The Impact of ESG Scores on Firm Performance: A Comparison of the European Market Before and After the 2008 Financial Crisis

Master’s Thesis 15 credits Specialization: Financial Management

Department of Business Studies Uppsala University Spring Semester of 2021

Date of Submission: 2021-06-02

Arran Pickwick Jacob Sewelén

Supervisor: Shruti Kashyap Abstract

This study explores the impact of ESG Scores on firm performance and seeks to establish whether the impact increased since the 2008 financial crisis. This is done by performing regressions on ESG Scores, and the respective pillars of Environmental, Social, and Governance, and firm performance, measured as both accounting-based performance, using ROA, and market-based performance, using Tobin’s Q. The study adopts a quantitative approach, utilising a random-effects model to analyse panel data across two sample periods - a pre-crisis period, from 2003-2006, and a post-crisis period, from 2010-2019. In addition, t-tests were performed to see if the impact changed significantly from the pre-crisis to post-crisis period. The study analyses data from 218 firms from the STOXX Europe 600 index, with four smaller sub-samples. The results indicate that ESG Scores have a positive impact on firm performance, with market-based firm performance being significantly correlated both before and after the crisis. Accounting-based performance, however, was only significantly correlated with ESG Scores before the crisis. In addition, the Environmental pillar was positively correlated with both measures of performance before and after the crisis, and the same was true for the Social pillar, except for with post-crisis accounting-based performance. Interestingly, the Governance pillar was not significantly correlated with performance in any of the regressions. Finally, while the average ESG Score among the observed companies increased in the post-crisis period, the impact of ESG performance on firm performance did not change significantly. The results of this study are supportive of the stakeholder theory perspective over the principal-agent theory, and show that ESG performance does indeed positively impact firm performance. Future research could explore whether other events have played a significant role in the rising importance of ESG, or if the results of the present study can be replicated across different time periods and geographical regions.

Key words: ESG, CSR, firm performance, Europe, EU, 2008 financial crisis, principal-agent theory

Acknowledgments

We would like to take this opportunity to thank our seminar participants for their useful feedback each week. Most of all, we would also like to thank our supervisor, Shruti Kashyap, for providing us with truly invaluable support, insight, and guidance, from start to finish. Without this support, writing this thesis would not have been possible.

Thank you. Arran Pickwick & Jacob Sewelén

Table of Contents 1. Introduction ...... 1 1.1 Research Gap ...... 3 1.2 Research Questions ...... 5 2. Theoretical Background and Previous Literature ...... 6 2.1 Theoretical Background ...... 6 2.1.1 ESG and CSR ...... 6 2.1.2 ESG Scores ...... 7 2.1.3 Differing Perspectives of ESG ...... 7 2.1.4 The Principal-Agent Theory Perspective of ESG ...... 8 2.2 Previous Literature ...... 9 2.2.1 The Increasing Importance of ESG ...... 9 2.2.2 The Impact of ESG on Firm Performance ...... 10 2.3 Development of Hypotheses ...... 11 2.3.1 The Impact of ESG on Accounting-Based Firm Performance ...... 12 2.3.2 The Impact of ESG on Market-Based Firm Performance ...... 12 2.3.3 Growth in the Impact of ESG on Firm Performance ...... 13 2.4 Summary of Theory and Literature ...... 15 3. Methodology ...... 16 3.1 Research Design ...... 16 3.2 Operationalisation ...... 16 3.3 Data Collection and Sample ...... 18 3.4 Data Analysis ...... 20 3.4.1 Properties of the Estimators- Unbiasedness, Consistency, and Efficiency ...... 21 3.4.2 Generalised Least Squares ...... 21 3.4.3 Fixed Effects and Random Effects ...... 22 3.4.4 T-Tests ...... 23 3.4.5 Hypothesis Testing ...... 23 3.5 Reliability and Validity ...... 23 3.6 Summary of Methodology ...... 24 4. Results ...... 25 4.1 Descriptive Statistics ...... 25 4.2 Pre-Crisis Accounting-Based Firm Performance Results ...... 25 4.3 Post-Crisis Accounting-Based Firm Performance Results ...... 28 4.4 Pre-Crisis Market-Based Firm Performance Results ...... 30 4.5 Post-Crisis Market-Based Firm Performance Results ...... 33 4.6 Test of Change in Coefficients for ESG Scores and ROA ...... 35 4.7 Test of Change in Coefficients for ESG Scores and Tobin’s Q ...... 36 4.8 Test of Change in Mean ESG Scores ...... 36 4.9 Summary of the Results ...... 37 5. Analysis...... 38 5.1 ESG Scores and Accounting-Based Firm Performance ...... 38 5.2 ESG Scores and Market-Based Firm Performance ...... 40 5.3 Change in Impact of ESG Score on Firm Performance ...... 43 5.4 Limitations ...... 45 6. Conclusion ...... 47 6.1 Suggestions for Future Research ...... 48 References...... 50 Appendices ...... 58 Appendix 1 ...... 58 Appendix 2 ...... 68 Appendix 3 ...... 71 Appendix 4 ...... 87 Appendix 5 ...... 88 Appendix 6 ...... 91 Appendix 7 ...... 94 Appendix 8 ...... 98 Appendix 9 ...... 102 Appendix 10 ...... 103

The Impact of ESG Scores on Firm Performance: A Comparison of the European Market Before and After the 2008 Financial Crisis

“Destruction leads to a very rough road, but it also breeds creation” - excerpt from the lyrics of Californication by Red Hot Chili Peppers, released in 1999.

1. Introduction The American author Laura Ingalls Wilder (1935, p.320) once famously wrote “There is no great loss without a small gain”. Indeed, the financial crisis of 2008 certainly brought the world to a rougher road as it wreaked havoc on the global economy. When the dust had settled, billions of dollars had been lost, and governments around the world incurred huge costs while battling the effects of the crisis. However, the cause of the crisis remains hotly debated (Sun et al, 2011, p.4). The OECD argues that weak corporate governance systems, including executive remuneration and risk management, contributed to the financial crisis (Sun et al, 2011, p.5). Regardless, firms have been more proactive in their ESG (environmental, social, and governance) engagements since the crisis (Sampei, 2018). It is therefore interesting to study whether ESG gained more traction after the 2008 financial crisis, as the impact that firms have is increasingly examined from an ethical standpoint (Sampei, 2018). This study will thus focus on the impact of ESG on firm performance, and whether this impact has increased since the 2008 financial crisis.

The starting point of the crisis varies by definition, but the bankruptcy of Lehman Brothers on 15 September 2008 marks the date when financial markets began to deteriorate significantly (Elliot, 2011; The Swedish Central Bank, 2021). Up to that point, banks had been considered too big to fail (Elliot, 2011). The near collapse of the banking sector highlighted several issues in the corporate governance systems prevailing at the time (Collin, 2009). On 29 September 2008 alone the total value of the US stock market fell by $1.2 trillion (Bajaj & Grynbaum, 2008). It was not long before Europe was also impacted by the crisis. Over the following three months, the STOXX Europe 600 index fell from 266 to 198 index points during 2008 alone, a decline of around 25.6% (Qontigo, 2021).

The destructive events of the Global Financial Crisis of 2008 bred creation in the way investors think of investment opportunities. ESG has emerged as a helpful tool for investment decisions post-crisis, across the globe (Sampei, 2018). The attention to ESG related issues has

1 increased among investors, corporate executives (Sampei, 2018), and regulators alike (Rowbottom & Locke, 2016). This can largely be attributed to the ethical implications of the consequences of the financial crisis, which drew more attention to the wider impact that the actions of businesses have on society (Sampei, 2018). In the European Union, the European Commission has released multiple regulations aiming to improve sustainability practices and increase transparency amongst businesses operating in Europe (KPMG, 2020). As of 2020, the European Commission has begun initiatives to curb the substantiation of firms’ environmental impact (European Commission, 2021b), in addition to proposing the standardisation of ESG reporting (European Commission, 2021a), reflecting the pervasiveness of the growth of ESG regulation in Europe.

