The relationship between Corporate Social Responsibility
and the pursuance of Mergers and Acquisitions
Master Thesis - Finance, Economics
University of Groningen, Faculty of Economics and Business
Kevin de Graaf Student number: 2728869
Supervisor: L. Dam Second Assessor: K.F. Roszbach
ABSTRACT
This paper studies the relationship between corporate social responsibility (CSR) and domestic and cross-border mergers and acquisitions (M&A) propensity. Using the ESG metrics Environment, Social and Governance, I empirically test how CSR relates to firm’s acquisition likelihood. I extend this research by analyzing how domestic and cross-border transactions are affected separately. I find evidence that companies performing strongly in the Environment and Governance departments are less likely to engage in M&A. I also put forward that firms with high Social scores are more inclined to pursue M&A.
I
INTRODUCTION
Firms invest in CSR as part of their vision and in order to create value for their stakeholders. As stakeholders benefit from CSR practices, the role of CSR in strategic decision-making is interesting, since M&A often creates uncertainty for those stakeholders. As such, CSR and M&A practices are connected through strategic decision-making. Although mergers and acquisitions are a thoroughly covered theme in financial and economic literature, empirical evidence on the relationship between M&A and CSR is still lacking. In this study, I aim to take a step forward in exploring the relationship between CSR and the propensity of firms to pursue mergers and acquisitions.1
As stated, the nature of CSR and M&A seem to contradict. The existing literature supports CSR practices being value enhancing for the firm itself, as well as its stakeholders (Deng, Kang, and Low 2013; Servaes and Tamayo, 2013). On the other hand, the motivation for engaging in mergers and acquisitions mainly comprises of improving shareholders’ wealth by increasing profits, amongst others. As such, the classic debate on stakeholder and shareholder theory is underlying my study. Furthermore, I want to investigate if the implied relationship between CSR and M&A differs between domestic and cross-border acquisitions. Cross-border M&A add a set of frictions that may deter or facilitate mergers, examples being cultural, geographical and institutional differences between both parties (Erel, Liao and Weisbach (2012). As CSR levels tend to deviate amongst countries (Liang and Renneboog, 2017), I expect a different relationship between CSR and M&A when considering cross-border acquisitions.
A possible explanation for the gap in literature between the two topics may be the empirical challenges that arise. The first challenge is of econometric nature. It is difficult to identify a causal relationship between CSR and acquisition likelihood, as CSR is likely to be partly determined by non-firm level factors, such as institutions and culture. The second challenge lies in the availability of data. There exist only a few providers of quantifiable CSR data. As mainly the large listed corporations disclose their CSR efforts and hence receive ratings, the majority of CSR data relates to these big firms that predominantly act as the bidding party in an M&A transaction. I overcome these challenges by adopting a rich data set of acquirers from different countries and by carefully assessing potential endogeneity bias.
In order to answer my research question, I make use of academic literature on corporate social responsibility, mergers and acquisitions, governance and ethics. I use CSR data on environmental, social and governance (ESG) practices to explain the likelihood of M&A pursuance. My data set includes scores on environmental, social and governance
practices of all listed European and United States firms for which this CSR data is available. Using a sample of 3,658 conducted mergers and acquisitions by 1,653 unique firms during the period 2002-2018, I am able to identify estimates on the acquisition likelihood of companies with different CSR scores.
I find that firms with higher environmental and governance scores are less likely to pursue M&A. One standard deviation increase in the Environment category decreases the acquisition likelihood by 11.5 percentage points, while the same increase in the Governance metric decreases the propensity to pursue M&A by 9.4 percentage points. Furthermore, I find that the Social category poses a positive relationship with M&A pursuance. One standard deviation increase in this metric increases acquisition likelihood by 13.6 percentage points. Domestic transactions show similar results as the main sample, while I cannot identify a distinct relationship between CSR and acquisition likelihood for the cross-border sample. My results suggest that companies with stronger environmental performance more carefully choose potential targets for takeovers, possibly because acquiring targets with low environmental performance increases integration costs. Also, higher scores in the Social metric suggest that a more dedicated workforce leads to better overall firm performance and decreases uncertainty surrounding deal completion. Furthermore, my findings on the Governance category are consistent with earlier theoretical findings that sounder governance mechanisms decrease the likelihood of short-term irrational decision-making.
To my knowledge, this research is the first to empirically test the relationship between CSR and M&A pursuance. As such, the main contribution of this paper is that the statistically and economically significant changes in acquisition likelihood for companies with different ESG scores uncover a possible relationship between the themes. As CSR engagement becomes the norm and due diligence on CSR factors becomes regular routine in M&A transactions, I think this research proposes solid ground for future research to expand on.2
The next section provides for a thorough overview of the existing literature on CSR and M&A. In addition, I lay out the relevant theories in the existing literature and explain what these theories predict, preferably in the context of my research. After the literature review, I report the data selection procedure and describe the data I use. In the section following the data, I describe the methodology and formalize my research question into statistical hypotheses using the existing literature as a foundation. In section V, I present my main results and explain what the results mean in the context of this paper and the existing literature. In the last section, I conclude my findings and list directions for future research.
2 Mergermarket (2019) reports that 90% of firms conducts due diligence on ESG related issues in
II
LITERATURE REVIEW
This section explores and analyzes relevant branches of literature that deal with portions of this study. Subsequently, I break down the seminal theories, debates and evidence put forward by the existing literature and identify where this study contributes to the existing work.
Corporate Social Responsibility
Edward Freeman’s (1984) influential publication: ‘Strategic Management: A Stakeholder Approach’ introduced stakeholder theory, stating that ‘a firm should create value for all stakeholders, not just shareholders.’ Advocates of stakeholder theory basically say that profits and business ethics are not mutually exclusive (Freeman, 1984; Porter and Kramer, 2006). Stakeholder theory has come a long way from there and is embedded in business practices we know today. Next to creating value for its shareholders, modern day corporations are expected to act responsibly towards their surroundings, either being their environment or the society, it conducts business in.
This paper explores a term coined from stakeholder theory; corporate social responsibility. The term refers to corporations ‘serving people, communities, and society in ways that go above and beyond what is legally required of a firm’ (Jo and Harjoto, 2011). A contradicting view with respect to CSR is the shareholder view, in which the single purpose of corporations is to maximize profits (Levitt, 1958). According to Friedman (1970), the justification for permitting the corporate executive to be selected by stockholders disappears when the executive spends proceeds for ‘social’ purposes and hence does not act in the best interest of his principal.
