑅𝑡 = ∑ 𝐴𝑅𝑡

𝑡+5

𝑡=𝑡−5

(3)

For a correct aggregation no clustering in the sample is assumed. In other words, the

abnormal returns should be independent across securities, implying the absence of any overlap

in the event windows of the different deal announcements (MacKinlay, 1997). Overlapping

event windows can cause covariances different from zero between the abnormal returns, which

influence the calculation of the variance of the CAR. As a result, the distributional results are

no longer applicable (Bernard, 1987; MacKinlay, 1997). The sample in this study has some

overlapping event windows, but since the deals are taken from a worldwide sample, no

clustering is assumed.14

3.6. Correlation matrix

Appendix C presents the Pearson correlation matrix of all variables used in the subsequent

analyses. The environmental and social score are highly correlated with each other and the

combined score (CSP). This is justified for the reason that both measures are used for the CSP

score. The results of the other correlations indicate that no serious near multicollinearity exists

between any two variables within the threshold level of 0.5, in compliance with Belsey et al.

(2005). The correlations indicate that the CAR(-5,5) is negatively correlated with the size of

the acquirer (SIZE) and the profitability of the target (TROA). The environmental score has a

higher positive association with CAR(-5,5) than the social score (0.130>0.035). The CSR

performance measures (ENV, SOC, CSP) are all positively correlated with the size of the

acquiring firm (ASIZE). The profitability of the acquirer (AROA) and target (TROA) are

respectively slightly negatively and positively correlated with the CSR measures. Furthermore,

the strength of institutional frameworks (IF) is positively correlated with the CSR measures.

Relative deal size (RELDS) and acquirer size (ASIZE) are highly correlated (-0.479), but within

the threshold level of 0.5.15

14 In line, Brown and Warner (1985) and Kolari and Pynnönen (2010) conclude that using market model estimates

to calculate the abnormal returns reduces the covariances to zero and can thus be ignored in our analyses. 15 To test for any remaining multicollinearity, I use the Variance Inflation Factor (VIF). Only the ENV and SOC

show VIF scores above ten, which indicate multicollinearity (O’Brien, 2007).

20

3.7. Empirical method

Ordinary least squares regression (OLS) is used to test the hypotheses. The individual

dimensions environmental and social can have different effects on acquirer returns (Galema,

Plantinga, and Scholtens, 2008). Therefore, they are included in separate regressions to capture

their individual impact. All in all, this results in the following empirical models:

𝐶𝐴𝑅𝑖 = 𝛼0 + 𝛽1𝐶𝑆𝑅 + 𝛽2𝐴𝑆𝐼𝑍𝐸 + 𝛽3𝐴𝑅𝑂𝐴 + 𝛽4𝐴𝐹𝐶𝐹 + 𝛽5𝑇𝑅𝑂𝐴 + 𝛽6𝑇𝑇𝑄 + 𝛽7𝑅𝐸𝐿𝐷𝑆 + 𝛽8𝐼𝑁𝐷𝐷𝐼𝑉 + 𝛽9𝐶𝑂𝑀𝑃 + 𝛽10𝐷𝑂𝑀 + 𝛽11𝑀𝐸𝑇𝐻𝑂𝐷 + ∑ 𝐹𝐼𝑋𝐸𝐷 𝐸𝐹𝐹𝐸𝐶𝑇𝑆 + 𝜀𝑖𝑡, (4)

𝐶𝐴𝑅𝑖 = 𝛼0 + 𝛽1|∆𝐶𝑆𝑅| + 𝛽2𝐴𝑆𝐼𝑍𝐸 + 𝛽3𝐴𝑅𝑂𝐴 + 𝛽4𝐴𝐹𝐶𝐹 + 𝛽5𝑇𝑅𝑂𝐴 + 𝛽6𝑇𝑇𝑄 + 𝛽7𝑅𝐸𝐿𝐷𝑆 + 𝛽8𝐼𝑁𝐷𝐷𝐼𝑉 + 𝛽9𝐶𝑂𝑀𝑃 + 𝛽10𝐷𝑂𝑀 + 𝛽11𝑀𝐸𝑇𝐻𝑂𝐷 + ∑ 𝐹𝐼𝑋𝐸𝐷 𝐸𝐹𝐹𝐸𝐶𝑇𝑆 + 𝜀𝑖𝑡, (5)

𝐶𝐴𝑅𝑖 = 𝛼0 + 𝛽1𝐶𝑆𝑅 + 𝛽2𝐼𝐹 + 𝛽3(𝐶𝑆𝑅 × 𝐼𝐹) + 𝛽4𝐴𝑆𝐼𝑍𝐸 + 𝛽5𝐴𝑅𝑂𝐴 + 𝛽6𝐴𝐹𝐶𝐹 + 𝛽7𝑇𝑅𝑂𝐴 + 𝛽8𝑇𝑇𝑄 + 𝛽9𝑅𝐸𝐿𝐷𝑆 + 𝛽10𝐼𝑁𝐷𝐷𝐼𝑉 + 𝛽11𝐶𝑂𝑀𝑃 + 𝛽12𝐷𝑂𝑀 + 𝛽13𝑀𝐸𝑇𝐻𝑂𝐷 + ∑ 𝐹𝐼𝑋𝐸𝐷 𝐸𝐹𝐹𝐸𝐶𝑇𝑆 + 𝜀𝑖𝑡, (6)

where 𝐶𝐴𝑅𝑖 is the cumulative abnormal return of acquiring firm i, 𝐶𝑆𝑅 is the proxy of interest

of the target firm (for 𝐸𝑁𝑉, 𝑆𝑂𝐶, 𝐶𝑆𝑃), |∆𝐶𝑆𝑅| indicates one of the CSR difference measures

of interest (representing |∆𝐶𝑆𝑃| , |∆𝐸𝑁𝑉| , |∆𝑆𝑂𝐶| ), and 𝐼𝐹 refers to the strength of the

institutional framework of the acquirer nation. All the equations contain the firm and deal-

specific controls 𝐴𝑆𝐼𝑍𝐸 , 𝐴𝑅𝑂𝐴 , 𝐴𝐹𝐶𝐹 , 𝑇𝑅𝑂𝐴 , 𝑇𝑇𝑄 , 𝑅𝐸𝐿𝐷𝑆 , 𝐼𝑁𝐷𝐷𝐼𝑉 , 𝐶𝑂𝑀𝑃 , 𝐷𝑂𝑀 ,

𝑀𝐸𝑇𝐻𝑂𝐷. The 𝐹𝐼𝑋𝐸𝐷 𝐸𝐹𝐹𝐸𝐶𝑇𝑆 includes year, country and industry dummies, and 𝜀 is the

error term. Eq. (4) models the full sample and tests hypothesis 1. Subsample A and B are made

to test Eq. (5) and hypothesis 2. Both subsamples are made based on the difference between

acquirer and target CSR scores (ATCSRD). Subsample A contains deals where the acquirer has

lower CSR scores than the target (A<T), whereas subsample B contains deals where the

acquirer has higher CSR scores than the target (A>T). Absolute CSR proxies (|∆CSR|) are

calculated by taking the absolute difference between acquirer and target CSR scores (A-T). Eq.

(5) models the absolute difference between acquirer and target CSR scores (|∆𝐶𝑆𝑅|). The

variable |∆𝐶𝑆𝑅| indicates one of the CSR difference measures of interest (representing |∆𝐶𝑆𝑃|,

|∆𝐸𝑁𝑉|, |∆𝑆𝑂𝐶|). Eq. (6) test hypothesis 3 and includes the interaction effect of 𝐼𝐹 to examine

the value implication of CSR proxies across acquirer countries with different institutional

contexts.

21

4. RESULTS

4.1. Descriptive statistics

Table 3 reports the summary statistics of the firm and deal characteristics. Several

outcomes are worth mentioning. The targets in the sample are on average slightly

underperforming with respect to CSR relative to other firms included in ASSET4. 16

Furthermore, the central tendency of the social score is higher than the environmental score. In

addition, both scores have a great standard deviation and the environmental score has a higher

positive skew, shown by a lower median. Comparing the profitability measures shows that the

acquirers have larger return on asset ratios than the targets. As for the deal-specific

characteristics, the majority of deals involved only one bidder (92%), around 42% of the deals

are fully paid by cash, 41% of the deals are cross-border, 34% diversifying, and the mean of

the relative deal size is 0.69.

16 The z-scores are normalized to a scale of 100, which implies that the mean score of all the included firms is 50.

Table 3. Summary statistics. This table shows summary statistics for the main variables used in the analyses.

The full sample of M&A deals covers 309 observations in 36 acquirer countries for the period 2004-2017 and is

obtained from the Thompson ONE SDC Database. The selection criteria are described in Section 3.4. The event-

study methodology used to calculate the CAR (-5,5) is described in Section 3.5. All variables are described in

Appendix A.

Variable Obs Mean Median Std. Dev. Min. Max.

(In)dependent variables

CAR (-5,5) 309 0.00 -0.01 0.08 -0.26 0.28

ENV 309 45.81 37.35 31.54 8.47 96.42

SOC 309 48.86 48.88 30.01 4.43 97.41

CSP 309 47.33 42.58 28.59 7.70 94.97

IF 266 -0.05 0.19 0.64 11.89 19.10

Firm characteristics

ASIZE 309 16.24 16.21 1.58 11.89 19.10

AROA 309 0.11 0.10 0.09 -0.05 0.32

AFCF 309 0.10 0.06 0.10 0.00 0.52

TROA 309 0.05 0.06 0.11 -0.84 0.42

TTQ 309 2.00 1.61 1.32 0.37 10.38

Deal characteristics

RELDS 309 0.69 0.32 1.12 0.00 9.38

INDDIV 309 0.34 0.00 0.47 0.00 1.00

COMP 309 0.08 0.00 0.27 0.00 1.00

DOM 309 0.59 1.00 0.49 0.00 1.00

METHOD 309 0.42 0.00 0.49 0.00 1.00

22

The descriptive statistics of both subsamples A (A<T) and B (A>T), taken from the CSP

differences (|∆𝐶𝑆𝑃|) are reported in Appendix D and E for the sake of brevity. Some important

differences in variables can be noticed. For example, the results of subsample A and B show a

positive mean CAR(-5,5) of 0.01 and a negative mean CAR(-5,5) of 0.01 respectively.