On the corporate side, there has been a growth in ethical banking in Europe since the financial crisis (Valls Martinez et al, 2020), born from the desire for the financial services sector to be more sustainable in its practices post-crisis (Valls Martinez et al, 2020). On the investing side, in Europe, equity ESG funds in the UCITS market have grown by more than double that of non-ESG UCITS funds in Europe over the last five years (EFAMA, 2021), showing the increasing importance placed on ESG considerations by investors in the European market. As a consequence, Europe becomes a natural area of interest to assess whether corporate engagements in ESG pay off in terms of firm performance.

This thesis starts with Section 1, where the research gap and the research questions are introduced. Here the area of interest is uncovered- the relationship between ESG and firm performance, and the growth in importance of ESG since the financial crisis. This is followed by Section 2, which details the theoretical background, including a literature review to present prevalent schools of thought and recent findings relating to ESG and firm performance. The hypotheses are also introduced in Section 2. Section 3, the methodology section, presents the research design as well as the methods that were employed to answer the research questions. In Section 4, the results section, we present the results from the regressions and t-tests. This is followed by Section 5, the analysis section, where the results are discussed based on previous literature and theory, which is followed by a summary of the limitations of the study. The paper concludes with some final remarks in Section 6, including a summary of interesting findings, and suggestions for future research.

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1.1 Research Gap Previous studies have found mixed results regarding the impact of ESG on market-based and accounting-based measures of firm performance (Orlitzky, Schmidt, & Rynes, 2003). One study conducted in Germany found that ESG performance had a positive impact on return on assets (ROA), an accounting-based performance measure, but had no significant impact on Tobin’s Q, a market-based performance measure (Velte, 2017), whilst ESG has been found to be positively correlated with market-based performance in others studies, measured as both market value of equity (Ahmad, Mobarek, & Roni, 2021) and Tobin’s Q (Ferell, Hao, & Renneboog, 2016).

Furthermore, whilst there has been a clear growth in the importance of ESG over the 21st century (Sampei, 2018), there has been a lack of evidence as to the extent to which the financial crisis was a key turning point in the development of ESG importance. By studying the impact of ESG on the perception of firm performance during two periods, one before the 2008 financial crisis, and one after, this study aims to inform on specifically how this impact has changed since the crisis. In addition, this study aims to provide more insight regarding the inconclusive results of previous literature that has explored the impact of ESG performance on firm performance (Ahmad, Mobarek, & Roni, 2021; Orlitzky, Schmidt, & Rynes, 2003; Velte, 2017; Wang, Dou, & Jia, 2015). Meta-analyses have found varying results regarding the relationship, based on the measure of firm performance utilised (Orlitzky, Schmidt, & Rynes, 2003; Wang, Dou, & Jia, 2015). Thus, it is beneficial to utilise both accounting-based and market-based firm performance measures. In the context of this study, we define firm performance based on the traditional perspective, that is, the firm’s ability to generate wealth for its shareholders (Belghitar, Clark, & Kassimatis, 2019).

ESG Scores, scores that are attributed to companies by external bodies in order to provide a measurement of the firm in question’s ESG performance (Drempetic, Klein, & Zwergel, 2019), have become increasingly prominent since the financial crisis (Kjellberg, Pradhan, & Kur, 2019). A previous study by Ahmad et al (2021) focussed on the impact that ESG scores had on the market-based firm performance of UK firms from 2002-2019. The present study differentiates from the study by Ahmad et al (2021) in three key aspects. Firstly, the present study is concerned with the impact that ESG scores have on firm performance from both an accounting and market-based perspective. Therefore, the present study will measure the impact on ROA, an accounting-based performance measure, and Tobin’s Q, a market-based

3 performance measure. Secondly, the present study is focused on the European market as a whole, rather than just the UK market. Thirdly, the present study will separate the time period used in the Ahmad et al (2021) study into two distinct periods, 2003-2006, and 2010-2019, omitting the years 2007-2009. The years 2007-2009 are omitted, as these are the three years in which the effects of the financial crisis were most pronounced (Eurostat, 2021). The justification for this argument rests on the fact that 2010 marked the year where real GDP experienced positive growth in the European Union, from a growth rate of -4.3% in 2009 to a growth rate of 2.2% in 2010 (ibid).

By measuring market-based performance, our study is also theoretically grounded in principal-agent theory. Tobin’s Q is an ideal method of measuring market-based performance as it will indicate the degree to which investors view ESG as negative (an agency cost), or positive (beneficial to the principal, the shareholders), through variations in the market’s valuation of the firm, relative to that of its book value (McWilliams & Siegel, 2001). The principal-agent theory perspective of ESG is described in greater detail in the theoretical background in Section 2.1 of this paper. The choice to analyse firm performance with both accounting-based and market-based measures both before and after the financial crisis of 2008 was made because of the increasing importance of ESG since the crisis, and due to the varying results in previous literature across different measures of performance (Orlitzky, Schmidt, & Rynes, 2003).

The choice to focus on the European market was made for a number of reasons. Firstly, the US market has been more widely studied, leaving a greater research gap for Europe. Secondly, ESG scores are more widely available in Europe as a whole due to the greater number of large listed companies which have been assigned ESG scores compared to taking firms from an individual European country. In addition, due to all of the countries represented in the sample being located in the European Economic Area (EEA) for the entirety of the study period, we ascertain that the results of this study are applicable to EEA countries in general.

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1.2 Research Questions In line with previous research, the first research question seeks to determine how ESG scores have an impact on market-based and accounting-based firm performance. It is important to determine this relationship for the sample of the present study due to the conflicting results of previous literature focusing on the impact of ESG scores on firm performance. Thus, the first research question was formed:

How do ESG scores impact firm performance in European firms?

In addition, ESG performance has become increasingly important since the 2008 financial crisis (Sampei, 2018). Due to this, we ascertain that the extent to which ESG performance impacts the external perception of firm performance will increase, as investors place more weight on ESG considerations. This led to the formation of the second research question:

How has the influence of ESG scores on firm performance in Europe changed in the period after the 2008 financial crisis compared to the period before the crisis?

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2. Theoretical Background and Previous Literature Section 2 contains three main sub-sections. The first is Section 2.1, which outlines the prevailing theoretical background relevant to the thesis. Section 2.2 contains a review of previous relevant literature, whilst Section 2.3 introduces the hypotheses.

2.1 Theoretical Background This section details the theoretical background behind the study. Firstly, the section defines the concept of ESG and the closely related concept of CSR. Secondly, the principal-agent theory perspective of ESG will be discussed, which is later used to generate the hypotheses, which will be presented in Section 2.3. After this, additional theoretical perspectives regarding the utility of ESG-enhancing activities will be discussed.

2.1.1 ESG and CSR The definition of corporate social responsibility (CSR) and ESG may vary both across the world and across different sectors. The European Commission (2011) defines CSR as “the responsibility of enterprises for their impacts on society”. This definition suggests that firms should be held accountable for any potential damage they may cause to society. However, it does not offer any specific guidance on specifically what to expect from firms. The United Nations (2021) takes the definition of CSR a step further and defines it as “a management concept whereby companies integrate social and environmental concerns in their business operations and interactions with their stakeholders”. The latter definition stipulates that a firm wishing to integrate CSR into its strategy should consider CSR across all aspects of its operations. In the context of CSR, ESG can be seen as a way for investors to measure a firm's CSR performance, by measuring the firm’s impact across three pillars: Environmental, Social, and Governance (Capelle-Blancard & Petit, 2017, p.1). Thus, ESG can be seen as a more refined and quantifiable measure of the somewhat broader concept of CSR.

On the other hand, Gerard (2019) states that the difference between the two related concepts is that CSR is concerned with the environmental and social conduct of the firm, whereas ESG also considers the governance of the firm. According to Inderst and Stewart (2018), environmental issues include pollution, carbon emissions, and climate change, and the environmental pillar to hold firms accountable for the environmental damage that is caused by firms’ activities. Similarly, the Social pillar includes diversity, human rights, and labour conditions (Inderst & Stewart, 2018). The third pillar refers to Corporate Governance

6 performance, or simply Governance (ibid). Inderst and Stewart (2018) identify corruption, transparency, and corporate governance as some of the issues related to Governance. For consistency, and due to the conceptual proximity of ESG and CSR, the term ESG will be used exclusively for the remainder of this paper, except for where a distinction is necessary.