Regardless of viewing CSR as a good or bad construct, statistics show that CSR is a growing phenomenon.3 Companies are increasingly expected to be responsible for the societies in which they operate (Werther and Chandler, 2005). Respondents to a McKinsey & Company (2009) survey, in which CFO’s and institutional investors were asked on their opinion on environmental, social and government programs and its value creation, largely believe that CSR creates value over the long term. This supports the statement that companies are engaging in socially responsible endeavors increasingly, as they believe these are value-adding activities.
The belief in the, financially, value-adding properties of CSR is a main focus of the academic literature on the topic as well. One of the renowned studies on this topic is written
by McWilliams and Siegel (2000). They critique the positive effects found in studies on the relationship between CSR and financial performance, as they claim that these results were flawed because of misspecified econometric models. However, the current state of literature is generally in favour of CSR being performance enhancing. Acclaimed studies conducted by Eccles, Ioannou and Serafeim (2012), Tsoutsoura (2004) and van Beurden and Gössling (2014) all provide empirical evidence for a positive correlation between CSR performance and firms’ financial performance.
The main motive for companies to invest in CSR is to enhance firm value (Deng, Kang and Low, 2013; Servaes and Tamayo, 2013), which can be measured in different ways other than financial performance. For example, Jo and Harjoto (2011) empirically investigate the effects of corporate governance and CSR on firm value. Utilizing a sample of 2,952 firms from 1993 to 2004, they find that CSR engagement positively influences Tobin’s q, a variable they use as proxy for firm value. Cheng, Ioannou and Serafeim (2014) study the role of CSR in the access of finances. Using a large sample of firms across 49 countries, they find that firms with better CSR performance face significantly lower capital constraints. The authors then argue that better stakeholder engagement and transparency around CSR performance are key drivers in reducing these capital constraints. Furthermore, they propose that companies that perform better in terms of CSR decrease agency costs and the likelihood of short-term opportunistic behaviour.
A lesser supported argument is that of Waddock and Graves (1997). They argue the opposite of the CSR – firm value link and propose that firms that invest more in CSR generally have better financial performance in the first place. They label this finding ‘doing good by doing well’ and is based upon firms investing in CSR because they believe that this will be viewed as a good cause by the public. While this argument may carry less support, it is relevant as it initiates debate on the direction of causality of CSR.
Liang and Renneboog (2017) put forward that both of these arguments are of inferior importance than legal origin is. Using CSR ratings for 23,000 firms from 114 countries, they find that CSR levels and legal origin are strongly correlated. Specifically mentioning the ‘doing good by doing well’ debate, they argue that legal origin is a stronger explanation for CSR levels. Their results show that firms from common law countries have lower CSR than firms from civil law countries. This argument retraces the inherent institutional differences between countries being a factor in play.
implies that firms with more public exposure, for instance S&P 500 firms, gain more from their CSR engagement than lesser known firms do.
Mergers and Acquisitions
To understand why companies engage in mergers and acquisitions, it is important to know what their motives are. According to Brouthers (1998), the three generally accepted categories of M&A motives are economic, personal and strategic respectively.4 He states that economic motives include increasing profits, achieving economies of scale, risk spreading and cost reduction amongst others. While the economic motives seem straightforward, the other two categories of motives deserve some more explanation.
Personal motives occur because managers see personal benefit. Examples of personal motives include increased prestige through increased sales and firm growth, or increased remuneration through increased sales or profitability (Lubatkin, 1983). The personal benefits theory relates to agency theory, in which the agent does not act in the best interests of the principal (Jensen and Meckling, 1976). Lubatkin (1983) argues that one of the reasons that mergers do not show improved performance is that managers may seek to maximize their own wealth at the expense of the shareholder’s wealth. Malmendier and Tate (2008) find that overconfident CEO’s are more eager to make acquisitions and are more likely than rational CEO’s to undertake value-destroying takeovers. Another study relating personal benefits to mergers and acquisition is the paper by Grinstein and Hribar (2004). They find that CEO’s with more power tend to engage in larger deals relative to their own firm size, and that the market responds more negatively to their acquisition announcements. They provide for evidence that managerial power is a primary driver of M&A bonuses.
Finally, Brouthers (1998) argues that strategic motives may be root to M&A. Examples include acquiring resources and pursuing market power. Borenstein (1990) examines mergers in the airline industry and finds that in such an oligopolistic industry, strategic merging can result in airport dominance and subsequent price increases. Erel, Liao and Weisbach (2012) add to this argument, stating that firms can charge profit-maximizing prices post-merger, which they were not able to set prior to the merger. Also, they specify that mergers can lower the combined tax liability of the two firms.
As my study focuses on an acquirer’s propensity to engage in mergers and acquisitions, it is important to know how these operations perform from their perspective. In other words, do the perceived upsides materialize? M&A performance has extensively been
4 Brouthers (1998) bases his definition of the economic and personal motives on work conducted by
researched to this date and the majority of existing literature finds that excess returns to the bidding party are essentially zero, implying that acquisitions from an acquirer’s perspective are zero net present value operations.5 This finding is supported by Agrawal, Jaffe and Mandelker (1992), who, with a large sample of mergers, find that shareholders of the acquiring firm suffer a 10% loss over the five-year post-merger period. Healy, Palepu and Ruback (1992) also find negative excess returns for the acquirer of 2.2% in a study involving the 50 largest US mergers in the period 1979 to 1983.
However, it is not always the case that acquirer’s experience negative returns. For instance, Agrawal and Jaffe (2002) survey a large set of literature on long-turn stock returns following acquisitions. They conclude that long-run performance is negative following mergers, but performance is non-negative, and sometimes positive, after tender offers. While short and long-term financial performance may be the most popular measures of M&A performance, other performance measures do exist. Examples of other performance measures include the effectiveness and speed of integration and retention of customers and employees.6
Besides investigating what the relationship between CSR and M&A pursuance is, my study also contributes to potential differences in acquisition likelihood between domestic and border acquisitions. Erel, Liao and Weisbach (2012) report that the volume of cross-border acquisitions has been growing worldwide, which appears to be a logical result of globalization. They argue that cross-border M&A add a set of frictions that can either impede or facilitate mergers. Examples of impeding factors are cultural or geographical differences between both parties that increase the costs of combining two firms, while differences in wealth between countries may facilitate M&A, as firms from wealthier countries tend to purchase firms from poorer countries due to a lower cost of capital (Froot and Stein, 1991). Hyun and Kim (2010) examine differences between domestic and cross-border M&A using a sample of firms from 101 countries over the year 1989 to 2005. They find that, next to geography, the level of economic integration between countries describes M&A flows.