Furthermore, the absolute mean of the CSP difference is higher in subsample B (33.22%) than

in subsample A (17.76%), same as the spread around the mean (25.26%>16.39%). Next, around

47% of the deals in subsample B are paid in cash, while this percentage is relatively smaller in

subsample A, namely 26%. Another important point to distinguish is the higher relative deal

size in subsample A (1.29) in comparison with subsample B (0.49).

4.2. Univariate analyses

The parametric results in conjunction with the nonparametric results show whether the

announcement of deals have statistical impact on the distribution of abnormal returns. Panel A

in Table 4 reports the mean and median CARs for the full sample and subsample A and B of

the CSP differences (|∆𝐶𝑆𝑃|) among acquirers and targets. The mean CAR(-1,1), CAR(-2,2),

and CAR(-5,5) for the full sample are negative, where the mean CAR(-1,1) of -0.5% is

statistically significant. This is consistent with prior studies, where the CARs are on average

slightly negative or at best zero, although often insignificant (Fuller et al., 2002; Andrade,

Mitchell, and Stafford, 2001). More in line with this research, Deng et al. (2013) find a negative

mean CAR(-5,5) of -0.445, which is significantly different from zero at the 5% level. Aktas et

al. (2011) report a lower statistically negative mean of -1.16% with a three-day abnormal return.

The results of subsample A and B show that the negative returns are mainly driven by

subsample B, which includes deals whereby acquirers have higher CSP scores than targets.

More specifically, the mean CAR(-1,1), CAR(-2,2), and CAR(-5,5) in subsample B are

significant and negative. On the contrary, the mean CARs of subsample A are higher and even

positive for the five- and eleven-day window, although not significant. The results of the median

CARs for the full- and subsamples are akin. The univariate method of analysis shows that the

difference in means of the acquirer abnormal returns for the subsamples A and B are significant

for CAR(-5,5). A possible alternative reason for these results is given by Moeller et al. (2004).

They find that negative CARs are associated with larger acquirers and public targets. Appendix

D and E, however, shows us that the mean and median market value of the acquirers in

subsample A and B are almost alike.

23

Panel B and C present four additional subsamples next to subsample A and B, based on

the sample medians of target CSP and acquirer CSP respectively, following Deng et al. (2013).

Subsequently, low and high CSP performing targets and low and high CSP performing

acquirers are distinguished. The results of CSP scores of targets in Panel B show that the

differences between the means are significant for CAR(-1,1) and CAR(-2,2). Moreover, all the

acquirer CARs are statistically negative related to low CSP targets. In contrast, the high CSP

targets generate higher mean CARs than the low CSP targets. This is partly in alignment with

the results of Aktas et al. (2011). They find an insignificant positive relation between high CSP

targets and acquirer CAR(-1,1), and a statistically significant negative relation between low

CSP targets and CAR (-1,1). Next to this, they find a statistically significant difference between

the means of low and high CSP targets at the 5% level. Panel C displays the differences between

Table 4. Acquirers’ Cumulative Abnormal Returns (CARs) and CSP ratings. This table reports the mean

and median CARs (in percentages) of acquirers during the three-day (CAR(-1,1)), five-day (CAR(-2,2)), and

eleven-day (CAR(-5,5)) windows. The event-study methodology used to calculate the CARs is described in

Section 3.5. The table the full sample, subsample A and B, and four additional subsamples. Panel A shows the

mean and median of the full sample and both subsample A(A<T) and B(A>T), based on the |∆CSP|. Panel B

presents the mean and median of the full sample and two additional subsamples based on the sample median

of target CSP. Panel C reports the mean and median for the full sample and two additional subsamples based

on the sample median of acquirer CSP. The full sample consists of 309 completed deals over the 2004-2017

period, and is extracted from Thompson ONE SDC. The selection criteria are described in Section 3.4. Tests

of differences in means are based on a two-Sample t-Test. N denotes the number of observations. *, ** and

*** denote statistical significance at the 0.10, 0.05 and 0.01 levels (2 tailed), respectively.

Panel A: Subsample based on the difference between CSP acquirer and target (|∆CSP|)

Full Sample

(N=309)

Subsample of targets

having higher CSP-

score: A

(N=76)

Subsample of acquirers

having higher CSP-

score: B

(N=233)

Test of

Difference

(A-B)

Mean Median Mean Median Mean Median Mean

CAR (-1,1) -0.005* -0.004 -0.002 -0.002 -0.006* -0.005 -0.005

CAR (-2,2) -0.005 -0.004 0.004 -0.001 -0.008** -0.006 -0.011

CAR (-5,5) -0.004 -0.007 0.007 -0.005 -0.007* -0.010 -0.014*

Panel B: Subsample based on target CSP

Full Sample

(N=309)

High CSP targets

(N=155)

Low CSP targets

(N=154)

Test of

Difference

(High-Low)

Mean Median Mean Median Mean Median Mean

CAR (-1,1) -0.005* -0.004 0.001 -0.004 -0.012** -0.004 -0.013*

CAR (-2,2) -0.005 -0.004* 0.001 -0.004 -0.011** -0.004 -0.012*

CAR (-5,5) -0.004 -0.007 0.002 -0.007* -0.009* -0.007 -0.011

Panel C: Subsample based on acquirer CSP

Full Sample

(N=309)

High CSP acquirers

(N=155)

Low CSP acquirers

(N=154)

Test of

Difference

(High-Low)

Mean Median Mean Median Mean Median Mean

CAR (-1,1) -0.005* -0.004 -0.006* -0.004 -0.005 -0.004 -0.013*

CAR (-2,2) -0.005 -0.004 -0.005 -0.004 -0.005 -0.004 -0.012*

CAR (-5,5) -0.004 -0.007 -0.007* -0.007 -0.000 -0.007 -0.011

24

the low and high CSP acquirers in relation with CAR. The results indicate that also the

differences between the mean of the low and high CSP acquirers are significant for CAR(-1,1)

and CAR(-2,2), which is comparable with the results of Deng et al. (2013). Their results indicate

a significant difference between the mean CAR(-1,1) of low CSR performing acquirers and

high CSR performing acquirers. Nevertheless, their other event windows give no significant

differences. Comparing the findings in Panel B and C shows that the difference between mean

CARs of low and high CSP targets are greater than the differences between mean CARs of low

and high CSP acquirers. This suggests that shareholders value the CSP rating of targets more

than the CSP ratings of acquirers.

All in all, the reported results in Panel A of Table 4 show that the acquirers’ abnormal

returns are significantly higher in subsample A in comparison with subsample B, which

indicates that acquirers announcing a deal with a target with relatively higher CSP scores are

valued more by the market than acquirers announcing deals with a target with relatively lower

CSP scores. These univariate findings support hypothesis 2. In addition, the difference between

mean CARs of low and high CSP targets are greater than the differences between mean CARs

of low and high CSP acquirers, suggesting that shareholders value the CSP rating of targets

more than the CSP rating of acquirers. Finally, the findings in Panel B of Table 4 support

hypothesis 1, stating that higher CSP of the target has a positive effect on the acquirers’

shareholder wealth.

4.3. Regression analyses

The univariate findings do not control for important firm and deal factors that possibly

affect the abnormal returns of the acquiring firms. Therefore, several OLS regression analyses

are carried to investigate whether the influence of target CSR performance and the difference

between CSR performance between acquirer and target remains after including controls.17 In

each model, the acquirers’ abnormal return with the eleven-day window is the dependent

variable. All the main models include year, industry, and country fixed effects to filter away

macroeconomic shocks and differences.18 Statistical significance is based on robust standard

errors. Only the interaction effect with institutional framework strength is tested without

country-fixed effects to capture the cross-country interaction impact. Table 5 represents the

17 Additionally, I run tests to check for non-linearity in the full and subsamples (Barnett and Salomon, 2006).

However, no statistical evidence is found for a non-linear relation. 18 Country dummies are taken by controlling for the U.S. and U.K. as acquirer nations, following Aktas et al.

(2011). Industries dummies are based on the first-digit SIC codes of the acquirers.

25

regression of the full sample with the independent variables ENV, SOC and CSP represented in

models 1, 2, and 3 respectively. The interaction effects of the CSR proxies with IF are presented

in models 4, 5, and 6.

Table 5. Full sample regressions of Acquirers’ Cumulative Abnormal Returns (CARs) and Targets’ CSR

ratings. This table reports the OLS regression results of the full sample with Acquirers’ CAR(-5,5) as dependent

variable and the Targets’ CSR proxies (ENV, SOC, CSP) as the main independent variables. The event-study

methodology used to calculate CAR(-5,5) is described in Section 3.5. The full sample contains 309 observations

from 36 to 34 unique countries over the 2004-2017 period. The sample selection is described in Section 3.4.

The models (1), (2), and (3) include the full sample and regress ENV, SOC, and CSP respectively. The models

(4), (5), and (6) include the interaction with IF and consist of 266 observations. Models (1)-(3) include year,

country, and industry fixed effects. Models (4)-(6) exclude the country fixed effects to capture the cross-country

interaction impact. The t-statistics based on robust standard errors are in parentheses. Appendix A presents

definitions and data sources of all used variables. *, ** and *** denote statistical significance at the 0.10, 0.05

and 0.01 levels (2 tailed), respectively.