2.1.2 ESG Scores Multiple previous studies have utilised ESG scores as a measure of a firm’s ESG performance (Wang, Dou, & Jia, 2015; Galbreath, 2013; Gerard, 2019; Ahmad, Mobarek, & Roni, 2021). ESG scores have been continually developed across the years following the financial crisis, with increasing amounts of data and research being compiled to assess companies on their ESG performance (Kjellberg, Pradhan, & Kur, 2019). ESG scores are a measure used to quantify the overall performance of a firm’s activities relative to the pillars of ESG (Environmental, Social, and Governance) (Duque-Grisales & Aguilera-Caracuel 2021). The scores are calculated based on performance in a variety of areas subordinate to each of the three pillars (ibid). In the Refinitiv ESG scoring framework, for example, the Environmental pillar is made up of such categories as emissions, whilst an example of a category for the Social pillar is human rights (Refintiv, 2021).

2.1.3 Differing Perspectives of ESG The value of ESG is supported by the stakeholder theory perspective. According to stakeholder theory, ESG leads to shareholder value maximization (McWilliams & Siegel, 2001). Stakeholder theory is rooted in the notion that firms maintain reciprocal relationships with stakeholders, and that they will ideally be mutually supportive (ibid). From this perspective, ESG-enhancing activities are value-creating. These positive benefits of ESG lead to the firm benefitting from brand equity and stakeholder loyalty, which have been found to be higher amongst high ESG performing firms (Du & Yu, 2020). Furthermore, stakeholder theory is rooted in the notion of ESG being used to develop relationships of trust with stakeholders, where ethical behaviour leads to positive long-term impact (McWilliams & Siegel, 2001). An important consideration regarding ESG in general is the notion that its benefit is more pronounced from a long-term perspective, rather than for short-term financial gain (Lamb & Butler, 2018). In contrast with the stakeholder theory perspective, slack resource theory posits that whilst many studies have found a positive relationship between firm performance and ESG Scores, this may ultimately be due to resource slack (Wang, Dou,

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& Jia, 2015). That is, firms invest in ESG enhancing activities due to having excess resources at their disposal (ibid).

The principal-agent theory perspective of ESG, however, posits that ESG has a negative impact on shareholder value maximization. From this perspective, ESG activities are value- destroying for shareholders, and represent managers deviating from their original task- the maximization of shareholder wealth (Ferell, Hao, & Renneboog, 2016). According to the principal-agent theory perspective, managers are obliged to focus only on shareholder value maximisation (ibid), meaning their responsibilities are limited specifically to financial goals. Due to the prominence of the principal-agent theory perspective in previous literature (Ferell, Hao, & Renneboog, 2016; Friedman, 1970; McWilliams & Siegel, 2001; Suchman, 1995), the present study will utilise this perspective as the core of the theoretical background.

2.1.4 The Principal-Agent Theory Perspective of ESG According to principal-agent theory, individuals often act primarily in their own interests in a variety of contexts, with humans often opting to make decisions that benefit their own interests over those of others (Ross, 1973). In principal-agent theory, two key roles are identified- the agent and the principal (ibid). The agent is assigned responsibility for making decisions that will benefit the interest of the principal (ibid). In a corporate context, the role of agent is assigned to a manager, whilst the role of the principal is assigned to the shareholder (Ferell, Hao, & Renneboog, 2016). Thus, it is the responsibility of the manager to act in the interest of the shareholder.

The principal-agent theory perspective views ESG as a deviation from the maximisation of shareholder value (Ferell, Hao, & Renneboog, 2016). In this context, the manager’s decision to engage in ESG-enhancing initiatives is attributed to a pursuit of their own socioemotional wealth- that is, the individual manager is deviating from the role assigned to them by the principal (shareholders) for their own gain (Friedman, 1970; Ferell, Hao, & Renneboog, 2016). Furthermore, Friedman (1970) ascertains that managers may pursue ESG-enhancing initiatives for their own agendas, such as for personal career progression (McWilliams & Siegel, 2001), or socio-political standing. In this case, the manager is misallocating the firm’s resources with an intention that deviates from maximising firm performance (Friedman, 1970). In this regard, from the principal-agent theory perspective, ESG is seen as value- destroying.

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The principal-agent theory perspective on ESG draws parallels with the moral legitimacy subset of legitimacy theory, in that the focus is shifted from shareholder value and performance maximisation (Suchman, 1995). The principal-agent theory perspective, however, specifically focuses on the decisions made by management regarding moral legitimacy and the socioemotional benefits managers can personally gain from engaging in ESG (Ferell, Hao, & Renneboog, 2016). This notion of managerial legitimacy-seeking as an agency problem leading to ESG-enhancing initiatives is supported by the results of one study, which found that managers were most likely to engage in ESG-enhancing activities at times when they had a greater need for legitimacy (Li & Lu, 2020).

Another important element of the principal-agent theory is the notion that there exists information asymmetry between the principal and the agent, whereby the agent has some informational advantage over the principal (Ross, 1973). It is this information asymmetry that enables the agent to act in their own interest, against the interests of the principal. In a corporate governance context and regarding ESG, information asymmetry may facilitate managers’ decisions to engage in ESG-enhancing activities that are not value maximising to shareholders (Ferell, Hao, & Renneboog, 2016).

2.2 Previous Literature This section highlights the relevant previous literature regarding the issues identified in the two research questions. Firstly, section 2.2.1 reviews the relevant literature regarding the increasing importance of ESG over time, and in particular since the financial crisis of 2008. Secondly, section 2.2.2 describes and discusses previous studies that have explored the impact of ESG on firm performance.

2.2.1 The Increasing Importance of ESG The development of ESG has been solidified by new ESG focussed policies which were introduced in the aftermath of the 2008 financial crisis, such as the commitment of governments to the UN’s Sustainable Development Goals (KPMG, 2020; Lunn, 2019). The European Commission has also issued a plethora of ESG-related regulations (KPMG, 2020). In 2020, the European Commission released the Action Plan for Financing Sustainable Growth, specifically focussing on improving sustainability-related issues in the finance sector by clarifying and improving transparency regarding sustainable investing for both asset

9 managers and investors (KPMG, 2020). The fallout from the 2008 financial crisis and the desire for firms to be held accountable for their wider impact contributed to the development of integrated reporting, whereby previously separate ESG and sustainability considerations have come to be reported alongside financial reports (Moon & Herzig, 2012; Rowbottom & Locke, 2016). Integrated reporting has been evident since 2002, however it was not until the years following the 2008 financial crisis that the concept began to be solidified (Rowbottom & Locke, 2016). This ultimately culminated in the International Integrated Reporting Council (IIRC) releasing a standardisation framework in 2013 (ibid).

ESG as a concept has grown substantially in importance and prevalence since the beginning of the 21st century (Leins, 2020). This growth however has become even more pronounced, and developed at a larger rate, since the 2008 financial crisis (ibid). Due to the close connection between CSR, which focuses on firm actions, and ESG, which seeks to measure firm performance in regard to these issues (Ahmad, Mobarek, & Roni, 2021), it is intuitive that growth in the importance of CSR activities on a macroscale is cognate with growth in importance of measures of ESG performance. With the strong stance that the European Union has taken on regulating and promoting ESG related issues (European Commission, 2021a), ESG investing has continued to grow at a fast rate in the European Union, further highlighting the increasing importance of ESG in the region (EFAMA, 2021).

2.2.2 The Impact of ESG on Firm Performance Previous literature has studied the effects of ESG scores in different settings. Ahmad et al. (2021) found that ESG has a positive effect on firm performance. However, they measured performance as the market value of equity and earnings per share (ibid). In addition, they did not find conclusive results on individual ESG dimensions’ effect on firm performance (ibid). Measured on an individual basis (Environmental, Social, and Governance), only the social dimension had a significant impact on firm performance in their study (ibid). However, in the case of earnings per share, all individual dimensions had a significant impact (ibid). Han et al. (2016) conducted a study of the Korean market and found that only Governance had a statistically significant impact on firm performance, measured both by Market-To-Book and stock returns. Lo & Sheu (2007) studied the US market between 1999 and 2002 and found that corporate sustainability and Tobin’s Q were positively correlated, suggesting that sustainable practices are valued in financial markets. Finally, a study conducted in Germany

10 found that the aggregate ESG performance had no impact on firm performance, measured as Tobin’s Q (Velte, 2017).