5 Bruner (2001) summarizes evidence from 130 M&A performance studies from 1971 to 2001. The
vast majority of acquiring firms report negative or neutral returns. Returns to target firms are on average significantly positive.
6 Zollo and Meier (2008) analyzed 88 M&A performance articles published in top management and
Linking CSR and M&A
While mergers and acquisitions are a vastly researched topic in financial and economic literature, the relationship between M&A and CSR is not as thoroughly studied. An explanation for this lies in the availability of data on CSR. However, as reporting rates on firms’ CSR activities have risen worldwide since the start of the millennium, empirical research on the topic has gained in interest.7
Waddock and Graves (2006) studied the impact of M&A on corporate stakeholder practices by comparing merging companies’ stakeholders-related practices pre- and post-merger. Using CSR data on acquirers and targets from S&P 500 companies, they find that stakeholder practices do not play a role in M&A decisions. Importantly, they note that few differences in stakeholder practices existed between acquirers and targets to begin with.
Chen and Gavious (2015) explored the relation between CSR and sale price with a sample of Israeli transactions and found no link between CSR and target valuation. Based on that study, Gomes and Marsat (2018) used a sample of worldwide listed companies and found that CSR performance of target firms is positively related to M&A bid premiums, implying that acquirers see positives in the CSR practices of targets. The latter finding is in line with a PwC (2012) survey that assessed corporate acquirer’s attitudes towards evaluating ESG risks and opportunities in their M&A activities. Key outcomes of this survey include that ESG factors can affect the likelihood of a deal occurring and that the cost of bringing a target company up to the acquirer’s standards with regards to managing ESG factors is a significant consideration in the deal process. The latter statement would suggest that firms with higher ESG scores have more difficulties integrating potential targets into their company, because discrepancies in ESG levels could give rise to additional roadblocks.
Next to bid premia, M&A uncertainty is a topic of interest. Arouri, Gomes and Pukthuanthong (2019) investigate the effects of CSR on the completion uncertainty of M&A, where they use an international sample of firms spanning over a long-time horizon. Their main finding suggests that acquirers with high CSR performance face reduced uncertainty surrounding a takeover. They motivate this finding by arguing that strong CSR attributes possibly reduce the probability of breach in implicit contracts and increase stakeholders’ support towards a firm.
7 Blasco and King (2017) find in a KPMG survey that 93% of the world’s largest 250 companies report
Deng, Kang and Low (2013) investigate whether CSR creates value for acquiring firms’ shareholders. They use a sample of 1,556 US firms from 1992 until 2007 and find that, compared with low CSR acquirers, high CSR acquirers realize higher merger announcement returns and larger increase in post-merger long-term operating performance. Next to this, they find that mergers by high CSR acquirers take less time to complete and are less likely to fail than mergers by low CSR acquirers.
III
DATA
This section covers the collected data, the process of gathering the data and the sources used. Also, detailed information on the variables used to conduct our analysis are provided. First, I break down the dependent variables. In the last subsection, descriptive statistics of the M&A data are provided and I expand on the data compiling process.
I use ESG scores data provided by Refinitiv Datastream to measure CSR.8 The database includes data for close to 9,000 companies globally, with approximately 1,000 companies dating back to 2002. The scores are designed to transparently and objectively measure a company’s performance, commitment and effectiveness across 10 main themes and are based on company-reported information. The company information is processed by a team of research analysts to provide for objective coverage. There are currently over 450 ESG measures, which are all processed manually and updated on a continuous basis aligned with corporate reporting patterns. A combination of those 10 main themes, weighted proportionately to the count of measures within each category, formulates three pillar scores: Environmental (E), Social (S) and Governance (G).
The final ESG score is a relative sum of the category weights which vary per industry for the Environmental and Social categories. For the Governance category, the weights remain the same across all industries. In the calculation of the ESG metric, Social is weighted the highest and Governance the lowest. The final ESG score and its metrics are all measured from zero, being the lowest score, to 100 and represent yearly scores. Examples of measures in the Environmental category are a corporation’s resource use, emissions and innovation. The Social score is calculated by using data on human rights issues and product responsibility,
8 In January 2018, Thomson Reuters Corp agreed to sell a 55% stake in their Financial & Risk unit to
amongst others. Finally, the Government pillar includes data on management and shareholders (Refinitiv, 2020).9
I use CSR time series data of the US and Europe. As CSR data of the other regions do not date back as early as for the US and Europe, I opted to examine just these two regions. Table I presents summary statistics on the CSR categories of the acquiring firms from the US and Europe. The table displays that the Social category has the highest mean, while ESG has the highest median value. Government scores the lowest mean and median values. Also, the reported standard deviations on all metrics are relatively high, indicating that the dispersion of data is large. As the ESG score is compiled out of the other pillars, its standard deviation is the smallest. Furthermore, this metric has the most observations because some companies started reporting ESG scores before also reporting pillar scores. An important point to consider regarding ESG scores is that companies are not required to disclose their CSR efforts. Hence, the data probably includes an upwards bias as companies are more likely to disclose their CSR when they do well in this department (Dhaliwal, Li, Tsang and Yang, 2011).
Table I
Firm-level summary statistics of CSR variables for the sample of acquirers
This table displays summary statistics of the acquirer sample for the CSR variables used in the analysis. The sample period is from January 2003 until December 2018 and the observations are of yearly frequency.
The sample consists of national and international takeovers between 2003 and 2018. The sample selection is derived in multiple steps and is in line with other studies regarding this theme (Arouri, Gomes and Pukthuanthong, 2018; Deng, Kang and Low, 2013). The initial sample is extracted from Refinitiv Eikon and contains completed deals over the 2003-2018 period from publicly traded acquirers. Importantly, the acquirer owns less than 50% of the target company’s shares prior to the offer and obtains a majority stake after the offer is accepted. Furthermore, the deal value has a minimum of $10 million and the acquirers are based in either the US or Europe. As I want to measure the likelihood of a firm being an acquirer, I want to limit the effect of serial acquirers in the sample. Hence, I limit the maximum of yearly acquisitions to be equal to one.