Independent variables (1) (2) (3) (4) (5) (6)

CSR proxy

ENV 0.000***

(2.69)

0.000**

(2.31)

SOC 0.000

(.23)

-0.000

(0.03)

CSP 0.000

(1.58)

0.000

(1.35)

Firm characteristics:

ASIZE -0.008**

(-2.04)

-0.005

(-1.35)

-0.006

(-1.66)

-0.007*

(-1.83)

-0.006

(-1.46)

-0.007

(-1.63)

AROA 0.085

(1.42)

0.067

(1.12)

0.076

(1.27)

0.119*

(1.69)

0.112

(1.57)

0.115

(1.62)

AFCF 0.017

(0.31)

0.031

(0.54)

0.026

(0.45)

-0.028

(-0.51)

-0.024

(-0.42)

-0.024

(-0.42)

TROA -0.110*

(-1.95)

-0.095

(-1.63)

-0.102

(-1.77)

-0.135**

(-2.14)

-0.128*

(-1.89)

-0.132**

(-2.02)

TTQ 0.007

(1.50)

0.004

(0.94)

0.006

(1.23)

0.007

(1.45)

0.005

(0.99)

0.006

(1.22)

Deal characteristics:

RELDS -0.004

(-0.76)

-0.002

(-0.36)

-0.003

(-0.54)

-0.004

(-0.67)

-0.003

(-0.61)

-0.003

(-.62)

INDDIV -0.007

(-0.80)

-0.006

(-0.67)

-0.007

(-0.80)

-0.014

(-1.39)

-0.015

(-1.45)

-0.015

(-1.51)

COMP 0.009

(0.45)

0.009

(0.48)

0.010

(0.48)

0.006

(0.27)

0.004

(0.19)

0.005

(0.21)

DOM -0.017*

(-1.88)

-0.015

(-1.58)

-0.015

(-1.60)

-0.008

(-0.84)

-0.010

(-0.99)

-0.008

(-0.79)

METHOD -0.010

(-1.03)

-0.012

(-1.22)

-0.011

(-1.07)

-0.011

(-1.03)

-0.012

(-1.17)

-0.011

(-1.06)

Strength of institutions

IF -0.011

(-0.85)

0.004

(0.29)

-0.005

(-0.37)

IF*CSR score 0.000

(0.82)

-0.000

(-0.58)

-0.000

(0.23)

Constant 0.188***

(3.06)

0.201***

(3.29)

0.199***

(3.23)

0.177***

(2.67)

0.200***

(3.01)

0.192***

(2.87)

Year fixed effects Yes Yes Yes Yes Yes Yes

Industry fixed effects Yes Yes Yes Yes Yes Yes

Country fixed effects Yes Yes Yes No No No

Adjusted R2 0.109 0.080 0.090 0.107 0.080 0.088

Observations 309 309 309 266 266 266

26

In model 1, I find a positive and statistically significant impact of the environmental score

(ENV). The positive coefficient is significant at the 1% level. This finding suggests a positive,

although small, relation between environmental performance of the target and acquirer

abnormal returns. The coefficient remains significant after including the strength of institutional

frameworks (𝐼𝐹) in model 4. This finding suggests that the environmental performance of

targets is valued positively by shareholders in the acquiring firms. The economic significance

of environmental corporate investments can be quite substantial. Note, an increase of 1 point

(scaled from zero to 100) in the targets environmental rating results in an increase in acquirer

abnormal returns by 0.049%. In contrast with model 1, the findings in models 2 and 3 indicate

no statistical evidence for a positive influence of the social (SOC) and total CSR score (CSP)

on the acquirer abnormal returns. This is in contrast with the findings of Aktas et al. (2011),

who find a significant positive influence of the environmental, social, and combined IVA score.

However, also their results indicate a stronger influence of the environmental score in

comparison with the social. Thus, the social performance and CSP of targets is not valued by

shareholders in this sample. Therefore, it can be concluded that hypothesis 1 is supported for

the environmental performance of the target, but not supported for the CSP and social

performance of the target. Indicating that shareholders value environmental investments. This

finding is in alignment with prior empirical evidence (see, e.g., Hall and Rieck, 1998; Cheng et

al., 2014).

Concerning the signs of the control variables in model 1, acquirer size (ASIZE) and

profitability (AROA) are in line with the literature (Rau and Vermaelen, 1998; Easton and Harris,

1991). Target profitability (TROA) and Tobin’s q (TTQ) coefficients are consistent with prior

studies as well (Shawyer, 2002; Lang et al., 1989). The coefficients of domestic (DOM) and

diversifying (INDDIV) deals are negative as expected, which agrees with the findings of Eckbo

(1983) and Doukas et al. (2002) respectively. The relative deal size (RELDS) works as a scaling

mechanism for the average deals being value decreasing for acquirers. The ASIZE coefficient

is significantly negative at the 5% level, while TROA and DOM are significantly negative at the

10% level. The signs of acquirer free cash flow (AFCF) contrasts the free cash flow hypothesis

of Jensen (1986). The coefficients of competitive (COMP) and cash paid (METHOD) deals are

respectively positive and negative. This is not in accordance with the expectation and prior

literature (Bradley et al., 1988; Travlos, 1987). In model 2 and 3, I find no significant control

variables. Next, the adjusted R-squared is smaller in model 2, which indicates that the social

pillar explains less of the variation in abnormal returns in comparison with the environmental

pillar.

27

Models 4, 5, and 6 represent the interaction effect of 𝐼𝐹 with ENV, SOC, and CSP

respectively. The findings indicate that the institutional framework variable and the interaction

effects with the CSR proxies are insignificant in all the models.19 The signs of the interaction

effect are consistent with the expectation in the SOC and CSP model, although not in line with

the expectation in the ENV model. Thus, no reasonable statistical evidence is found to state that

the relation between CSR and abnormal returns is weaker (stronger) in countries with stronger

(weaker) institutional frameworks. Therefore, hypothesis 3 is not confirmed for the

environmental, social, and CSP performance.

Table 6 shows the regression outcomes of the subsamples A(A<T) and B(A>T) with the

explanatory variables |∆𝐸𝑁𝑉|, |∆𝑆𝑂𝐶|, |∆𝐶𝑆𝑃|. For clarity, the subsamples A and B indicate

the absolute value of the difference in CSR proxies between acquirer and target (A-T). Three

main findings emerge. First, the coefficient of |∆𝐸𝑁𝑉| is positive and significant at the 5%

level in subsample A. In contrast, the coefficient of |∆𝐸𝑁𝑉| is negative and significant at the

5% level in subsample B. Furthermore, both subsample A and B |∆𝐸𝑁𝑉| coefficients are higher

than the full sample ENV outcomes, namely 0.002 and -0.004 respectively. Hence, a greater

difference between acquirer and target environmental performance seem to matter for the

effects on acquirer abnormal returns. All in all, shareholders appear to value (disvalue)

acquirers taking over targets with a relatively higher (lower) environmental performance, which

is in line with hypothesis 2. To conclude, the regression results presented in Tables 5 and 6

partly confirm the univariate results reported in Table 4. The significant interaction effect of

the ENV and SOC difference with 𝐼𝐹 is presented in Appendix F. The interaction coefficient is

slightly positive (negative) and significant at the 10% level in the ENV (SOC) model. The

significant positive influence of ENV holds after including the interaction effect. This indicates

that larger environmental differences between acquirer and target are valuated more positively

in strong institutional frameworks if the acquirer scores lower compared to the target in

environmental performance. This is contradicting hypothesis 3. All the interaction effects are

insignificant in subsample B.

19 Additional regressions on the separate scores of CAPMD (representing SMD and CMD) and BR (representing

LSPR and BF) are run. Also these tests give no significant results.

28

Table 6. Subsample regressions of Acquirers’ Cumulative Abnormal Returns (CARs) and Targets’ CSR

ratings. This table reports the OLS regression results of the subsamples A(A<T) and B(A>T) with Acquirers’

CAR(-5,5) as dependent variable and the ATCSRD proxies (|∆ENV|, |∆SOC|, |∆CSP|) as the main independent

variables. The event-study methodology used to calculate CAR(-5,5) is described in Section 3.5. Subsample A

contains the effects of |∆ENV|, |∆SOC|, and |∆CSP| over respectively 86, 90, and 76 observations. Subsample

B contains the effects of |∆ENV|, |∆SOC|, and |∆CSP| over respectively 220, 219, and 233 observations. The

subsample selection is described in Section 3.6. All models (7)-(12) include year, country, and industry fixed

effects. The t-statistics based on robust standard errors are in parentheses. Appendix A presents definitions and

data sources of all used variables. *, ** and *** denote statistical significance at the 0.10, 0.05 and 0.01 levels

(2 tailed), respectively.

Subsample A Subsample B

Independent variables (7) (8) (9) (10) (11) (12)

ATCSRD proxy

|∆ENV| 0.002**

(2.21)

-0.004**

(-1.98)

|∆SOC| -0.000

(-0.31)

-0.000

(-0.04)

|∆CSP| -0.000

(-0.09)

-0.000

(-0.81)

Firm characteristics

ASIZE -0.011

(-1.36)

-0.004

(-0.42)

-0.009

(-0.88)

0.001

(0.10)

-0.006

(-1.11)

-0.001

(-0.22)

AROA 0.102

(0.67)

0.285

(1.81)

0.175

(0.88)

0.067

(0.97)

-0.029

(-0.38)

-0.013

(-0.19)

AFCF 0.148

(1.22)

0.012

(0.08)

0.228

(1.49)

0.012

(0.19)

0.047

(0.78)

0.033

(0.56)

TROA -0.185

(-1.45)

0.191

(1.07)

0.002

(0.01)

-0.109*

(-1.68)

-0.120**

(-2.07)

-0.112*

(-1.84)

TTQ 0.014

(1.35)

-0.011

(-0.89)

0.003

(0.18)

0.004

(0.81)

0.005

(1.12)

0.004

(0.93)

Deal characteristics

RELDS -0.011

(-1.27)

0.003

(0.39)

-0.001

(-0.14)

-0.001

(-0.07)

-0.004

(-0.33)

0.004

(0.36)

INDDIV -0.018

(-0.68)

-0.035

(-1.12)

-0.020

(-0.65)

0.000

(0.03)

0.009

(0.83)

0.005

(0.45)

COMP -0.088**

(-2.30)

-0.004

(-0.08)

-0.030

(-0.59)

0.030

(1.47)

0.014

(0.65)

0.023

(1.12)

DOM -0.026

(-1.12)

-0.026

(-1.07)

-0.042*

(-1.74)

-0.017*

(-1.73)

-0.019*

(-1.78)

-0.016

(-1.62)

METHOD -0.022

(-1.08)

-0.016

(-0.62)

-0.021

(-0.72)

-0.014

(-1.26)

-0.019

(-1.63)

-0.013

(-1.16)

Constant 0.150

(1.17)

0.034

(0.27)

0.083

(0.63)

0.104

(1.08)

0.230

(2.81)***

0.162

(2.10)**

Year fixed effects Yes Yes Yes Yes Yes Yes

Industry fixed effects Yes Yes Yes Yes Yes Yes

Country fixed effects Yes Yes Yes Yes Yes Yes

Adjusted R2 0.210 0.096 0.042 0.075 0.061 0.084

Observations 86 90 76 220 219 233

4.4. Portfolio results

The following analysis includes portfolios of events sorted by their CARs. Events are

assigned in equally weighted quartile portfolios, thereby having events with the lowest CARs

in Q1 and events with the highest CARs in Q4 respectively. Table 7 presents portfolio results

for the researched relations. An examination of the results for the full sample (Panel A) reveals

29

that the target CSP considerably increases for portfolios Q1-Q3. Moreover, the score only

marginally decreases in the highest CARs (Q4), exhibiting an inverse U-shaped relation. The

portfolios of Panel A provide evidence for the increasing relation between target CSP and CARs,

suggesting that shareholders significantly price higher target CSP values, from an economic

point of view.