Lööf & Stephan (2019) investigated the relationship between ESG and downside risk in five European countries between 2005 and 2017. The authors were not able to find statistical support that a high ESG performance has any impact on risk-adjusted return (ibid). They did, however, find results that supported the notion that high ESG performance reduces the downside risks in stock returns (ibid). The conclusion from their research is that ESG performance affects stock returns by reducing potential losses or declines in stock prices (ibid).

A closely related research enquiry was conducted by Eccles et al. (2014) on the US market where they measured firms’ propensity to adopt sustainability policies and firm performance. Firms that voluntarily adopted sustainability policies were considered to be high sustainability companies and were tested to see whether or not they outperform low sustainability companies (ibid). The general findings were that high sustainability companies outperform other companies in the long term, both measured on an accounting-based performance level, as well as a market-based performance level (ibid).

The market-based performance results from Eccles et al (2014) are in alignment with stakeholder theory. In the long-term, ESG-enhancing activities have a positive impact on firm performance, as the firm is perceived more positively by investors in the market. This also draws comparison to legitimacy theory, which explains organizational actions, and the underlying motives behind legitimacy-seeking behaviour (Suchman, 1995). Suchman (1995) identifies a specific subset of legitimacy theory, pragmatic legitimacy, whereby a firm engages in legitimacy-seeking behaviour that has a utility benefit to the firm, so as to maximise the wealth of shareholders. In the context of ESG, firms engage in ESG practices in order to legitimise the firm to stakeholders in the market, which will thus have a positive impact on how the firm is perceived (ibid).

2.3 Development of Hypotheses In this section, the hypotheses are introduced, and the background of their development is explained. Section 2.3.1 introduces hypotheses 1 and 2, which are focused on the impact of ESG on accounting-based firm performance, whilst Section 2.3.2 introduces hypotheses 3

11 and 4, which are about the impact of ESG on market-based firm performance. Section 2.3.3 introduces hypotheses 5 and 6, which are focused on the change in the impact of ESG on both measures of firm performance from the period before the 2008 financial crisis to the period after it.

2.3.1 The Impact of ESG on Accounting-Based Firm Performance Ahmad et al (2021) studied the effect ESG scores had on firms’ performance in the UK. More specifically, they studied the FTSE 350 companies and concluded that ESG scores had a positive impact on firm performance, measured as earnings-per-share (an accounting-based measure) (Ahmad et al, 2021). Their conclusions confirmed the notion that seeking legitimacy through ESG-enhancing activities has a positive impact on accounting-based performance (ibid). Furthermore, the 2003 meta-analysis by Orlitzky and his colleagues found that accounting-based measures of firm performance were more significantly correlated with ESG performance than market-based firm performance. This led to the formation of our next set of hypotheses, whereby we measure the impact of ESG scores on firm performance, measuring firm performance through ROA, an accounting-based measure:

H1: ESG Scores had a statistically significant impact on ROA from 2003 to 2006

H2: ESG Scores had a statistically significant impact on ROA from 2010 to 2019

These hypotheses are grounded in the principal-agent theory. Should ESG have a positive impact on performance, this would indicate ESG is a performance-enhancing activity, rather than an agency cost. This will thus be reflected in the firm performance of the company. Hypotheses 1 and 2 of the present study are used to answer the first research question, “how do ESG scores impact firm performance?”

2.3.2 The Impact of ESG on Market-Based Firm Performance High ESG performance improves trust, which in turn may improve the performance of the firm due to a reduction in transaction costs, since stakeholders place more trust in the firm (Ahmad, Mobarek, & Roni, 2021). This reduction in transaction costs may take the form of a lower cost of capital, due to a perception of reduced information asymmetry amongst suppliers of capital (Du & Yu, 2020). Thus, creditors may require less interest, whilst equity investors may be willing to pay a higher price for shares if they believe their respective risks

12 are lower due to higher transparency. The results of previous literature have been mixed, with Ferell and colleagues (2016) finding that CSR performance, which is a close concept to ESG performance, is positively associated with Tobin’s Q. Another study, however, did not find a statistically significant relationship between ESG performance and Tobin’s Q (Velte, 2017).

Based on the principal-agent theory perspective, we formed the next set of hypotheses, theorising that the increasing importance of ESG since the financial crisis will lead to a greater causal relationship between ESG and market-based firm performance, through the channels of improved trust and reduced information asymmetry, and a greater emphasis being placed on this by the market in the post-crisis period. This is measured by Tobin’s Q, used to measure market-based firm performance (Velte, 2017). This led to the construction of the next set of hypotheses, which also seek to answer the first research question, “how do ESG scores impact firm performance in Europe?”

H3: ESG Scores had a statistically significant impact on Tobin’s Q from 2003 to 2006

H4: ESG Scores had a statistically significant impact on Tobin’s Q from 2010 to 2019

These hypotheses are similarly grounded in principal-agent theory, in that if the market perceives ESG enhancing activities as value-creating, there will be a positive relationship between ESG scores and Tobin’s Q. A negative relationship will indicate that ESG activities are perceived as an agency cost by the market.

2.3.3 Growth in the Impact of ESG on Firm Performance For businesses, maintaining relations with stakeholders, such as customers and investors, is important to their success (McWilliams & Siegel, 2001). For investors, particularly those with a long-term focus, a firm’s ESG standing is important in making investment decisions, as previous research has found that ESG investing is associated with positive firm performance (Friede, Busch & Bassen, 2015). The increasing ESG orientation amongst investors can be seen in the rise of ESG related financial products, such as ‘green’ bonds (Judd & Worthington, 2021). Furthermore, the increasing publication and utilisation of ESG scores by institutions signifies the increasing relevance of ESG factors in the market, as well as a growing reliance on these scores (Kjellberg, Pradhan, & Kur, 2019).

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The increase in ESG reporting and regulation is symptomatic of the wider institutionalisation that has been evident across much of the 21st century (Rowbottom & Locke, 2016; Higgins, Stubb, & Milne, 2018; Leins, 2020). Institutional theory posits that the wider institutional environment applies pressure for firms to engage in ESG reporting, through regulatory, cognitive, and normative channels (Higgins, Stubb, & Milne, 2018). Regulatory institutionalisation refers to the rules and regulations highlighted previously in this section, the enforcement of which applies pressure on companies to report their ESG activity (ibid). The normative channel of institutionalisation is concerned with social pressure on the company as to what is perceived socially as the correct action to take (ibid). Meanwhile, the cognitive channel refers to actions being taken because they have become so institutionalised that other alternatives are not considered (ibid). Since higher quality ESG reporting has been correlated with future ESG performance (Du, & Yu 2020), the institutionalisation of ESG as a concept can be linked to ESG performance amongst firms. Furthermore, financial analysts have increasingly placed emphasis on ESG considerations, as financial markets have paid more attention to ESG since the crisis (Leins, 2020).

Based on the notion of financial markets placing increasing emphasis on ESG considerations after the financial crisis (Leins, 2020) and the institutionalisation of ESG amongst firms and institutions (Higgins, Stubb, & Milne, 2018), we ascertain that the importance of ESG performance in the perception of the market as a whole has increased in the period after the financial crisis. Thus, we hypothesise that the impact ESG performance has on firm performance will be greater in this period. With ESG scores representing overall ESG performance, the fifth, and sixth hypotheses were formed:

H5: The impact of ESG scores on ROA is higher from 2010 to 2019 than it was from 2003 to 2006

H6: The impact of ESG scores on Tobin’s Q is higher from 2010 to 2019 than it was from 2003 to 2006

The fifth and sixth hypotheses of the present study are used to answer the second research question, “how has the influence of ESG scores on firm performance in Europe changed in the period after the 2008 financial crisis compared to the period before the crisis?”