9 Appendix A contains an outlay of the different measures and their definitions
Mean Median Std. Dev Min Max Observations
ESG 55.2 55.5 17.9 7.3 97.7 14,566
Environment 53.6 52.5 23.1 4.1 99.5 13,199
Social 55.4 54.9 20.6 3.8 99.1 13,395
Next, I extracted all US and European companies for which CSR data is disclosed. I merged both samples and subsequently removed deals that were executed by acquirers without CSR scores available. Then, I gathered control variables and removed the firms for which there was data missing on any of those control variables. Lastly, I merged the sample with Orbis to obtain the industry SIC codes. I end up with a total of 3,658 acquisitions conducted by 1,653 unique firms.
Table II and III present summary statistics on the sample of acquirers. As seen in Panel A of Table II, the United States represent more than half of the total sample of acquiring firms. Regarding the European countries in the sample, the United Kingdom host the most acquiring firms. Panel B of Table II shows that the majority of the sample firms are from the Manufacturing and Finance, Insurance and Real Estate sectors, which together comprise about 60% of the total sample. Table III provides a distribution of the M&A sample by year. The table shows that the latter half of the time period has more conducted acquisitions than the first half. Furthermore, the number of domestic transactions is clearly higher than the number of cross-border acquisitions across the sample. Also, the mean values are highly skewed upwards because of large transactions in the respective years.
Next to the firms that have conducted takeovers, I assigned a control group. This group consists out of companies that do not undertake M&A that satisfy the above conditions. The selection process amounts to the following steps: firstly, I gathered all European and US firms for which Refinitiv Datastream has CSR data available. Then, I removed all firms from this sample that were already selected for the M&A sample. Lastly, I removed the firms for which there was data missing on any of the control variables. ROE (return on equity) is my performance measurement of choice and is calculated by dividing net income by shareholders’ equity. D/E (debt-to-equity ratio) is defined as the total liabilities divided by shareholders’ equity. MTB (market-to-book ratio) is measured by dividing market value by book value. Market cap (market capitalization) is defined by multiplying the share price and the number of issued shares. I use the natural logarithm of market capitalization to account for large differences between companies. Employees represents the number of fulltime and part-time employed workers. Again, I use the natural logarithm of this variable to account for large discrepancies between firms. To address unobserved heterogeneity and improve robustness of results, I control for year, industry and country fixed effects. Outliers in the financial variables are dealt with by eliminating the top and bottom 1% of those variables.10
10 Appendix B12 includes my results without winsorizing, my results are similar to those presented in
Table II
Industry and country composition of the sample of acquirers
This table displays a sample distribution by country and industry for the sample of acquirers. Panel A shows the country of origin of the acquirer sample. Panel B shows the major industry sector to which a company belongs, where SIC stands for Standard Industrial Classification. The sample period is January 2003 – December 2018.
Panel A: Distribution of acquirer sample by country
Country Firms % of total
United States 989 59.8 United Kingdom 164 9.9 Germany 67 4.1 France 67 4.1 Italy 46 2.8 Sweden 41 2.5 Switzerland 39 2.4 Spain 38 2.3 Netherlands 27 1.6 Norway 21 1.3 Other 154 9.3 Total 1,653 100.0
Panel B: Distribution of acquirer sample by industry
Industry (first two digits of the SIC-codes) Firms % of total Agriculture, Forestry and Fishing (01-09) 5 0.3 Mining and Construction (10-17) 120 7.3
Manufacturing (20-39) 592 35.8
Transportation and Communications (40-49) 174 10.5 Wholesale and Retail Trade (50-59) 102 6.2 Finance, Insurance and Real Estate (60-69) 392 23.7
Services (70-89) 261 15.8
Public Administration (90-99) 7 0.4
Table III
Sample distribution of domestic and cross-border M&A deals from 2003-2018
This table displays a sample distribution of domestic and cross-border M&A deals by year in the time period January 2003 – December 2018. Deals represents the number of deals per year. % of total denotes the share of yearly deals as a percentage of the total amount of deals. Mean and median represent the mean and median values of the deals in a respective year. Domestic and Cross-border denote the number of domestic and cross-border deals respectively.
Year Deals % of total Mean Median Domestic Cross-border
2003 153 4.2% 1127.0 122.3 98 55 2004 173 4.7% 866.5 118.1 116 57 2005 212 5.8% 1920.8 169.0 127 85 2006 227 6.2% 1528.1 185.3 126 101 2007 272 7.4% 1005.1 158.1 151 121 2008 198 5.4% 1135.1 170.6 96 102 2009 147 4.0% 2098.8 274.5 94 53 2010 219 6.0% 802.2 148.6 134 85 2011 224 6.1% 1073.3 150.1 139 85 2012 219 6.0% 634.5 158.5 125 94 2013 207 5.7% 885.0 182.7 139 68 2014 295 8.1% 1475.8 180.0 179 116 2015 296 8.1% 1931.9 200.0 195 101 2016 253 6.9% 2275.1 240.8 176 77 2017 272 7.4% 1902.1 270.0 182 90 2018 291 8.0% 2204.6 353.5 186 105 Total 3,658 100.0% 1465.9 187.4 2,263 1,395
To account for possible multicollinearity issues, I opt to examine the correlations between the variables. None of the variables are highly correlated with one another, mitigating collinearity concerns.11 Table IV displays a sample split of all independent variables between the acquirer and control groups. The CSR metrics as well as the market capitalization and employee variables show statistically significant differences between both groups. On average, the ESG score of acquirer group is six points higher than the score of the control group. This result is significant at the 1% level, indicating that the difference in mean ESG scores between acquirer’s and non-acquirers is statistically different from zero. The difference of CSR pillar scores between the two groups are also all statistically different from zero. This indicates that regarding CSR, the acquirer group outscores the control group on all metrics.
Table V presents a sample split of the CSR metrics by region. Most notably, European firms have significantly higher average CSR scores relative to their US peers. This result implies that, on average, European firms engage more in CSR than American firms. Similar to Table IV, acquirers outscore the non-acquiring firms over all CSR metrics. Another interesting result
from Table V is that US acquirers score higher on the Governance metric than their European counterparts, although performing worse on all other metrics. This finding supports the results of a study conducted by Aggarwal, Erel, Stulz and Williamson (2007), who provide evidence that US firms on average have better governance attributes than European firms.12 Doidge, Karolyi and Stulz (2006) show that country characteristics are an important determinant of governance on the firm-level and that the benefits of good governance depends on the country’s institutions. Furthermore, they propose that governance is cheaper to put in place in countries with better institutions. As my European sample consists out of many different countries with different institutions in place, this might attribute to a relatively better performance of US firms in terms of Governance.