Table 7. Portfolio results of Acquirers’ Cumulative Abnormal Returns (CARs) and Acquirers’ and

Targets’ CSR ratings. This table reports the portfolio results of the full sample and both subsamples.

Observations are assigned into equally weighted quartile portfolios according to Acquirers’ CAR(-5,5). The

events with the highest CARs are exhibited in Q4 and the lowest CARs in Q1. The event-study methodology

used to calculate CAR(-5,5) is described in Section 3.5. The subsample selection is described in Section 3.6.

Panel A shows the portfolio results of the full sample. Panel B reports the results of subsample A(A<T) and

Panel C of subsample B(A>T). The subsample selection is described in Section 3.6. Appendix A presents

definitions and data sources of all used variables. The t-statistics based are in parentheses. N denotes the number

of observations. *, ** and *** denote statistical significance at the 0.10, 0.05 and 0.01 levels (2 tailed),

respectively.

Panel A: Full sample

Quartiles

1 2 3 4

ENV_A 61.43

(3.44)

75.10

(3.23)

71.60

(3.37)

63.45

(3.59)

SOC_A 62.37

(3.58)

74.51

(3.16)

72.70

(2.80)

63.09

(3.36)

CSP_A 61.90

(3.39)

74.81

(3.09)

72.15

(3.00)

63.27

(3.38)

ENV_T 39.06

(3.19)

42.57

(3.63)

51.18

(3.76)

50.49

(3.63)

SOC_T 45.87

(3.49)

48.86

(3.24)

50.59

(3.54)

50.17

(3.42)

CSP_T 42.46

(3.11)

45.72

(3.19)

50.89

(3.38)

50.33

(3.30)

N 78 77 77 77

Panel B: Subsample A (A<T)

Quartiles

1 2 3 4

|∆ENV| 10.72

(3.86)

10.71

(4.69)

12.90

(3.64)

28.44

(5.21)

|∆SOC| 19.05

(4.32)

19.03

(3.59)

15.74

(5.59)

20.24

(3.76)

|∆CSP| 14.88

(3.62)

14.87

(2.59)

14.32

(3.96)

24.34

(3.84)

N 22 16 14 24

Panel C: Subsample B (A>T)

Quartiles

1 2 3 4

|∆ENV| 35.37

(3.83)

43.88

(3.72)

27.82

(3.67)

31.71

(3.78)

|∆SOC| 30.47

(3.86)

37.37

(3.59)

30.52

(3.49)

27.93

(3.68)

|∆CSP| 32.92

(3.36)

40.62

(3.30)

29.17

(3.08)

29.82

(3.37)

N 56 61 63 53

30

With reference to Panel B, which includes the absolute differences between acquirer and

target CSR proxies, it can be observed that the difference in ENV rises considerably, with values

highest for highest CARs (Q4). However, this positive correlation is not found for SOC,

consistent with the regression results. Moreover, the difference in CSP is roughly the same for

Q1-3, though it shows a sizeable increase for the highest CARs (Q4). Hence, this result indicates

that CAR is economically large for deals including firms with high CSP values.

Portfolio analysis of panel C exhibits a hump-shaped and decreasing relation between

differences in CSP values and CARs. The largest differences in CSP values are found in Q2

and Q3 respectively. This suggests that the lowest (Q1) and highest (Q4) CARs have the

smallest differences between acquirer’s CSP and targets’ CSP value. Thus, when the CSP of

the acquirer is larger than the CSP value of the target (panel C), the M&As with the smallest

differences earn the lowest and highest CARs. Consequently, the portfolio analysis provides

economically significant evidence for a positive relation between CSP values and CAR.

4.4. Robustness tests

Several tests are employed on the main models to check whether the results are robust. The

choice of event window could possibly influence the obtained results. Therefore, the ENV

regressions are run with the three-days (-1,1), five-days (-2,2), seven-days (-3,3), eleven-days

(-5,5), twenty-one-days (-10,10) and thirty-one-days (-15,15). The results of the full sample and

subsample A are presented in Appendix G and H and are qualitatively similar among all the

event windows. This indicates that the significant small positive influence of the targets

environmental performance on acquirers’ abnormal returns is robust among all the event

windows in the full model and subsample A. Additionally, the results of subsample B are

presented in Appendix H and are only significant for the twenty-one days event window. Thus,

the significant, negative effect of ENV on acquirers’ abnormal returns is not robust when

acquirers score higher compared to targets in relation to environmental performance. Also, the

less sophisticated mean adjusted model give similar results for all the event windows, but are

not reported for the sake of brevity. This is in alignment with Brown and Warner (1985), who

confirm the robustness of the short-term event study method to the choice of event windows

and choice of modelling the normal returns. Dyckman, Philbrick, and Stephan (1984) also show

that the market model performs significantly better than the simpler mean adjusted model. The

robustness of using local indices versus global broader indices in an event study is demonstrated

by Campbell, Cowan, and Salotti (2010). Thus, I assume that the findings in this study are

31

robust in relation with the MSCI World index used to determine the market model normal

returns.

To check if the results are influenced by U.S. acquirers, I re-run all the regressions on a

non-U.S. sample. The results of the full sample and both subsamples are presented in Appendix

I and Appendix J respectively. The significant small positive (negative) influence remains in

the full sample (subsample B). However, the environmental differences in subsample A is found

insignificant. Thus, the results in the full models and subsample B models are robust, however,

the results in subsample A are not.

As indicated by Hong and Kacperczyk (2009) and El Ghoul et al. (2011), SIN stocks have

higher risk and therefore returns compared to conventional stocks. Hence, the regressions are

re-examined with controlling dummies for SIN firms. SIN industries are based on the Fama and

French (1997) industry classifications. I consider coal, petroleum, biotech, alcohol, tobacco,

defense, cement, and gambling as SIN industries. The related SIC codes used to classify the

SIN industries are described in Appendix A. Appendix K and L summarizes the results of the

full model and subsamples respectively. The results of the full sample look quite similar to the

results of the normal regression. Including the interaction effect of SIN industries lead to a

higher environmental coefficient compared to the normal model (0.001>0.000). Additionally,

the CSP becomes positive significant at the 10% level. The results of the subsamples are not

robust after controlling for SIN industries. The interaction with the environmental differences

in subsample A is significant positive at the 5% level, indicating a cross-over interaction.

5. CONCLUDING REMARKS

Motivated by the lack of consensus on the effects of CSR, this study examines the effect

of CSR on CFP in the context of M&As. These unanticipated events are a useful manner to

identify the causal relation between these widely researched concepts. This paper seeks to

determine whether acquirer announcement returns are positively affected by the CSR

performance of the target firm and the acquirer-target CSR difference (ATCSRD), thereby

considering cross-country differences in institutional contexts. Based on the stakeholder view,

RBV, and contract theory, this paper argues that the CSR performance of targets can be of

considerable importance for the acquirer to obtain a sustainable competitive advantage.

Furthermore, acquiring a higher CSR target can enhance the acquirers’ access to capital and

can give a positive signal to its stakeholders. Next, this study suggests that a higher ATCSRD

Leads to positive reputational effects and a higher learning potential for the acquirer. Finally,

this study states that the CSR performance of firms can substitute market-supporting institutions

32

and hence might overcome market failures resulting from weak institutional frameworks. As a

consequence, CSR investments are considered more valuable in weak institutional frameworks

compared to strong institutional contexts.

In light of these arguments, this paper finds the following empirical results. A substantial

positive influence of the environmental performance of the target on acquirer’ abnormal returns

is found in all main and subsample models after controlling for acquirer, target, deal

characteristics and year, industry, and country fixed effects. This suggests that environmental

investments are valued positively by investors. The results are robust to a variety of alternative

model specifications. Interestingly, no evidence is found for a positive valuation regarding

social and CSP investments. In addition, ATCSRD is researched by using two subsamples,

which distinguish between relatively higher CSR performing targets (A) and relatively lower

CSR performing targets (B) in comparison with acquiring firms. The findings indicate that a

greater difference between acquirer and target environmental performance is valued positively

(negatively) by shareholders in subsample A (subsample B). Therefore, I find support for the

stakeholder view on the short-term. Moreover, no evidence is found for a positive interaction

effect between CSR performance and weak institutional frameworks. All in all, the results

suggest that integrating stakeholders’ interests, by taking over high(er) CSR targets, in their

corporate investment decisions enhance short-term abnormal returns. These findings support

the stakeholder view, describing that taking into account all stakeholder interests can be

combined with shareholder wealth creation.

For practitioners, the findings in this study can increase managers’ confidence in investing

in CSR activities, especially in environmental activities. These investments not only contribute

to society at large, but also brings value to shareholders of acquiring firms. Also, potential target

firms should consider increasing their environmental activities to be more valuable for acquirers.