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2.4 Summary of Theory and Literature Firstly, prevailing theories on the subject were presented. Overall, the principal-agent theory perspective implies that ESG-enhancing activities will have a negative impact on firm performance, as ESG is seen as an agency cost. In contrast, the stakeholder theory and slack resource theory perspectives posit that ESG performance and firm performance are positively correlated, however, the reason for the relationship differs across the two theories. The theoretical background was further substantiated by a summary of the conclusions of previous literature. Hypotheses 1-4 were developed using principal-agent theory, with the fundamental notion being that should ESG performance be negatively correlated with firm performance, both market-based and accounting-based, ESG-enhancing activities can be viewed as an agency cost. Should the correlation be negative, the opposite view is supported. Previous findings are mixed and inconclusive, particularly across different measures of firm performance, suggesting support for opposing theories. Meanwhile, previous studies have detailed the increasing importance of ESG since the financial crisis, with increasing ESG- related regulation and initiatives in the European Union. It is based on this literature that hypotheses 5 and 6 were formed, which are formed around the notion that the impact of ESG on firm performance has increased in the period after the 2008 financial crisis.

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3. Methodology This section will describe the research methods used to conduct this study. Firstly, the strategy and design of the research are presented. This is followed by details of the study samples and data collection methodology. The data analysis methodology will then be presented, followed by discussions of the reliability and validity of the study, and the ethical considerations.

3.1 Research Design In line with previous studies such as those by Ahmad et al. (2021), Han et al. (2016), and Velte (2017), a quantitative method was utilised in this study to answer our research question. The regression models were utilised to generate the impact of ESG Scores on performance. In addition, t-tests were performed to assess whether there has been a significant change in coefficients between the pre-crisis and post-crisis periods. Finally, additional t-tests were performed to see if the average ESG Score has changed between the pre-crisis and post-crisis periods.

By utilising a quantitative methodology, we were able to make inferences from a large sample of 218 companies that meet the inclusion criteria outlined in Section 3.3 of this paper. In addition, the variables which we tested to answer the research question are ideal for a quantitative study. Firstly, this is because the numerical values of ESG scores are ideal for statistical analysis. In addition, the dependent variables used to measure firm performance are ROA and Tobin’s Q. These are financial metrics that are ideal for statistical analysis. Furthermore, the study was conducted over a larger time period using panel data, from 2003- 2006, and from 2010-2019. Using a quantitative methodology allowed for a larger data set in this regard, as the study includes data points across all of the years for each of the two study periods.

3.2 Operationalisation ESG scores were measured using the ESG Combined Scores sourced from the Eikon Refintiv database. There are multiple providers of ESG scores, however, the Refinitiv score was chosen due to a combination of availability and a large amount of coverage in terms of the number of companies that have scores provided by Refinitiv. A large number of scores is ideal to maximise the sample size of the study. The standard ESG score has a value between 0 and 100, with lower numbers indicating lower scores, and thus lower ESG performance

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(Refintiv, 2021). The standard ESG score is comprised of scores for the Environmental, Social, and Governance pillars, with varying weightings on each pillar, dependent on sector. The Refinitiv ESG Combined Score comprises the standard ESG Score weighted at 50% against an ESG controversy score. The ESG controversy score is a score based on a variety of metrics that measure the impact and severity of ESG controversies in which the firm was involved for a specific financial year. ESG controversy scores may only have a negative impact on the combined score, and thus the ESG Combined Score is equal to the standard ESG score if the ESG Controversy Score exceeds the standard ESG Score. The ESG Combined score was used to provide a more complete picture of the firm’s ESG performance, since the score considers the performance of the firm for all of the three pillars of ESG, as well as controversies.

ESG scores were retrieved for each firm and year in the sample and study period, respectively. Furthermore, the individual scores for the pillars of Environmental, Social, and Governance were also measured. The individual pillar scores also have values between 0 and 100, and are used by Refinitiv to calculate the total ESG score (Refintiv, 2021). The weightings attributed to each pillar by Refinitiv for the total score vary by industry (ibid).

Following from previous literature, accounting-based firm performance was measured using ROA (Wang, Dou, & Jia, 2015). ROA is calculated by dividing the net income of a firm by its total assets. ROA indicates the firm’s ability to generate income relative to the total value of its assets, and thus uses accounting information to measure its scaled performance, i.e. its ability to generate profits, relative to the value of the assets which it utilises to do so. ROA was calculated for each year and firm in the sample and study period, respectively.

Also following from previous literature, market-based firm performance was measured using Tobin’s Q (Wang, Dou, & Jia, 2015). Tobin’s Q is ideal for measuring market-based performance as it measures the market value of equity relative to the book value of equity, taking into account the perception of the firm’s performance by the market. Observations with negative Tobin’s Q values were removed as these were the result of negative book value of equity observations, which are usually ignored by both practitioners and academics, as it is difficult to interpret (Brown et al., 2007). This is a consequence of the fact that a negative book value of equity contradicts the notion that shareholders do not owe anything to the firm (ibid). In addition, due to the non-normal distribution of Tobin’s Q in the sample, the natural

17 logarithm of Tobin’s Q value was used. The natural logarithm is ideal when measuring Tobin’s Q, as it diminishes the impact of extreme outlier observations, as well as observations that may potentially be a result of mismeasurement (Hirsch & Seeks, 1993). Tobin’s Q was retrieved for each firm and year in the sample and study period.

3.3 Data Collection & Sample This study collected secondary data from the Refinitv Eikon database and the STOXX Europe 600 index. Firstly, the iShares STOXX Europe 600 UCITS ETF was used in order to obtain the list of firms currently listed on the index. After this, ESG scores for the firms listed on the index were sourced from the Refinitiv Eikon database. If ESG scores were not available from 2003, then the firm was excluded from the sample. The year 2003 was selected as a starting point as ESG scores were only released by Refinitiv starting in 2002, and many firms still did not have scores in 2002. Thus, the sample is much larger when starting from 2003. After this, the market-based performance measure, Tobin’s Q, was calculated. Tobin’s Q was calculated by dividing the total market value of equity sourced from Refinitiv Eikon by the total book value of equity, which was also sourced from Refinitiv Eikon. Subsequently, the accounting-based firm performance measure, ROA, was also sourced from the Refinitiv Eikon database for each firm in the sample.

The study collected data on 218 publicly listed firms listed on the STOXX Europe 600 index. The STOXX Europe 600 index comprises 600 firms that are publicly listed in 17 European countries, however only 14 countries were represented by the sample. The inclusion criteria for the sample was that the firm must be currently listed on STOXX Europe 600 index at the time of collecting data, and must have ESG scores available from 2003-2006, and 2010-2019, the periods which are focused on in the study. As a result of many firms on the STOXX Europe 600 index not having ESG scores dating from 2003, our sample was reduced to 220 firms. Two of the 220 firms was later removed from the sample due to insufficient data across both sample periods, bringing the sample to 218 firms from the STOXX Europe 600 index. The number of firms listed in each of the 14 countries is shown in Table 1 below. In addition, Table 2 below shows the number of firms in the sample that were classified under each of the 11 GICS Sectors.

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Table 1 Table 2 Countries Number of Firms Sectors Number of Firms Austria 5 Communication Services 16 Belgium 7 Consumer Discretionary 28 Denmark 9 Consumer Staples 18 Finland 8 Energy 7 France 33 Financials 40 Germany 22 Healthcare 18 Ireland 2 Industrials 46 Italy 13 Information Technology 9 Netherlands 12 Materials 18 Norway 7 Real Estate 7 Spain 7 Utilities 11 Sweden 20 Total 218 Switzerland 25 United Kingdom 48 Total 218

Furthermore, four additional sub-samples were generated from the sample of 218 firms due to unavailability of data that was specific to each regression. In order to maintain a balanced data set, firms were excluded altogether in the specific sub-sample if they had any missing values for any year of a variable that was used in the regression for that sub-sample. The listwise deletion of cases due to missing values in any of the variables is a common approach to solve this issue (Allison, 2009, p.73). This approach was deemed reasonable since the missingness of values for ESG Scores among the applicable firms did not depend on the dependent variable, rather, it was due to missing data on the Refinitiv database (Allison, 2009, p.74). Thus, listwise deletion should not cause biased results (Allison, 2009, p.74).

Since many firms had missing values for Environmental Scores in both the pre-crisis and post-crisis periods, the decision was made to create separate sub-samples for the study of Environmental Scores specifically. The reason for creating separate sub-samples for the study of Environmental Scores was that leaving that dimension out from this study would not provide a full picture of the impact of ESG Scores on firm performance. Further, to reduce the sub-samples for the other dimensions (Social and Governance) so that it matched that of the Environmental sub-sample would yield a significantly lower sample size.