Table IV
Summary statistics for the acquirer and control groups
This table displays a sample split of variable means. The means are displayed for the acquirer and control group. Difference is calculated as the value of the acquirer group subtracted by the value of the control group. The p-values report the outcomes of a difference in mean value t-test between the groups. *, **, *** indicate statistical significance at the 10%, 5% and 1% level respectively.
Table V
Sample split of CSR variable means by region
This table displays a sample split of CSR variable means by region. The means are displayed for the acquirer and control group. Difference is calculated as the value of the acquirer group subtracted by the value of the control group. *, **, *** report the outcomes of a difference in mean value t-test between the groups and indicate statistical significance at the 10%, 5% and 1% level respectively.
ESG Environment Social Government European acquirer group 59.88 60.58 59.48 51.25 US acquirer group 51.20 48.56 52.38 52.31 Difference 8.68*** 12.02*** 7.10*** -1.06***
European control group 53.71 59.72 57.89 49.71 US control group 45.30 42.73 46.45 47.47 Difference 8.41*** 16.99*** 11.44*** 2.24*** 12 Aggarwal, Erel, Stulz and Williamson (2007) find that the UK, Finland, Switzerland and the
Netherlands are the only European countries where more than 10% of firms have better governance than their matching US counterparts.
Acquirer group Control group Difference p-Value
IV
METHODOLOGY AND HYPOTHESES
This section links the analyzed literature of chapter II and the data of the previous chapter to the research question. I explore hypotheses that will be tested and show which regressions I estimate. In order to determine whether the fixed-effects model or the random-effects model is an appropriate estimation method for the longitudinal data set, I conduct a Hausman test. The test results show that the models are statistically not significantly different from zero, hence I decide to use the random-effects estimator in order to find the effect of the CSR metrics on the acquisition likelihood. I conduct my base estimation by using the following logistic regression:
𝑃[𝑦𝑖𝑡|𝑥𝑖𝑡] =
𝑒𝑥𝑖𝑡′𝛽
1 + 𝑒𝑥𝑖𝑡′𝛽 (1)
where 𝑦𝑖𝑡 is a dummy variable equal to one if firm 𝑖 announces one or more transactions in year 𝑡, subject to the conditions arranged in the Data chapter, and equal to zero otherwise. 𝑥𝑖𝑡′ is a vector containing the relevant CSR metric and the following control variables: ROE measures return on equity; D/E measures the debt-to-equity ratio; MTB measures market-to-book-ratio; MARKET CAP represents the natural logarithm of market capitalization and
EMPLOYEES measures the natural logarithm of employees in year 𝑡. Finally, 𝑥𝑖𝑡′ also includes year, country and industry fixed-effects. I control for year-effects in order to deal with time trends that might bias the relationship between CSR and acquisitions I want to capture. I use industry fixed-effects to ensure that possible high or low CSR industries do not bias the results. For the same reasons, I include country fixed-effects to mitigate differences between countries. Finally, 𝛽 is a vector of coefficients.
To account for serial correlation, I conduct Wooldridge tests on my dependent and independent variables. The test outcomes return large F-statistics, which lead me to not reject the null hypothesis of no autocorrelation. To account for any possible heteroskedasticity problems, I estimate the model with robust standard errors.
had a positive impact on returns for the majority of respondents.13 This finding is interesting, because the firms did not mention increased returns as a driver for CSR investments. The same survey posits that there is no certainty yet on stronger ESG performance yielding better returns on investments, but they do confirm that bad ESG performance definitely causes lower returns.
To my knowledge, there is no literature yet providing evidence for a relationship between M&A propensity and environmental-friendly firms. Despite the lack of prior research, I predict that stronger performance in the Environment pillar reduces the likelihood of M&A activity. I think that performing well in this category is a costly and time-consuming investment, hence I expect that environmentally strong performing bidders have less potential target firms that conform to their standards and therefore this will reduce their acquisition likelihood.
The social category encompasses themes such as product responsibility and employees. I expect firms that care better for their employees in terms of salary, working conditions and training to perform better, and in turn be more likely to conduct M&A’s. Valentine and Fleischman (2008) present evidence that ethical codes and ethics training are positively associated with employee job satisfaction. They also argue that enhancing social performance likely leads to more satisfied employees, which in turn could translate to enable organizations to reach higher levels of CSR in general. Similarly, Edmans (2012) states that job satisfaction can improve firm value and firm performance in several ways. He posits that a satisfying workplace can foster job embeddedness, job motivation and ensure that talented employees stay with the firm, as well as being a valuable tool for recruiting new workers.
I expect firms with higher Governance scores to be less likely to conduct mergers and acquisitions. Regarding the assumption that corporations with more competent management suffer less from agency problems, I propose that the likelihood of unnecessary acquisitions decreases in the case of stronger Governance performance. Also, as CSR strategy directly influences the Governance metric, I propose that firms with a deliberate CSR strategy more carefully choose their targets, again decreasing the likelihood of unnecessary acquisitions. Both of these statements align with the study of Cheng, Ioannou and Serafeim (2014), which I mentioned in chapter II, but I put forward that these effects are driven by the Governance category specifically.
Based on the sample data, cross-border acquisitions are conducted less frequently relative to domestic acquisitions. About 30% of the sample’s transactions are cross-border
13 Mergermarket (2019) surveyed private equities, asset managers and corporate executives on the
takeovers. This is an accurate representation of the observed global M&A volume, as according to a Deloitte (2017) study, 31% of total global deals in 2016 were cross-border deals. Cross-border takeovers are different than their domestic counterpart by nature. According to Erel, Liao and Weisbach (2012), national borders can add an additional set of frictions that can impede or facilitate M&A. Also, they mention that cultural, geographic, currency and governance-related differences across countries make the transaction more complex.