Thus, acquirers who integrate stakeholders’ interests by investing in better environmental

performing targets are valued in the short-term by their shareholders.

This research acknowledges several limitations. The quality of CSR measures is a concern

in academic literature (see, e.g., Chatterji et al., 2009; Orlitzky et al., 2003). Likewise, the used

ES data from the ASSET4 database in this study is subject to some limitations as well. First,

the reliability of ES data is not yet confirmed by prior studies. This is mainly due, the ES data

points, which are the inputs for the calculation of the KPIs, are collected by around 100 trained

research analysts and therefore subject to subjectivity. In addition, the underlying values of the

overall environmental and social pillar scores are not available and accordingly no transparency

is given about their specific method of assessment (Thomson Reuters, 2013). Therefore, it is

33

difficult to research the different pillars in-depth. Taking a closer look at the underlying scores

shows that the overall environmental and social pillar scores for most of the firms are composed

of only a few data points that are mainly process-based, not outcome-based. Second, the use of

frameworks to capture the complex and dynamic CSR concept can lead to the loss of important

information. Another limitation of the ASSET4 database is the relatively high representation of

global and large firms, since large firms have more ES availability. This leads to a selection of

relatively large firms in the sample though this paper controls for firm size in the regressions.

Finally, the compatibility with other CSR measures, such as the KLD rating and IVA rating, is

difficult due to the different methodologies used. However, Semenova and Hassel (2015) find

that the environmental strengths of KLD and the environmental performance metrics of

ASSET4 highly correlate.

The results of this study disclose several directions for future research. First, future

examinations can use all CSR measures to investigate whether results deviate for various

measures of CSR. Furthermore, the results of this study suggest shareholder wealth

enhancement as a result of environmental investments on the short-term. However, new

research is needed to investigate this effect in the long-term. Third, this paper focusses only on

shareholder wealth, future studies can research the value implications of other major

stakeholders, such as suppliers, employees, bondholders, and consumers. Additionally, the role

of shareholder activism in CSR investments is interesting to consider. Activist shareholder

pressure could potentially influence CSR investments. Lastly, this study controls for SIN

industries interaction as a robustness check. A further exploration of the role of SIN industries

was beyond the scope of this paper, but is an interesting direction for future research.

34

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40

APPENDICES

Appendix A. Variable description. This table reports the variable descriptions of all used variables in the analyses. All the

independent variables are taken prior to the deal announcement year. The values of the acquirer-specific and target-specific control

variables are taken as of the year-end prior the deal announcement. The macro-economic factors of the moderating variables are

taken for the deal announcement year. Full data sources are Datastream (DS), Fraser Institute’s Economic Freedom of the World

(EFW), World Development Indicator (WCY), and Securities Data Company (SDC).

Variable name Variable description Source

Dependent variables

CAR (-5,5)

Eleven-day cumulative abnormal returns (in percentages) calculated by using market

model parameters estimated over the period (-250, -10) with the MSCI World index as

the market index. Price data type P#T is used which is adjusted for other capital events,

such as stock splits and add no repeating data after the stock close down.

DS

CAR (-1,1) Three-day cumulative abnormal returns (in percentages) calculated the same as

CAR (-5,5)

DS

CAR (-2,2) Five-day cumulative abnormal returns (in percentages) calculated the same as

CAR (-5,5)

DS

CAR (-3,3) Seven-day cumulative abnormal returns (in percentages) calculated the same as

CAR (-5,5)

DS

CAR (-10,10) Twenty-one-day cumulative abnormal returns (in percentages) calculated the same as

CAR (-5,5) with an estimation period of (-250, -20).

DS

CAR (-15,15) Thirty-one-day cumulative abnormal returns (in percentages) calculated the same as CAR

(-5,5) with an estimation period of (-250, -20).

DS

Independent variables

ENV

The environmental performance (in percentages) measures a firm's influence on non-

living and living natural systems, including water, soil, air and complete ecosystems. A

higher value relates to relatively more (perceived) environmental efforts by the firm.

ASSET4

SOC

The social performance (in percentages) measures a firm’s capacity to generate loyalty

and trust with its employees, customers, and society, through its use of best management

practices. A higher value relates to relatively more (perceived) social efforts by the firm.

ASSET4

CSP

The overall CSR performance (in percentages) is calculated by taken an equal weighted

average of the environmental (ENV) and social (SOC) performance. A higher value

relates to relatively more (perceived) CSR efforts by the firm.

ASSET4

|∆ENV| The absolute difference between the acquirer and target environmental (ENV) scores. ASSET4

|∆SOC| The absolute difference between the acquirer and target social (SOC) scores. ASSET4

|∆CSP| The absolute difference between the acquirer and target CSP scores. ASSET4

Moderating variables

IF Equally weighted average of the normalized scores of SMD, CMD, BF, and LSPR in the

acquirer country.

EFW&WCY

SMD

Equally weighted average of the normalized scores of stock market capitalization over

GDP, total value of shares traded over GDP and total value of domestic shares traded

over market capitalization. All related to the acquirer country.

WCY

CMD Total volume of domestic credits provided by the financial sector divided by GDP in the

acquirer country.

WCY

CAPMD Total strength of capital market calculated by taking equally weighted average of SMD

and CMD.

WCY

BF

Index for the quality of business freedom in the acquirer country. A higher score means

fewer regulations and thus more business freedom. The index contains the following

subcomponents: Administrative requirements, bureaucracy costs, starting a business,

extra payments/bribes/favoritism, licensing restrictions, cost of tax compliance.

EFW

LSPR

Index for the quality of the legal system and the security of property rights in the acquirer

country. The nine subcomponents indicate how effective the protective functions are

performed by the government. A higher score implies a higher quality of legal systems

and property rights. The index exists of the following subcomponents: Judicial

independence, impartial courts, protection of property rights, military interference in rule

of law, integrity of the legal system, legal enforcement of contracts, regulatory costs of

the sale of real property, reliability of police, business costs of crime.

EFW

BR Total quality of business regulations calculated by taking equally weighed average of BF

and LSPR.

EFW

41

SIN

Dummy variable equals one if the acquirer is from an SIN industry, equals zero

otherwise. Industry classifications are based on the 48 SIC industry classifications of

Fama and French (1997). In this study, SIN industries include coal (1200-1299),

petroleum (1300, 1310-1339, 1370-1382, 1389, 2900-2912, 2990-2999.), biotech (2833-

2836), alcohol (2080-2085), tobacco (2100-2199), defense (3760-3769, 3795, 3480-

3489), cement (3240-3241), and gambling (7980-7999).

SDC

Control variables

ASIZE

Natural logarithm of acquirer's market value in millions of US$ calculated by multiplying

the share price with the number of ordinary shares in issue. The natural logarithm is taken

to pull in extreme observations of this continuous variable with no natural boundary.

DS

AROA Acquirer measure of profitability (ROA) calculated by taking the net income before

extraordinary items over the average of last and current year's total assets.

DS

AFCF Acquirer free cash flow measured by free cash flow over total assets. DS

TROA Target measure of profitability (ROA) calculated by taking the net income before

extraordinary items over the average of last and current year's total assets.

DS

TTQ Target Tobin’s q measured by market value of assets (market capitalization plus total

liabilities) divided by book value of assets (total liabilities plus common stock).

DS

RELDS Ratio of the deal transaction (excluding fees and expenses) reported in SDC to acquirer's

market value

SDC&DS

INDDIV Dummy variable equals one if acquirer 's and the target's primary two-digit standard

industrial classification (SIC) codes are different, equals zero otherwise.

SDC

COMP Dummy variable equals one if the number of bidders is larger than one, equals zero if the

number of bidders is one.

SDC

DOM Dummy variable equals zero if the acquirer and target are from different countries, equals

one if they have the same country of origin.

SDC

METHOD Dummy variable equals one if the deal is purely financed by cash, zero otherwise. SDC

FIXED EFFECTS Year, country, and industry dummies. STATA

Appendix B. Sample distribution initial sample. This table presents the sample distribution by country, year, and industry. The initial

sample consists of 6,954 deals. Countries which are not included in the full sample are excluded from the initial sample. Therefore, the

sample contains 6,044 deals over the 2002-2017 period. The following main two-digit SIC industry classification, obtained from SDC, is

used: mining (10-14), construction (15-17), manufacturing (20-39), transportation (40-49), wholesale and retail trade (50-59), real estate (65)

(only targets), and services (70-89). The selection criteria are described in Section 3.4.