Thus, the first sub-sample was for the pre-crisis period, which consisted of 216 firms from the sample. The second sub-sample was also for the pre-crisis period, but was only used for

19 the Environmental regression. The second sub-sample consisted of 156 firms, due to a large number of firms not having Environmental scores for all years in the pre-crisis period. The third sub-sample was for the post-crisis period and consisted of 212 firms, whilst the fourth sub-sample consisted of 205 firms and was only used for the post-crisis regressions including the Environmental variable. Similar to the second sub-sample, the fourth sub-sample was generated due to some of the firms not having environmental scores for all years in the post- crisis period. The first sample was used for all other pre-crisis regressions except for those involving Environmental, whilst the third sub-sample was used for all other post-crisis regressions. A comprehensive list of the firms in each of the sub-samples, and total sample, is available in Appendix 1.

3.4 Data Analysis A regression model is an equation that defines a relationship between a dependent variable and one or more explanatory variables (Gujarati, 2003, p.15). This is done by estimating the effects that an independent variable has on the dependent variable, where the effect is represented by the parameter Beta (Gujarati, 2003, p.41, p47-50). The regressions in this thesis were run using STATA. In line with Ferrell, Hao and Renneboog (2015), we utilised leverage ratio, the logarithm of assets, dividend yield, and ROA (in the case of the Tobin’s Q regression) as control variables. The regression models for ROA and Tobin’s Q were specified as the following:

ROA = α + β1* Leverage Ratio + β2 * Log(Assets) + β3 * Dividend Yield + β4 * ESG Scores + ε

Tobin’s Q = α + β1* Leverage Ratio + β2 * Log(Assets) + β3 * ROA + β4 * Dividend Yield + β5 * ESG Scores + ε

In the models, “α” is the intercept, “β” is the population parameters, and ε is the error term. The regression models were identical for both the post-crisis and pre-crisis sample periods. Meanwhile, the regression models for the individual pillars were conducted with the respective pillar, either Environmental, Social, or Governance replacing the ESG Score.

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3.4.1 Properties of the estimators - Unbiasedness, Consistency, and Efficiency Three properties of the estimators are used to determine which estimator is better in terms of reflecting the true, unknown, population value as best as possible (Stock & Watson, p.57). Unbiasedness states that an estimator, on average, is equal to the population characteristic of the greater population (Dougherty, 2016, p.27; Stock & Watson, p.57). Consistency refers to that the estimator gets closer to the population estimator as the sample size increases (Stock & Watson, p.57). Efficiency is the final property and Dougherty (2016, p.28-29) explains this feature as being a property that reflects reliability, which is reflected by its probability of giving a close estimate of the population characteristic, where a high probability is desirable (ibid). The most efficient estimator is thus the estimator with the lowest variance (Stock & Watson, p.58).

3.4.2 Generalised Least Squares The most commonly used estimator is the Ordinary Least Squares (OLS) (Stock & Watson, p.102), however, in order to be able to make inferences on other countries through the random-effects model, which is discussed further in Section 3.4.3, the Generalised Least Squared (GLS) method was used in the present study. However, certain assumptions regarding the error term must hold (Brooks, 2002, p.55-56), which are outlined below. The GLS assumptions are the same as the OLS assumptions albeit with a few exceptions, namely those of homoscedasticity and autocorrelation (Murray, 2006, p. 408; Gujarati, 2003, p. 947).

Assumption 1 - The mean of the errors is zero, i.e. normality (ibid). However, as this regression equation contains a constant term alpha (α) the first assumption was not violated (Brooks, 2002, p.146).

Assumption 2 - The error and its corresponding x variable are unrelated, i.e. little or no multicollinearity (Brooks, 2002, p.56; Gujarati, 2003, p.929). In order to detect the presence of multicollinearity, correlation matrices among the independent variables were presented. None of the pairwise correlations produced a correlation higher than 0.8, thus the regression model was deemed to not suffer from multicollinearity (Gujarati, 2003, p.359). The results for the tests of multicollinearity for each sub-sample are presented in Appendix 2.

Assumption 3 - The errors are normally distributed (Brooks, 2002, p.56). In order to prove whether this assumption holds, a Shapiro-Wilks test was performed. As the error terms were

21 found to be non-normal, a larger sample size is suggested (Brooks, 2002, p.182). Since the number of observations in our sub-samples was 864 (pre-crisis), 624 (pre-crisis, Environmental) 2,120 (post-crisis), and 2050 (post-crisis, Environmental), the sub-samples could be said to approximate a normal distribution. The results of the Shapiro-Wilks tests can be seen in Appendix 3.

Homoscedasticity - An important difference between OLS and GLS is that assumption can be relaxed (Murray, 2006, p. 408). Regardless, robust-standard errors were used in the regression models.

Autocorrelation - This assumption states that errors are independent of one another, both over time and cross-sectionally (Brooks, 2002, p.155). As a Generalized-least squares estimator is employed, the assumption of no autocorrelation is relaxed (Gujarati, 2003, p.947). This discussed further in the following section.

3.4.3 Fixed Effects and Random Effects As the sample groups in this study contain firms from only 14 European countries, the challenge is to what extent the results and inferences can be extrapolated to other European countries which may not be represented in the sample groups. The Hausman test is typically used to choose between the fixed effects and random effects, however, Gujarati (2003, p.651) argues that the Hausman test may not be sufficient in deciding between the two different models. Bryan and Jenkins (2013, p.7) point out that random effects models can be used in instances where inferences are intended to be made to a broader category of entities, such as countries in the EEA, where each entity has its own individual characteristics. In line with these arguments, a random effects model was employed in order to enable the results to be extrapolated to European countries that are not represented in the STOXX Europe 600 index. This was necessary in order to make our study relevant to Europe as a whole, as our sample only contains firms from 14 European countries. The random effects model was estimated using the Generalized-least-squares (GLS) estimator. The GLS relaxes the assumptions of no autocorrelation and thus no tests for autocorrelation needed to be done (Gujarati, 2003, p.947).

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3.4.4 T-Tests In order to determine whether the regression coefficients had changed across the two periods, pre-crisis and post-crisis, t-tests were conducted. Similarly, t-tests were conducted to see whether the average ESG Scores, including each pillar, had changed across the two periods. Thus, the T-tests were used to inform on hypotheses 5 and 6.

The t-tests for the change in mean of ESG Scores were performed in Excel and since each sample group was not identical across the two time periods, an independent t-test was conducted. In addition, the variance of the populations was not known, thus we used the same methodological approach as described by Newbold et al (2007, p.378-379).

In order to see whether there had been any significant change in the regression coefficients between the pre-crisis and the post-crisis period, the methodology suggested by Currell (2015, p.80) was followed. According to this approach, the difference between the post-crisis and the pre-crisis coefficients was divided by the difference of the respective standard errors (ibid). This gave the t-statistics which were then compared to the critical value.

β푝표푠푡−푐푟푖푠푖푠 − β푝푟푒−푐푟푖푠푖푠 푡푠푡푎푡 = 푆퐸푝표푠푡−푐푟푖푠푖푠 − 푆퐸푝푟푒−푐푟푖푠푖푠

The degrees of freedom were calculated as the sum of the two sample sizes subtracted by 4 (Currell, 2015, p.81). A more comprehensive methodological description is provided by Currell (2015, pp.80-82).

3.4.5 Hypothesis Testing Hypothesis testing was employed in this thesis to determine how reliable the estimates of the true parameters are. As the estimator only gives estimates of the true population parameters, it was necessary to test if the estimated parameters are in line with the expectations (Brooks, 2002, p.64-65). A 5% significance level was suggested as it is the most commonly used (Brooks, 2002, p.72).

3.5 Reliability and Validity Reliability relates to whether the results of a study are consistent (Bell et al, 2019, p.46). A common problem that can arise in quantitative research is whether different estimation

23 techniques can yield two different measurements of the same phenomenon (ibid). In the scope of this thesis, ESG Scores are particularly vulnerable to this issue as different estimation techniques for this phenomenon exist. However, in order to address this issue, we have the same definition of the variables as previous research.