In order to form an expectation on difference between domestic and cross-border M&A, I assume that companies with higher CSR scores did not achieve these by chance, but by deliberate strategy.14 The following assumption I make is that companies do not want to forgo on these investments by acquiring companies that do not align with their own CSR strategy. This assumption is supported by the results of a PwC (2011) survey, finding that the cost of integration of a target can refrain the bidder from completing a transaction. As cross-border M&A are more complex by nature compared to domestic M&A, I predict that adding another layer of frictions in the form of CSR strategy will decrease the likelihood of acquisitions across all metrics. Regarding domestic transactions, I expect the same signs as for the full sample of transactions. I predict that the absence of frictions imposed by crossing borders makes it easier to adhere to a strategy.
V
RESULTS
In this section, I present my main results. I show which techniques I use to strengthen the robustness of my results and what the results imply in the bigger scheme of this study and the existing literature.15 The regression results of the main sample are presented in Table VI. Table VII and VIII present the regression results of the cross-border model and the domestic sample respectively.
The effect of ESG scores on the dependent variable is negative, but statistically insignificant. I predicted a positive relationship between ESG scores and the likelihood of being an acquirer, based upon the beforementioned literature, but my results do not support any statistically significant effect. The ESG score has a positive effect on cross-border acquisitions, which is not in line with my expectations. Although the observed effect is statistically not significant, it is interesting to note that the estimated sign is positive while the sign is negative for the full sample and domestic sample. This proposes that ESG might affect
14 This assumption is embedded in the construction of the Governance pillar, as CSR strategy is one of
the pillar’s three main categories.
cross-border acquisitions differently, although I cannot base any conclusions from this finding because there is no statistical support. However, I estimated the same model excluding country fixed-effects and yield statistically significant results pointing in the same direction.16 Regarding the domestic M&A model, ESG negatively influences the likelihood of national acquisitions. This direction is in line with the base model and my predictions. Statistically, this result is not significantly different from zero.
Regarding the relationship between Environment and a firm’s M&A activity, my results support my expectations. I find a statistically significant negative effect, implying that environmental measures such as the usage of resources or innovation of products decrease the likelihood of a firm announcing a takeover. One standard deviation increase in this category decreases the acquisition likelihood by 8.1 percentage points. The magnitude of this effect is even greater when measuring the pillars jointly, as a standard deviation increase reduces M&A propensity by 11.5 percentage points. These findings support one of the main outcomes of the aforementioned PwC survey (PwC, 2012). The survey reports that poor performance on ESG factors can affect the likelihood of a deal occurring. As many companies consider environmental issues in their due diligence and that the ease of integration of the target company can be a key factor in the acquirer’s willingness to do a deal, it seems that companies performing well in the Environment category will more carefully choose their target companies based upon the perceived ease of bringing the target up to its standards.17 Mergermarket (2019) reports that all surveyed parties view business risks as a reason why they consider ESG factors when making investment or M&A decisions. This again seems to align with the performed due diligence on environmental factors. Identifying risks regarding emissions and resource use are forward-looking measures and companies are not willing to take any risks regarding these environmental issues.
Considering only national takeovers, the impact of Environment on acquisition likelihood is even greater. One standard deviation increase in this metric reduces the likelihood of a firm pursuing M&A by 18.5 percentage points and this result is statistically significant. It is difficult to identify why this is exactly the case and I think further research on the dynamics between environmental measures and acquisition propensity is needed to clarify this. Regarding the cross-border sample, the obtained coefficient for Environment is small and statistically not different from zero.
Governance has a negative effect on a firm’s propensity to engage in M&A as well. My results report a statistically significant coefficient of -0.0032, implying that one standard
16 Estimating the cross-border model without country fixed-effects yields a coefficient of 0.00615 for
ESG at the 10% significance level. This estimation is included in Table B5 of the Appendix.
17 A PwC study on the integration of ESG measures reports that 63% of companies always consider
deviation increase in the Governance metric decreases acquisition likelihood of a firm by 6.9 percentage points. This relationship is in line with my predictions and is supported by the existing literature. As mentioned in chapter IV, sound governance mechanisms reduce the possibility of agency problems (Malmendier and Tate, 2008; Grinstein and Hribar, 2004). Also, more competent management reduces the risks of ‘empire building’, which can result in excessive investments that destroy shareholder value (Hope and Thomas, 2008). Regarding the joint test, I find that one standard deviation increase of Governance decreases the firm’s likelihood to pursue M&A by 9.4 percentage points. This result is statistically significant as well, strengthening the claim of the aforementioned proposed reduction of agency problems. Governance returns statistically insignificant results in Table VII and VIII. Thus, while the Governance metric yields negative coefficients for all three models, only the base model reports a statistically significant effect of Governance on acquisition likelihood. PwC (2012) reports that Governance is the least considered ESG factor during due diligence. Hence, companies may attach lower weight to this metric as other CSR components impose larger risks to them. Also, Governance receives the lowest weight of the pillars in ESG calculations, maybe inducing companies to devote less attention to this pillar.
The results of the Social category in Table VI show the predicted sign, but are statistically insignificant. Regarding the joint test, the same expected positive relationship between the Social category and the propensity to pursue M&A is found, but here it is statistically significant. One standard deviation increase of Social increases acquisition likelihood by 13.6 percentage points. The cross-border model returns statistically insignificant estimates for the Social metric. However, the domestic model does yield some interesting results. The Social category returns a statistically significant coefficient of 0.0050. This implies that one standard deviation increase in this metric increases the estimated likelihood of M&A by 10.3 percentage points. The results from the joint test are interesting as well. The Social coefficient is of greater economic significance than when I estimated its influence in the absence of the other metrics. Social returns a coefficient of 0.010 at the 1% significance level. This result implies that for each standard deviation increase ceterus paribus, acquisition likelihood increases by 20.5 percentage points. Hence, the Social metric yields positive signs in all three models.
Table VI
Logistic regression estimation of the relationship between CSR and acquisition likelihood for the full sample of acquiring firms
This table displays a logistic regression estimation on the relationship between CSR practices and the likelihood of M&A pursuance. The sample includes all 3,658 transactions. The dependent variable equals one if firm i announced a transaction in year t. All five columns are logit regressions to estimate the likelihood of a firm pursuing M&A. The coefficients of the CSR variables are multiplied by 100 for clarity purposes. The t-statistics are presented in parentheses and are based upon robust standard errors adjusted for heteroskedasticity. Pseudo R2 represents McFadden’s R2. Fixed-effects include
year, country and industry fixed-effects. Appendix C provides definitions of all used variables. *, **, *** indicate statistical significance at the 10%, 5% and 1% level respectively.