Panel A. Sample distribution by country Panel B. Sample distribution by year

Acquirer Target

Country N % Country N % Year N %

Australia 335 5.54 Australia 414 6.85 2002 397 6.57

Austria 13 0.22 Austria 13 0.22 2003 422 6.98

Bahrain 1 0.02 Bahrain 1 0.02 2004 424 7.02

Belgium 22 0.36 Belgium 20 0.33 2005 516 8.54

Brazil 53 0.88 Brazil 64 1.06 2006 512 8.47

Canada 927 15.34 Canada 1,015 16.79 2007 578 9.56

Chile 3 0.05 China 58 0.96 2008 425 7.03

China 73 1.21 France 175 2.9 2009 456 7.54

Denmark 15 0.25 Germany 96 1.59 2010 382 6.32

Finland 20 0.33 Gibraltar 1 0.02 2011 330 5.46

France 179 2.96 Greece 26 0.43 2012 326 5.39

Germany 98 1.62 Hong Kong 52 0.86 2013 277 4.58

Gibraltar 1 0.02 India 171 2.83 2014 284 4.7

Greece 18 0.3 Ireland-Rep 12 0.2 2015 332 5.49

Hong Kong 51 0.84 Italy 37 0.61 2016 275 4.55

India 150 2.48 Japan 1,116 18.46 2017 108 1.79

Ireland-Rep 23 0.38 Kuwait 1 0 Total 6,044 100

Isle of Man 2 0.03 Luxembourg 9 0.15

Israel 23 0.38 Mexico 23 0.38 Panel C. Sample distribution by industry acquirer

Italy 59 0.98 Morocco 5 0.08 Industry N %

Japan 1,208 19.99 Netherlands 43 0.71 Agriculture 23 0.39

Mexico 26 0.43 New Zealand 11 0.18 Mining 1,257 20.8

Netherlands 62 1.03 Norway 76 1.26 Construction 140 2.31

Norway 51 0.84 Papua N Guinea 2 0.03 Manufacturing 2,530 41.86

Poland 3 0.05 Singapore 59 0.98 Transportation 466 7.71

Saudi Arabia 1 0.02 South Africa 50 0.83 Wholesale & Retail trade 474 7.85

Singapore 52 0.86 South Korea 175 2.9 Services 1,152 19.05

South Africa 47 0.78 Spain 21 0.35 Public Administration 2 0.03

South Korea 161 2.66 Sweden 66 1.09 Total 6,044 100

Spain 25 0.41 Switzerland 43 0.71

Sweden 72 1.19 Thailand 40 1 Panel D. Sample distribution by industry target

Switzerland 86 1.42 United Kingdom 339 5.61 Industry N %

Thailand 36 0.6 United States 1,810 29.95 Agriculture 34 0.57

United Kingdom 363 6.01

Mining 1,237 20.46

United States 1,782 29.48

Construction 144 2.38

Utd Arab Em 3 0.05

Manufacturing 2,273 37.63

Transportation 420 6.95

Wholesale & Retail trade 482 7.97

43

Finance 137 2.27

Services 1,314 21.74

Public Administration 2 0.03

Total 6,044 100 6,044 100 Total 6,044 100

Appendix C. Correlation matrix. This table reports the Pearson’s correlations between all the variables used in the analyses. The Spearman’s rank correlation coefficients

give quantitatively similar results. The correlation coefficients above 0.5 are marked bold. The full sample contains 309 observations. The correlations with IF contains 266

observations. *, ** and *** denote statistical significance at the 0.10, 0.05 and 0.01 levels (2 tailed), respectively.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

(1) CAR (-5,5) 1.000

(2) ENV 0.130** 1.000

(3) SOC 0.035 0.726*** 1.000

(4) CSP 0.090 0.933*** 0.926*** 1.000

(5) ASIZE -0.041 0.155*** 0.129** 0.153*** 1.000

(6) AROA 0.039 -0.091 -0.052 -0.077 0.211*** 1.000

(7) TROA -0.063 0.079 0.084 0.088 0.107 0.265*** 1.000

(8) TTQ 0.061 -0.165*** -0.135 -0.162*** 0.183*** 0.111* 0.129** 1.000

(9) AFCF 0.063 -0.022 -0.065 -0.046 0.092 0.062 -0.022 0.313*** 1.000

(10) RELDS 0.057 -0.033 -0.011 -0.024 -0.479*** -0.110* 0.059 0.048 -0.054 1.000

(11) INDDIV -0.016 0.089 0.127** 0.116** 0.077 0.013 0.020 -0.011 -0.084 -0.100* 1.000

(12) COMP 0.075 -0.029 -0.037 -0.036 0.015 0.023 0.002 0.057 0.045 0.117** -0.030 1.000

(13) DOM -0.060 -0.053 -0.128** -0.097* -0.121** -0.048 0.013 -0.002 0.008 0.064 0.030 0.021 1.000

(14) METHOD -0.023 -0.003 -0.011 -0.008 0.294*** 0.158*** 0.001 0.098 0.189*** -0.294*** 0.030 -0.025 -0.213*** 1.000

(15) IF 0.007 0.091 0.048 0.075 0.065 -0.223*** -0.044 0.109* 0.181*** 0.055 0.048 0.095 0.079 -0.024 1.000

Appendix D. Summary statistics subsample A. This table shows summary statistics for the main variables

used in the analyses. Subsample A covers 76 observations for the period 2004-2017 and is obtained from the

Thompson ONE SDC Database. The subsample selection is described in Section 3.6. The event-study

methodology used to calculate the CAR (-5,5) is described in Section 3.5. All variables are described in Appendix

A.

Variable Obs Mean Median Std. Dev. Min. Max.

(In)dependent variables

CAR (-5,5) 76 0.01 0.00 0.09 -0.26 0.23

|∆CSP| 76 17.76 15.57 16.39 0.07 64.39

IF 67 -0.03 0.69 -2.35 0.70

Acquirer characteristics

ASIZE 76 15.41 15.70 1.45 11.89 18.09

AROA 76 0.11 0.10 0.09 -0.05 0.32

AFCF 76 0.06 0.06 0.08 -0.28 0.27

Target characteristics

TROA 76 1.86 1.49 1.14 0.37 7.18

TTQ 76 0.10 0.06 0.09 0.00 0.40

Deal characteristics

RELDS 76 1.29 0.63 1.76 0.01 9.38

INDDIV 76 0.32 0.00 0.47 0.00 1.00

COMP 76 0.09 0.00 0.29 0.00 1.00

DOM 76 0.67 1.00 0.47 0.00 1.00

METHOD 76 0.26 0.00 0.44 0.00 1.00

Appendix E. Summary statistics subsample B. This table shows summary statistics for the main variables used

in the analyses. Subsample B covers 233 observations for the period 2004-2017 and is obtained from the

Thompson ONE SDC Database. The subsample selection is described in Section 3.6. The event-study

methodology used to calculate the CAR (-5,5) is described in Section 3.5. All variables are described in Appendix

A.

Variable Obs Mean Median Std. Dev. Min. Max.

(In)dependent variables

CAR (-5,5) 233 -0.01 -0.01 0.08 -0.25 0.28

|∆CSP| 233 33.22 27.82 25.26 0.27 85.62

IF 199 -0.50 0.18 0.63 -2.75 0.86

Acquirer characteristics

ASIZE 233 16.51 16.52 1.53 11.89 19.10

AROA 233 0.11 0.10 0.09 -0.05 0.32

AFCF 233 0.05 0.06 0.11 -0.84 0.42

Target characteristics

TROA 233 2.05 1.67 1.37 0.42 10.38

TQ 233 0.10 0.06 0.10 0.00 0.52

Deal characteristics

RELDS 233 0.49 0.25 0.71 0.00 5.16

INDDIV 233 0.35 0.00 0.48 0.00 1.00

COMP 233 0.07 0.00 0.26 0.00 1.00

DOM 233 0.56 1.00 0.50 0.00 1.00

METHOD 233 0.47 0.00 0.50 0.00 1.00

46

Appendix F. Institutional frameworks in Subsample A and B. This table reports the OLS regression results of

the subsamples A(A<T) and B(A>T) with Acquirers’ CAR(-5,5) as dependent variable and the ATCSRD proxies

(|∆ENV|, |∆SOC|, |∆CSP|) and interaction effects with IF as the main independent variables. The event-study

methodology used to calculate CAR(-5,5) is described in Section 3.5. Subsample A contains the effects of |∆ENV|,

|∆SOC|, and |∆CSP| over respectively 79, 79, and 67 observations. Subsample B contains the effects of |∆ENV|,

|∆SOC|, and |∆CSP| over respectively 184, 187, and 199 observations. The subsample selection is described in

Section 3.6. All models (1)-(6) include year and industry fixed effects. The t-statistics based on robust standard

errors are in parentheses. Appendix A presents definitions and data sources of all used variables. *, ** and ***

denote statistical significance at the 0.10, 0.05 and 0.01 levels (2 tailed), respectively.

Subsample A Subsample B

Independent variables (1) (2) (3) (4) (5) (6)

ATCSRD proxy

|∆ENV| 0.002**

(2.20)

-0.000

(-1.59)

|∆SOC| -0.000

(-0.13)

0.000

(0.45)

|∆CSP| 0.000

(0.07)

-0.000

(-0.47)

Acquirer characteristics

ASIZE -0.013

(-1.53)

0.005

(0.44)

0.001

(0.10)

0.000

(0.07)

-0.006

(-1.06)

-0.001

(-0.23)

AROA 0.037

(0.20)

0.440***

(2.94)

0.320

(1.35)

0.096

(1.16)

0.044

(0.53)

0.026

(0.35)

AFCF 0.126

(0.94)

-0.044

(-0.31)

0.148

(1.06)

-0.049

(-0.77)

-0.021

(-0.37)

-0.026

(-0.45)

TROA -0.196*

(-1.47)

0.168

(0.73)

-0.149

(-0.56)

-0.144**

(-2.04)

-0.159**

(-2.45)

-0.145**

(-2.08)

TTQ 0.014*

(1.22)

-0.009

(-0.75)

0.002

(0.09)

0.005

(0.99)

0.005

(1.08)

0.004

(0.84)

Deal characteristics

RELDS -0.016*

(-1.94)

0.001

(0.07)

-0.006

(-0.76)

-0.004

(-0.21)

-0.004

(-0.38)

0.003

(0.25)

INDDIV -0.027

(-0.92)

-0.066*

(-1.94)

-0.043

(-1.35)

-0.006

(-0.45)

0.002

(0.19)

-0.003

(-0.26)

COMP -0.103**

(-2.29)

-0.017

(-0.31)

-0.046

(-0.79)

0.027

(1.19)

0.011

(0.45)

0.017

(0.76)

DOM -0.032

(-1.38)

-0.010

(-0.44)

-0.027

(-1.18)

-0.008

(-0.76)

-0.009

(-0.76)

-0.007

(-0.65)

METHOD -0.029

(-1.30)

-0.016

(-0.59)

-0.012

(-0.40)

-0.015

(-1.17)

-0.021

(-1.77)

-0.015

(-1.26)

Strength of institutional frameworks

IF -0.030

(-1.11)

0.080**

(-2.47)

0.051

(1.22)

-0.021

(-1.18)

-0.003

(-0.21)

-0.010

(-0.56)

IF*CSR proxy 0.001*

(1.77)

-0.001*

(-1.86)

-0.000

(-0.67)

0.000

(0.51)

0.000

(0.54)

0.000

(0.41)

Constant 0.198

(1.47)

-0.079

(-0.57)

-0.010

(-0.06)

0.081

(0.81)

0.220**

(2.50)

0.165*

(1.94)

Year-fixed effects Yes Yes Yes Yes Yes Yes

Industry-fixed effects Yes Yes Yes Yes Yes Yes

Country-fixed effects No No No No No No

Adjusted R2 0.239 0.138 0.028 0.082 0.076 0.084

Observations 79 79 67 184 187 199

47

Appendix G. Robustness check different event windows full sample. This table reports the OLS regression

results of the full sample with Acquirers’ CARs as dependent variable and ENV as the main independent

variable. The event-study methodology used to calculate the different CARs is described in Appendix A. The

full sample contains 309 observations from 36 to 34 unique countries over the 2004-2017 period. The models

(1), (2), (3), (10), and (15) represents the three-day, five-day, seven-day, twenty-one-day, and thirty-one day

window respectively. The sample selection is described in Section 3.4. All the models include year, country,

and industry fixed effects. The t-statistics based on robust standard errors are in parentheses. Appendix A

presents definitions and data sources of all used variables. *, ** and *** denote statistical significance at the

0.10, 0.05 and 0.01 levels (2 tailed), respectively.