Two components of validity are internal validity and external validity (Bell et al, 2019, p.46- 47). Internal validity refers to the degree of certainty with which we can say that a causal relationship exists between two variables (Bell et al, 2019, p.46-47). Therefore, as this thesis used variables and assumed the same causal relationships as previous research, internal validity can be assumed to be upheld in this thesis. External validity refers to the degree to which the findings of a study can be generalized to other settings (Bell et al, 2019, p.47). This thesis observed companies included in the STOXX Europe 600 index, covering companies from multiple European countries. The use of a random effects model enabled us to extrapolate the results to the broader community of European countries (Bryan & Jenkins 2013, p.7).

3.6 Summary of Methodology The sample sizes were 216 firms in the pre-crisis period and 156 firms for the Environmental group in the pre-crisis period. Similarly, the post-crisis period consisted of 212 firms and 205 firms in the Environmental post-crisis period. The pre-crisis period consisted of data from 2003-2006, whilst the post-crisis period consisted of data from 2010-2019. Data was collected on ESG Combined Scores to measure ESG Scores, as well as the Environmental, Social, and Governance pillars. Accounting-based firm performance was measured using ROA, whilst market-based performance was measured using Tobin’s Q. To assess the impact of ESG Scores on firm performance, a random effects regression model was employed to test panel data across the sample periods. The GLS estimator was used for the regressions. The choice of random effects allowed us to make inferences about companies listed in a broader group of European countries. Throughout the thesis, a 5% significance level was applied to all statistical tests to infer significance. The coefficients that were generated from each regression were then tested against their corresponding values for both the pre-crisis and post-crisis periods in a t-test to see if there was any statistically significant change in the impact of ESG Scores on market-based or accounting-based firm performance.

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4. Results This section begins with descriptive statistics for the variables measured. Following this, we report the results from the regressions conducted to test the hypotheses of the study. In addition, we report the results of statistical tests used to test the assumptions of an OLS regression detailed in the method section.

4.1 Descriptive Statistics Table 3 Standard No. Descriptive Statistics Period Mean Median Maximum Minimum No. Firms Deviation Observations Pre-Crisis 43.98 16.86 43.65 89.17 3.55 864 216 ESG Scores Post-Crisis 67.73 16.27 70.03 94.68 2.84 2120 212 Pre-Crisis 42.26 21.67 43.81 94.04 0.57 624 156 Environmental Post-Crisis 70.48 19.53 74.86 99.10 0.77 2050 205 Pre-Crisis 43.44 19.96 41.69 98.03 2.56 864 216 Social Post-Crisis 70.58 19.11 74.46 98.63 2.38 2120 212 Pre-Crisis 51.88 22.35 53.58 98.37 2.41 864 216 Governance Post-Crisis 61.40 21.29 65.06 98.30 6.64 2120 212

The descriptive statistics for the data set are presented in Table 3 above. The pre-crisis sample consisted of 216 firms, each being observed annually over a 4-year period. This yields a total of 864 observations per variable. However, as some firms did not have any values for Environmental Scores, that particular regression contained only 156 firms, thus yielding 624 observations per variable. The post-crisis sample group consisted of 212 firms, with a total of 2120 observations per variable, as the period consisted of 10 years. Meanwhile, only 205 firms in the post-crisis sample had Environmental Scores for all 10 years, yielding 2050 observations for that particular regression. Appendix 4 gives a more complete picture of the descriptive statistics, including for control variables.

4.2 Pre-Crisis Accounting-Based Firm Performance Results The results of all the regressions in the pre-crisis sample period (2003-2006), where ROA was the dependent variable, indicated positive and significant correlations between ROA and the independent variables of ESG Scores and the Environmental and Social pillars. The Governance pillar, however, was not significantly correlated with ROA. In the sections below, p-values refer to level of significance and r-squared indicates the explanatory power of each regression. For a more comprehensive overview of these regression statistics, please see Appendix 5.

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Model 1 Dependent Variable Return on Assets

Independent Variables Coefficient Standard Errors Z-statistics P-values ESG Scores 0.0002 0.0001 2.37 0.018 Dividend Yield -0.1272 0.0836 -1.52 0.128 Debt Ratio -0.0005 0.0001 -8.43 0.000 Log of Total Assets -0.0140 0.0018 -7.73 0.000 Constant 0.3810 0.0436 8.73 0.000

Number of Firms 216 Observations per Firm 4 Number of Observations 864 R-squared 0.2589

Model 1 presents the impact of ESG Scores on ROA. As expected, ESG Scores were positively correlated with ROA pre-crisis, with a coefficient of 0.0002, which was statistically significant (P= .018), with an R² of 0.26. Among all the regressions on ROA pre- crisis, the regression of ESG Scores on ROA yielded the strongest explanatory power. Furthermore, the significance of the correlation supported the first hypothesis, ESG Scores had a statistically significant impact on ROA from 2003 to 2006.

Model 2 Dependent Variable Return on Assets

Independent Variables Coefficient Standard Errors Z-statistics P-values Environmental Scores 0.0002 0.0001 3.23 0.001 Dividend Yield -0.0913 0.0742 -1.23 0.219 Debt Ratio -0.0004 0.0001 -7.94 0.000 Log of Total Assets -0.0114 0.0017 -6.73 0.000 Constant 0.3195 0.0412 7.76 0.000

Number of Firms 156 Observations per Firm 4 Number of Observations 624 R-squared 0.2166

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As can be seen in Model 2, Environmental Scores were also positively correlated with ROA pre-crisis, with a coefficient of 0.0002, which was highly statistically significant (P=.001), with an R² of 0.22.

Model 3 Dependent Variable Return on Assets

Independent Variables Coefficient Standard Errors Z-statistics P-values Social Scores 0.0001 0.0001 2.07 0.038 Dividend Yield -0.1219 0.0840 -1.45 0.147 Debt Ratio -0.0005 0.0001 -8.30 0.000 Log of Total Assets -0.0138 0.0018 -7.53 0.000 Constant 0.3794 0.0440 8.63 0.000

Number of Firms 216 Observations per Firm 4 Number of Observations 864 R-squared 0.2557

Similar to ESG Scores and Environmental Scores, Social Scores were positively and significantly related to ROA pre-crisis, with a coefficient of 0.0001, which was statistically significant (P=.038), with an R² of 0.26. The results for the pre-crisis regressions for Social Scores and ROA are depicted in Model 3 above.

Model 4 Dependent Variable Return on Assets

Independent Variables Coefficient Standard Errors Z-statistics P-values Governance Scores 0.0000 0.0001 0.58 0.559 Dividend Yield -0.1201 0.0840 -1.43 0.153 Debt Ratio -0.0005 0.0001 -8.48 0.000 Log of Total Assets -0.0133 0.0018 -7.58 0.000 Constant 0.3730 0.0433 8.62 0.000

Number of Firms 216 Observations per Firm 4 Number of Observations 864 R-squared 0.2531

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Lastly, as can be seen in Model 4 above, Governance Scores were also positively correlated with ROA pre-crisis, with a coefficient of less than 0.0001, however this was not statistically significant, (P=.559).

4.3 Post-Crisis Accounting-Based Firm Performance Results The regressions where ROA was the dependent variable for the post-crisis sample period (2010-2019) gave largely mixed results. Whilst the regression for the Environmental pillar indicated a positive and significant correlation with ROA post-crisis, the Social pillar and ESG Scores were not significantly correlated with ROA post-crisis. In line with the pre-crisis findings, the Governance pillar was not significantly correlated with ROA post-crisis either. More detailed statistics for these regressions are presented in Appendix 6.

Model 5 Dependent Variable Return on Assets

Independent Variables Coefficient Standard Errors Z-statistics P-values ESG Scores 0.0000 0.0001 0.19 0.853 Dividend Yield 0.0376 0.0925 0.41 0.684 Debt Ratio -0.0084 0.0019 -4.43 0.000 Log of Total Assets -0.0105 0.0015 -6.83 0.000 Constant 0.3089 0.0373 8.28 0.000

Number of Firms 212 Observations per Firm 10 Number of Observations 2120 R-squared 0.2314

ESG Scores were positively correlated with ROA post-crisis, with a coefficient of less than 0.0001, however, unlike for the pre-crisis period, this was not statistically significant (P=.853). Due to the lack of significance of the regression result, the second hypothesis, ESG Scores had a statistically significant impact on ROA from 2010 to 2019, was not supported. The post-crisis ESG Score and ROA regressions are depicted in Model 5 above.