(1) (2) (3) (4) (5)
ESG Environment Governance Social Pillars
ESG -0.119 (-0.53) Environment -0.350* -0.498* (-2.14) (-2.56) Governance -0.322* -0.435* (-2.06) (-2.51) Social 0.267 0.661** (1.50) (2.90) ROE 0.374* 0.192 0.272 0.272 0.177 (2.52) (1.26) (1.76) (1.76) (1.11) D/E 0.026 0.001 0.036 0.036 0.009 (1.10) (0.05) (1.37) (1.36) (0.32) MTB -0.012 0.001 -0.009 -0.009 -0.001 (-0.65) (0.05) (-0.46) (-0.45) (-0.05) Market cap 0.200*** 0.208*** 0.214*** 0.198*** 0.193*** (6.17) (6.58) (6.83) (6.27) (5.80) Employees 0.062* 0.079** 0.076** 0.069* 0.089** (2.41) (3.01) (2.85) (2.55) (3.24) Intercept -6.418*** -6.537*** -7.740*** -7.656*** -7.262*** (-4.01) (-6.39) (-7.17) (-6.88) (-7.15)
Fixed-effects Yes Yes Yes Yes Yes
Table VII
Logistic regression estimation of the relationship between CSR and acquisition likelihood for the cross-border sample of acquiring firms
This table displays a logistic regression estimation on the relationship between CSR practices and the likelihood of cross-border M&A pursuance. The sample includes 1,395 transactions. The dependent variable equals one if firm i announced a cross-border transaction in year t. All five columns are logit regressions to estimate the likelihood of a firm pursuing cross-border M&A. The coefficients of the CSR variables are multiplied by 100 for clarity purposes. The t-statistics are presented in parentheses and are based upon robust standard errors adjusted for heteroskedasticity. Pseudo R2 represents
McFadden’s R2. Fixed-effects include year, country and industry fixed-effects. Appendix C provides
definitions of all used variables. *, **, *** indicate statistical significance at the 10%, 5% and 1% level respectively.
(1) (2) (3) (4) (5)
ESG Environment Governance Social Pillars
ESG 0.371 (1.17) Environment 0.053 0.070 (0.22) (0.24) Governance -0.208 -0.428 (-0.91) (-1.67) Social 0.091 0.262 (0.37) (0.80) ROE 0.591* 0.288 0.470 0.470 0.288 (2.57) (1.22) (1.89) (1.89) (1.10) D/E 0.024 -0.041 0.023 0.023 -0.063 (0.67) (-1.01) (0.52) (0.52) (-1.38) MTB 0.022 0.039 0.021 0.021 0.042 (0.82) (1.34) (0.72) (0.72) (1.42) Market cap 0.220*** 0.226*** 0.250*** 0.244*** 0.203*** (5.13) (5.16) (5.66) (5.48) (4.39) Employees 0.106** 0.150*** 0.132** 0.128** 0.169*** (2.67) (3.75) (3.07) (2.98) (3.89) Intercept -8.594*** -9.587*** -7.886*** -7.886*** -7.299*** (-4.64) (-5.93) (-6.86) (-6.87) (-6.60)
Fixed-effects Yes Yes Yes Yes Yes
Table VIII
Logistic regression estimation of the relationship between CSR and acquisition likelihood for the domestic sample of acquiring firms
This table displays a logistic regression estimation on the relationship between CSR practices and the likelihood of domestic M&A pursuance. The sample includes 2,263 transactions. The dependent variable equals one if firm i announced a domestic transaction in year t. All five columns are logit regressions to estimate the likelihood of a firm pursuing domestic M&A. The coefficients of the CSR variables are multiplied by 100 for clarity purposes. The t-statistics are presented in parentheses and are based upon robust standard errors adjusted for heteroskedasticity. Pseudo R2 represents
McFadden’s R2. Fixed-effects include year, country and industry fixed-effects. Appendix C provides
definitions of all used variables. *, **, *** indicate statistical significance at the 10%, 5% and 1% level respectively.
(1) (2) (3) (4) (5)
ESG Environment Governance Social Pillars ESG -0.415 (-1.49) Environment -0.463* -0.801** (-2.26) (-3.21) Governance -0.269 -0.339 (-1.40) (-1.59) Social 0.502* 0.995*** (2.24) (3.47) ROE 0.152 0.077 0.096 0.103 0.077 (0.86) (0.42) (0.53) (0.57) (0.41) D/E 0.028 0.028 0.042 0.042 0.046 (0.98) (0.90) (1.39) (1.39) (1.45) MTB -0.040 -0.027 -0.027 -0.027 -0.029 (-1.74) (-1.12) (-1.17) (-1.15) (-1.18) Market cap 0.178*** 0.186*** 0.180*** 0.152*** 0.178*** (4.28) (4.58) (4.48) (3.72) (4.13) Employees 0.031 0.031 0.040 0.029 0.040 (1.02) (1.00) (1.27) (0.92) (1.24) Intercept -4.213*** -6.014*** -6.086*** -5.859*** -5.732*** (-5.26) (-5.38) (-5.17) (-4.82) (-4.85)
Fixed-effects Yes Yes Yes Yes Yes
Weisbach, 2012). Jo and Harjoto (2011) find that greater social enhancement within the firm increases firm value more than other categories of CSR do. As I find solely positive coefficients, my results indicate that strong Social performance enhances the likelihood of pursuing M&A. As mentioned in chapter IV, a possible explanation may be that a strongly motivated workforce accompanied by good working conditions and firm performance reduces the possible resistance of shareholders objecting to future acquisitions.
Although estimations are not statistically significant for all metrics and subsamples, the results seem to show a downward effect of CSR on M&A propensity. This raises the question whether, in an opposite relation, higher volumes of M&A may decrease CSR levels. While to some extent mergers and acquisitions can be disruptive, I think the results from Mergermarket (2019) and PwC (2012) mitigate this concern. Both surveys basically state that ESG is becoming increasingly important in M&A activities and that acquiring parties do not want to adopt business or reputational risks by acquiring another company.18
In order to address potential endogeneity issues, I estimate two-staged least squares instrumental variables regressions. In the first stage, I use OLS to estimate the predicted values of the CSR variables and introduce two instrumental variables in order to do so. Cheng, Ioannou and Serafeim (2014) show that CSR is influenced by a time-invariant component at the country level. In other words, a firm’s CSR is influenced by CSR performance of others firms within the same country or industry. Following their rationale, I computed country-year and country-industry means of the CSR metrics. As the F-statistic of the instruments is high, I conclude that both instruments are relevant. As my dependent variable is binary, my second stage regression is non-linear. I followed Newey (1987) and used the predicted values of CSR in the second stage by conducting a logistic regression.