Independent variables (1) (2) (3) (10) (15)

CSR proxy

ENV 0.000*

(1.86)

0.000**

(2.02)

0.000**

(2.04)

0.000**

(2.20)

0.000*

(1.85)

Firm characteristics:

ALNMV -0.004

(-1.21)

-0.002

(-0.61)

-0.006

(-1.52)

-0.011**

(-2.57)

-0.010*

(-1.76)

AROA 0.004

(0.07)

0.009

(0.17)

0.037

(0.63)

0.090

(1.21)

0.030

(0.30)

AFCF -0.003

(-0.06)

0.002

(0.05)

0.017

(0.33)

-0.033

(-0.54)

-0.038

(-0.50)

TROA -0.046

(-0.96)

-0.052

(-1.18)

-0.085**

(-2.06)

-0.122**

(-1.98)

-0.082

(-1.18)

TTQ 0.004

(1.05)

0.004

(1.01)

0.003

(0.95)

0.016***

(3.22)

0.018***

(3.07)

Deal characteristics:

RELDS -0.002

(-0.45)

0.002

(0.36)

-0.001

(-0.24)

-0.004

(-0.61)

-0.010

(-1.13)

INDDIV -0.006

(-0.77)

-0.007

(-0.86)

-0.008

(-0.92)

-0.005

(-0.48)

-0.007

(-0.58)

COMP 0.002

(0.11)

0.010

(0.60)

0.004

(0.23)

-0.002

(-0.10)

0.009

(0.35)

DOM -0.011

(-1.25)

-0.013

(-1.60)

-0.017**

(-1.99)

-0.020*

(-1.83)

-0.021

(-1.55)

METHOD -0.004

(-0.42)

-0.003

(-0.36)

-0.003

(-0.30)

-0.008

(-0.70)

-0.011

(-0.79)

Constant 0.077

(1.49)

0.090*

(1.66)

0.102*

(1.71)

0.273***

(4.05)

0.302***

(3.59)

Year fixed effects Yes Yes Yes Yes Yes

Industry fixed effects Yes Yes Yes Yes Yes

Country fixed effects Yes Yes Yes Yes Yes

Adjusted R2 0.076 0.111 0.108 0.079 0.022

Observations 309 309 309 309 309

Appendix H. Robustness check different event windows subsample A and B. This table reports the OLS regression results of both subsamples A and B with Acquirers’

CARs as dependent variable and |∆ENV| as the main independent variable. The event-study methodology used to calculate the different CARs is described in Appendix A.

The full sample contains 309 observations from 36 to 34 unique countries over the 2004-2017 period. The models (1), (2), (3), (10), and (15) represents the three-day, five-

day, seven-day, twenty-one-day, and thirty-one day window respectively. The sample selection is described in Section 3.4. All the models include year, country, and industry

fixed effects, but are not disclosed for brevity. The t-statistics based on robust standard errors are in parentheses. Appendix A presents definitions and data sources of all used

variables. *, ** and *** denote statistical significance at the 0.10, 0.05 and 0.01 levels (2 tailed), respectively.

Subsample A Subsample B

Independent variables (1) (2) (3) (10) (15) (1) (2) (3) (10) (15)

ATCSRD proxy

|∆ENV| 0.002**

(2.58)

0.002**

(2.63)

0.002***

(2.78)

0.002

(1.65)

.002*

(1.93)

-0.000

(-0.75)

-0.000

(-0.72)

-0.000

(-1.08)

-0.000*

(-1.72)

-0.000

(-1.33)

Acquirer characteristics

ALNMV -0.004

(-0.48)

-0.004

(-0.59)

-0.008

(-0.99)

-0.003

(-0.24)

0.005

(0.37)

-0.002

(-0.43)

0.002

(0.41)

0.000

(0.06)

-0.009*

(-1.67)

-0.009

(-1.22)

AROA 0.115

(0.93)

0.088

(0.83)

0.067

(0.59)

0.232

(1.31)

0.234

(1.08)

0.006

(0.12)

0.011

(0.17)

0.043

(0.65)

0.033

(0.39)

-0.060

(-0.53)

AFCF 0.038

(0.31)

0.086

(0.81)

0.099

(0.84)

0.131

(0.80)

0.161

(0.76)

0.023

(0.50)

0.006

(0.12)

0.003

(0.06)

-0.009

(-0.13)

-0.038

(-0.46)

Target characteristics

TROA 0.043

(0.43)

-0.020

(-0.21)

-0.159

(-1.41)

-0.258*

(-1.91)

-0.219

(-1.13)

-0.059

(-1.25)

-0.061

(-1.29)

-0.076

(-1.63)

-0.102

(-1.37)

-0.081

(-1.03)

TTQ 0.005

(0.49)

0.010

(1.00)

0.012

(1.09)

0.026**

(2.22)

0.032**

(2.29)

0.004

(1.19)

0.003

(0.88)

0.002

(0.45)

0.014**

(2.32)

0.015**

(2.19)

Deal characteristics

RELDS -0.007

(-0.86)

-0.005

(-0.75)

-0.010

(-1.31)

0.007

(0.65)

0.005

(0.35)

-0.003

(-0.30)

0.003

(0.27)

0.004

(0.30)

-0.022

(-1.12)

-0.030

(-1.17)

INDDIV -0.021

(-0.87)

-0.026

(-1.32)

-0.025

(-1.08)

-0.023

(-.77)

-0.021

(-0.58)

0.002

(0.20)

0.003

(0.30)

0.001

(0.13)

0.009

(0.77)

0.004

(.32)

COMP -0.086**

(-2.31)

-0.069**

(-2.41)

-0.081**

(-2.30)

-0.174***

(-3.76)

-0.180***

(-3.78)

.013

(0.85)

0.021

(1.38)

.020

(1.21)

0.029

(1.36)

0.035

(1.24)

DOM -0.022

(-0.98)

-0.021

(-1.09)

-0.021

(-1.00)

-0.021

(-.73)

-0.044

(-1.22)

-0.005

(-0.55)

-0.011

(-1.29)

-0.018**

(-2.02)

-0.029**

(-2.36)

-0.024

(-1.61)

METHOD -0.024

(-1.01)

-0.025

(-1.36)

-0.020

(-0.98)

-0.034

(-1.32)

-0.027

(-0.83)

-0.005

(-0.53)

-0.005

(-0.53)

-0.007

(-0.70)

-0.013

(-0.99)

-0.015

(-0.93)

Constant 0.055

(0.47)

0.077

(0.78)

0.120

(0.97)

0.033

(0.21)

-0.079

(-0.43)

0.048

(0.63)

0.009

(0.11)

0.044

(0.48)

0.296***

(2.92)

0.354***

(2.59)

Adjusted R2 0.250 0.087 0.029 0.029 0.029 0.029 0.081 0.029 0.070 0.091

Observations 80 82 68 68 68 68 199 68 200 214

Appendix I. Robustness check non-U.S. sample. This table reports the OLS regression results of the non-

U.S. full sample with Acquirers’ CAR(-5,5) as dependent variable and the Targets’ CSR proxies (ENV, SOC,

CSP) as the main independent variables. The event-study methodology used to calculate CAR(-5,5) is described

in Section 3.5. The non-U.S. sample contains 196 observations over the 2004-2017 period. The models (1), (2),

and (3) regress ENV, SOC, and CSP respectively and include year, country (only U.K.), and industry fixed

effects. The t-statistics based on robust standard errors are in parentheses. Appendix A presents definitions and

data sources of all used variables. *, ** and *** denote statistical significance at the 0.10, 0.05 and 0.01 levels

(2 tailed), respectively.

Independent variables (1) (2) (3)

CSR proxy

ENV 0.000*

(1.79)

SOC 0.000

(.44)

CSP 0.000

(1.19)

Firm characteristics:

ASIZE -0.010*

(-1.68)

-0.006

(-1.18)

-0.008

(-1.42)

AROA 0.084

(1.08)

0.051

(0.67)

0.067

(0.87)

AFCF 0.084

(0.86)

0.097

(1.00)

0.093

(0.96)

TROA -0.115*

(-1.73)

-0.104

(-1.55)

-0.110

(-1.64)

TTQ 0.006

(1.06)

0.004

(0.75)

0.005

(0.92)

Deal characteristics:

RELDS 0.001

(0.08)

0.005

(0.45)

0.003

(0.25)

INDDIV -0.002

(-0.80)

-0.002

(-0.14)

-0.003

(-0.22)

COMP 0.044

(1.26)

0.044

(1.35)

0.046

(1.35)

DOM -0.018

(-1.40)

-0.012

(-0.98)

-0.014

(-1.12)

METHOD -0.022*

(-1.80)

-0.023*

(-1.82)

-0.023*

(-1.81)

Constant 0.116

(1.52)

0.104

(1.39)

0.114

(1.49)

Year fixed effects Yes Yes Yes

Industry fixed effects Yes Yes Yes

Country fixed effects Yes Yes Yes

Adjusted R2 0.157 0.134 0.142

Observations 196 196 196

50

Appendix J. Robustness check non-U.S. sample. This table reports the OLS regression results of the

subsamples A(A<T) and B(A>T) with Acquirers’ CAR(-5,5) as dependent variable and the ATCSRD proxies

(|∆ENV|, |∆SOC|, |∆CSP|) as the main independent variables. The event-study methodology used to calculate

CAR(-5,5) is described in Section 3.5. Subsample A contains the effects of |∆ENV|, |∆SOC|, and |∆CSP| over

respectively 50, 60, and 45 observations. Subsample B contains the effects of |∆ENV|, |∆SOC|, and |∆CSP| over

respectively 144, 136, and 151 observations. The subsample selection is described in Section 3.6. All models

(4)-(9) include year, country, and industry fixed effects. The t-statistics based on robust standard errors are in

parentheses. Appendix A presents definitions and data sources of all used variables. *, ** and *** denote

statistical significance at the 0.10, 0.05 and 0.01 levels (2 tailed), respectively.

Independent variables

(4)

SubsampleA

(5)

(6)

(7)

SubsampleB

(8)

(9)

ATCSRD proxy

|∆ENV| 0.001

(0.58)

-0.001**

(-2.38)

|∆SOC| -0.001

(-0.35)

-0.000

(-1.05)

|∆CSP| -0.002

(-0.86)

-0.000

(-1.15)

Firm characteristics

ASIZE -0.025

(-1.52)

-0.009

(-0.87)

-0.009

(-0.51)

-0.003

(-0.47)

-0.006

(-0.86)

-0.004

(-0.71)

AROA 0.448

(1.17)

0.282

(1.40)

0.067

(0.32)

0.132

(1.73)

-0.061

(-0.60)

0.015

(0.15)

AFCF 0.580

(1.68)

0.201

(0.95)

0.277

(1.12)

0.041

(0.39)

0.046

(0.42)

0.071

(0.65)

TROA 0.018

(0.04)

0.269

(1.12)

0.998

(2.16)

-0.159**

(-2.57)

-0.126**

(-2.19)

-0.146**

(-2.33)

TTQ -0.001

(-0.05)

-0.026*

(-1.97)

0.034*

(-2.11)

0.007

(1.46)

0.010**

(2.18)

0.008

(1.52)

Deal characteristics

RELDS -0.021

(-0.93)

-0.002

(-0.10)

0.001

(0.05)

-0.001

(-0.06)

0.002

(0.07)

0.003

(0.14)

INDDIV 0.019

(0.33)

0.038

(0.96)

0.035

(0.81)

-0.009

(-0.74)

0.002

(0.12)

0.000

(0.02)

COMP -0.072

(-0.64)

-0.001

(-0.01)

0.030

(0.36)

0.084***

(3.34)

0.079***

(2.81)

0.082**

(2.97)

DOM -0.055

(-0.65)

-0.032

(-0.93)

-0.121**

(-2.54)

-0.018

(-1.22)

-0.026

(-1.58)

-0.018

(-1.24)

METHOD -0.035

(-0.96)

0.006

(0.16)

-0.006

(-0.12)

-0.027**

(-2.03)

-0.028*

(-1.77)

-0.028*

(-1.95)

Constant 0.288

(1.32)

0.155

(0.88)

0.161

(0.57)

0.074

(0.66)

0.113

(1.08)

0.079

(0.88)

Year-fixed effects Yes Yes Yes Yes Yes Yes

Industry-fixed effects Yes Yes Yes Yes Yes Yes

Country-fixed effects Yes Yes Yes Yes Yes Yes

Adjusted R2 0.030 0.152 0.304 0.207 0.170 0.169

Observations 50 60 45 144 136 151

51

Appendix K. Robustness check controlling for SIN full sample. This table reports the OLS regression results

of the full sample with Acquirers’ CAR(-5,5) as dependent variable and the Targets’ CSR proxies (ENV, SOC,

CSP) and SIN interaction as the main independent variables. The event-study methodology used to calculate

CAR(-5,5) is described in Section 3.5. The full sample contains 309 observations from 36 to 34 unique countries

over the 2004-2017 period. The models (1), (2), and (3) include the full sample and regress ENV, SOC, and

CSP respectively. The sample selection is described in Section 3.4. All the models include year, country, and

industry fixed effects. The t-statistics based on robust standard errors are in parentheses. Appendix A presents

definitions and data sources of all used variables. *, ** and *** denote statistical significance at the 0.10, 0.05

and 0.01 levels (2 tailed), respectively.

Independent variables (1) (2) (3)

CSR proxy

ENV 0.001***

(2.80)

SOC 0.000

(0.81)

CSP 0.000*

(1.89)

Firm characteristics:

SIN (dummy) 0.028

(1.21)

0.045**

(2.24)

0.039*

(1.78)

SIN*CSR proxy -0.000

(-0.63)

-0.001*

(-1.81)

-0.000

(-1.33)

ASIZE -0.009**

(-2.17)

-0.006

(-1.39)

-0.007*

(-1.75)

AROA 0.080

(1.35)

0.060

(1.01)

0.070

(1.18)

AFCF 0.021

(0.38)

0.037

(0.65)

0.030

(0.54)

TROA -0.108*

(-1.89)

-0.092

(-1.52)

-0.100*

(-1.69)

TTQ 0.006

(1.41)

0.004

(0.83)

0.005

(1.13)

Deal characteristics:

RELDS -0.004

(-0.77)

-0.001

(-0.25)

-0.003

(-0.49)

INDDIV -0.005

(-0.54)

-0.003

(-0.33)

-0.005

(-0.49)

COMP 0.009

(0.43)

0.009

(0.45)

0.009

(0.46)

DOM -0.017*

(-1.87)

-0.013

(-1.41)

-0.014

(-1.52)

METHOD -0.008

(-0.84)

-0.010

(-1.04)

-0.009

(-0.89)

Constant 0.193***

(3.12)

0.179***

(2.79)

0.194***

(3.08)

Year fixed effects Yes Yes Yes

Industry fixed effects Yes Yes Yes

Country fixed effects Yes Yes Yes

Adjusted R2 0.108 0.085 0.092

Observations 309 309 309

52

Appendix L. Robustness check controlling for SIN subsamples. This table reports the OLS regression results

of the subsamples A(A<T) and B(A>T) with Acquirers’ CAR(-5,5) as dependent variable and the ATCSRD

proxies (|∆ENV|, |∆SOC|, |∆CSP|) and SIN interactions as the main independent variables. The event-study

methodology used to calculate CAR(-5,5) is described in Section 3.5. Subsample A contains the effects of

|∆ENV|, |∆SOC|, and |∆CSP| over respectively 86, 90, and 76 observations. Subsample B contains the effects

of |∆ENV|, |∆SOC|, and |∆CSP| over respectively 220, 219, and 233 observations. The subsample selection is

described in Section 3.6. All models (4)-(9) include year, country, and industry fixed effects. The t-statistics

based on robust standard errors are in parentheses. Appendix A presents definitions and data sources of all used

variables. *, ** and *** denote statistical significance at the 0.10, 0.05 and 0.01 levels (2 tailed), respectively.

Independent variables

(4)

SubsampleA

(5)

(6)

(7)

SubsampleB

(8)

(9)

ATCSRD proxy

|∆ENV| 0.001

(1.33)

-0.000

(-1.50)

|∆SOC| -0.000

(-0.08)

-0.000

(-0.21)

|∆CSP| -0.000

(-0.20)

-0.000

(-0.66)

Firm characteristics

SIN -0.036

(-0.78)

0.032

(0.85)

-0.019

(-0.24)

0.044*

(1.73)

0.004

(0.15)

0.020

(0.84)

SIN*CSR score 0.004**

(2.08)

-0.001

(-0.63)

0.001

(0.35)

-0.001

(-1.09)

0.000

(0.48)

-0.000

(-0.32)

ASIZE -0.010

(-1.15)

-0.004

(-0.36)

-0.010

(-0.90)

-0.001

(-0.22)

-0.006

(-1.23)

-0.002

(-0.40)

AROA 0.091

(0.57)

0.273*

(1.71)

0.176

(0.87)

0.068

(1.02)

-0.031

(-0.40)

-0.016

(-0.23)

AFCF 0.121

(0.99)

0.019

(0.12)

0.214

(1.23)

0.017

(0.28)

0.048

(0.79)

0.034

(0.57)

TROA -0.231*

(-1.86)

0.189

(1.03)

-0.000

(-0.00)

-0.110

(-1.65)

-0.116*

(-1.95)

-0.113*

(-1.82)

TTQ 0.011

(0.95)

-0.011

(-0.84)

0.002

(0.13)

0.004

(0.79)

0.005

(1.03)

0.004

(0.33)

Deal characteristics

RELDS -0.011

(-1.37)

0.004

(0.42)

-0.002

(-0.19)

-0.002

(-0.15)

-0.004

(-0.34)

0.004

(0.33)

INDDIV -0.014

(-0.51)

-0.039

(-1.18)

-0.019

(-0.61)

0.002

(0.14)

0.010

(0.95)

0.005

(0.49)

COMP -0.152***

(-4.36)

-0.004

(-0.07)

-0.034

(-0.60)

0.033

(1.60)

0.015

(0.66)

0.024

(1.16)

DOM -0.035

(-1.45)

-0.023

(-0.93)

-0.045*

(-1.71)

-0.017*

(-1.81)

-0.019*

(-1.76)

-0.016

(-1.63)

METHOD -0.015

(-0.70)

-0.022

(-0.81)

-0.018

(-0.52)

-0.011

(-0.96)

-0.017

(-1.45)

-0.010

(-0.92)

Constant 0.077

(0.57)

0.019

(0.14)

0.096

(0.66)

0.127

(1.24)

0.239***

(2.85)

0.164**

(2.04)

Year-fixed effects Yes Yes Yes Yes Yes Yes

Industry-fixed effects Yes Yes Yes Yes Yes Yes

Country-fixed effects Yes Yes Yes Yes Yes Yes

Adjusted R2 0.227 0.071 0.001 0.081 0.054 0.079

Observations 86 90 76 220 219 233