The results are presented in Table IX and show that my predicted values for ESG, Environment and Social all have a negative, statistically significant, effect on acquisition likelihood. The effect of Governance is positive and statistically insignificant. The first two outcomes support my results of Table VI and suggest that ESG and Environment are not affected by endogeneity problems. However, Social and Governance both yield unexpected signs that raise the issue of an endogeneity bias in the model. As truly identifying a causal relationship is beyond the scope of this research, I think assessing possible endogeneity problems and treating found results with caution still provides for an understanding of the general dynamics at hand.
Table IX
Instrumental variables estimations using linear estimation in the first stage and logistic regression in the second stage
This table displays instrumental variables estimations. In the first-stage regressions, I regress the CSR variables using OLS on two instruments, which are the country-year and country-industry means of the CSR scores. The dependent variables are ESG in column (1), Environment in column (3), Social in column (5) and Governance in column (7). The control variables are the same as those used in Tables V, VI and VII. In the second-stage regression, I used logistic regression with a binary dependent variable equal to one if firm i announced a transaction in year t and zero otherwise. CSR_pr is the predicted value of ESG in column (2), Environment in column (4), Social in column (6) and Governance in column (8). The sample consists of 3,658 transactions initiated between January 2003 and December 2018. The t-statistics are presented in parentheses in the odd columns and z-statistics are presented in parentheses in the even columns. These statistics are based upon bootstrapped standard errors. Appendix B provides definitions of all used variables. *, **, *** indicate statistical significance at the 10%, 5% and 1% level respectively.
CSR Environment Social Governance
First stage Second stage First stage Second stage First stage Second stage First stage Second stage
(1) (2) (3) (4) (5) (6) (7) (8) CSR_pr -0.0166*** (-4.13) -0.0144*** (7.77) -0.0165*** (-5.40) 0.0034 (-0.77) Country-year 0.3322*** (13.23) 0.3845*** (13.02) 0.4914*** (17.42) 0.7349*** (11.16) Country-industry 0.5869*** (25.15) 0.7607*** (26.84) 0.7574*** (28.29) 0.8399*** (23.25) Intercept -70.2895*** (56.41) -6.0049*** (31.04) -61.2337*** (37.11) -5.7538*** (-37.66) -66.4904*** (-37.09) -5.6194*** (-37.10) -62.2494*** (-16.89) -5.5592*** (-31.71)
Controls Yes Yes Yes Yes Yes Yes Yes Yes
VI
CONCLUSION
This section summarizes my findings in this paper and answers my research question. Furthermore, I describe what my results mean in the context of the existing literature and I shed light on potential limitations of my study. Finally, I provide suggestions for further research on this topic.
My paper seeks to add to the limited research done on the relationship between CSR and M&A. This study empirically examined the effect of CSR on a firm’s likelihood to pursue M&A. I used a sample of 3,658 deals conducted between January 2003 and December 2018 by an international sample of firms. Furthermore, I split my research into investigating domestic and cross-border transactions. In order to answer my research question, I broke it down into multiple hypotheses.
I find that stronger performance in the Environment and Governance categories decrease the acquisition likelihood of a firm. Most likely, firms that perform well environmentally have made their necessary efforts to do so in the first place, by reducing emissions for example, and do not want to suffer risks by acquiring companies that are not up to its environmental standards. Enhancing environmental performance can be a time-consuming endeavor, hence this may deter potential bidders from acquiring companies if they believe integrating these companies will be too costly. Regarding Governance, I propose that sounder governance mechanisms decrease acquisition likelihood because there is less room for agency problems that stimulate empire building or excessive investments. I also find positive signs in the Social category that indicate a positive impact on M&A likelihood. The results might indicate that better policies in terms of working conditions result in a more dedicated workforce that trusts the capabilities of their managers. I think internal announcements and discussions on M&A negotiations receive less resistance as a result, thus increasing the possibility of a future deal.
CSR performance seems to play a stronger role in domestic transactions than in cross-border M&A. While the domestic sample returns larger coefficients than the base model, the results of the cross-border sample are all statistically insignificant. Based upon the statistically insignificant results of the cross-border model, I think that the legal and institutional differences between countries dilute the dynamics between CSR and M&A observed in the other models.
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APPENDIX A – Description of Refinitiv CSR metrics
Environment
Resource use: The resource use score reflects a company’s performance and capacity to reduce the use of materials, energy or water, and to find more eco-efficient solutions by improving supply chain management.
Emission reduction: The emission reduction score measures a company’s commitment and effectiveness towards reducing environmental emissions in its production and operational processes.
Innovation: The innovation score reflects a company’s capacity to reduce the environmental costs and burdens for its customers, thereby creating new market opportunities through new environmental technologies and processes or eco-designed products.
Social
Community: The community score measures the company’s commitment to being a good citizen, protecting public health and respecting business ethics.
Human rights: The human rights score measures a company’s effectiveness in terms of respecting fundamental human rights conventions.
Product responsibility: The product responsibility score reflects a company’s capacity to produce quality goods and services, integrating the customer’s health and safety, integrity and data privacy.
Workforce: The workforce score measures a company’s effectiveness in terms of providing job satisfaction, a healthy and safe workplace, maintaining diversity and equal opportunities and development opportunities for its workforce.
Governance
CSR strategy: The CSR strategy score reflects a company’s practices to communicate that it integrates economic (financial), social and environmental dimensions into its day-to-day decision-making processes.
Management: The management score measures a company’s commitment and effectiveness towards following best practice corporate governance principles.
APPENDIX B – Tables and figures
Table B1
Correlation matrix of sample variables
This table displays correlation coefficients between the variables of the sample. ESG represents the ESG score. Env is the Environment variable. Gov represents the Government variable. Soc is the Social variable. ROE represents return on equity. D/E represents the debt-to-equity ratio. MTB is the market-to-book ratio. Cap represents the natural logarithm of market capitalization. Emp represents the natural logarithm of employees. The financial variables are winsorized at the 1% and 99% level. P-values are presented in parentheses.