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Model 6 Dependent Variable Return on Assets

Independent Variables Coefficient Standard Errors Z-statistics P-values Environmental Scores 0.0002 0.0001 2.02 0.043 Dividend Yield 0.0363 0.0945 0.38 0.701 Debt Ratio -0.0084 0.0020 -4.31 0.000 Log of Total Assets -0.0116 0.0017 -6.77 0.000 Constant 0.3193 0.0390 8.19 0.000

Number of Firms 205 Observations per Firm 10 Number of Observations 2050 R-squared 0.2259

On the other hand, Environmental Scores were positively correlated with ROA post-crisis, with a coefficient of 0.0002, which was statistically significant (P=.043), with an R² of 0.23. The results of the regression for Environmental Scores and ROA post-crisis are depicted in Model 6 above.

Model 7 Dependent Variable Return on Assets

Independent Variables Coefficient Standard Errors Z-statistics P-values Social Scores 0.0002 0.0001 1.57 0.117 Dividend Yield 0.0403 0.0924 0.44 0.663 Debt Ratio -0.0080 0.0019 -4.34 0.000 Log of Total Assets -0.0117 0.0016 -7.32 0.000 Constant 0.3252 0.0369 8.81 0.000

Number of Firms 212 Observations per Firm 10 Number of Observations 2120 R-squared 0.24

Model 7 above depicts the results of the regression for Social Scores and ROA post-crisis. Whilst Social Scores were positively correlated with ROA post-crisis, with a coefficient of 0.0002, this was not statistically significant (P=.117).

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Model 8 Dependent Variable Return on Assets

Independent Variables Coefficient Standard Errors Z-statistics P-values Governance Scores 0.0000 0.0001 -0.28 0.776 Dividend Yield 0.0383 0.0927 0.41 0.679 Debt Ratio 0.0084 0.0019 -4.46 0.000 Log of Total Assets -0.0103 0.0016 -6.49 0.000 Constant 0.3071 0.0375 8.19 0.000

Number of Firms 212 Observations per Firm 10 Number of Observations 2120 R-squared 0.2319

Finally, as can be seen in Model 8 above, Governance Scores were negatively correlated with ROA post-crisis, with a coefficient of less than -0.0001. However, similarly to the Social Score correlation, the correlation was also not statistically significant (P=.776).

4.4 Pre-Crisis Market-Based Firm Performance Results Similar to the results for the regressions with ROA as the dependent variable, the results of the regressions for the pre-crisis sample period (2003-2006) with Tobin’s Q as the dependent variable indicated positive and significant correlations between Tobin’s Q and ESG Scores. This was also true for Environmental and Social Scores. Governance scores, however, were both negatively and non-significantly correlated with Tobin’s Q. Appendix 7 presents more detailed statistics for the pre-crisis market-based firm performance regressions.

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Model 9 Dependent Variable Tobin's Q

Independent Variables Coefficient Standard Errors Z-statistics P-values ESG Scores 0.0030 0.0011 2.81 0.005 Return on Assets 3.1299 0.4978 6.29 0.000 Dividend Yield -8.0588 3.4134 -2.36 0.018 Debt Ratio 0.0184 0.0008 24.36 0.000 Log of Total Assets -0.0854 0.0184 -4.65 0.000 Constant 2.8786 0.3966 7.26 0.000

Number of Firms 216 Observations per Firm 4 Number of Observations 864 R-squared 0.443

As can be seen in Model 9 above, the third hypothesis, ESG Scores had a statistically significant impact on Tobin’s Q from 2003 to 2006, is supported by the results. This is due to ESG Scores being positively correlated with Tobin’s Q pre-crisis, with a coefficient of 0.0030, which was highly statistically significant (P=.005), with an R² of 0.44.

Model 10 Dependent Variable Tobin's Q

Independent Variables Coefficient Standard Errors Z-statistics P-values Environmental Scores 0.0019 0.0010 1.98 0.048 Return on Assets 3.3418 0.6231 5.36 0.000 Dividend Yield -7.1657 3.5583 -2.01 0.044 Debt Ratio 0.0183 0.0005 36.89 0.000 Log of Total Assets -0.0600 0.0193 -3.10 0.002 Constant 2.2660 0.4377 5.18 0.000

Number of Firms 156 Observations per Firm 4 Number of Observations 624 R-squared 0.3972

Model 10 depicts the regression results for Environmental Scores and Tobin’s Q pre-crisis. Much like ESG Scores, Environmental Scores were also positively correlated with Tobin’s Q

31 pre-crisis, with a coefficient of 0.0019, which was statistically significant (P=.048), with an R² of 0.40.

Model 11 Dependent Variable Tobin's Q

Independent Variables Coefficient Standard Errors Z-statistics P-values Social Scores 0.0027 0.0009 2.92 0.004 Return on Assets 3.1435 0.4992 6.30 0.000 Dividend Yield -8.0148 3.3969 -2.36 0.018 Debt Ratio 0.1841 0.0007 24.70 0.000 Log of Total Assets -0.0855 0.0186 -4.59 0.000 Constant 2.8897 0.4014 7.20 0.000

Number of Firms 216 Observations per Firm 4 Number of Observations 864 R-squared 0.4494

Similar to Environmental Scores and ESG Scores, Social Scores were positively correlated with Tobin’s Q pre-crisis, with a coefficient of 0.0027, which was highly statistically significant (P=.004), with an R² of 0.45. These results can be seen in Model 11 above.

Model 12 Dependent Variable Tobin's Q

Independent Variables Coefficient Standard Errors Z-statistics P-values Governance Scores -0.0002 0.0008 -0.30 0.763 Return on Assets 3.2052 0.5022 6.38 0.000 Dividend Yield -7.9224 3.3978 -2.33 0.020 Debt Ratio 0.0182 0.0008 24.13 0.000 Log of Total Assets -0.0726 0.0174 -4.18 0.000 Constant 2.7106 0.3841 7.06 0.000

Number of Firms 216 Observations per Firm 4 Number of Observations 864 R-squared 0.4376

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Lastly, Governance Scores were negatively correlated with Tobin’s Q pre-crisis, with a coefficient of -0.0002. However, the results were not statistically significant (P=.763). The results for the regression with Governance Scores as the independent variable and Tobin’s Q as the dependent variable for the pre-crisis period are displayed in Model 12 above.

4.5 Post-Crisis Market-Based Firm Performance Results The results of the regressions with Tobin’s Q as the dependent variable and the four forms of ESG Scores as the independent variables for the post-crisis period produced similar results to the same regressions for the pre-crisis period. ESG Scores, Environmental Scores, and Social Scores were all positively and statistically significantly correlated with Tobin’s Q. Similarly, to the pre-crisis period, Governance Scores were still not significantly correlated with Tobin’s Q in the post-crisis period. For a more comprehensive overview of these regression statistics, please see Appendix 8.

Model 13 Dependent Variable Tobin's Q

Independent Variables Coefficient Standard Errors Z-statistics P-values ESG Scores 0.0027 0.0010 2.84 0.005 Return on Assets 3.4372 0.4810 7.15 0.000 Dividend Yield -5.0474 1.4220 -3.55 0.000 Debt Ratio 0.0092 0.0231 4.29 0.000 Log of Total Assets -0.2089 0.0284 -7.36 0.000 Constant 5.4476 0.6770 8.05 0.000

Number of Firms 212 Observations per Firm 10 Number of Observations 2120 R-squared 0.4862

Similar to the pre-crisis results, ESG Scores were positively correlated with Tobin’s Q post- crisis, with a coefficient of 0.0027, which was highly statistically significant (P=.005), with an R² of 0.49. These results supported the fourth hypothesis, ESG Scores had a statistically significant impact on Tobin’s Q from 2010 to 2019, and can be seen in Model 13 above.

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Model 14 Dependent Variable Tobin's Q

Independent Variables Coefficient Standard Errors Z-statistics P-values Environmental Scores 0.0022 0.0011 1.97 0.049 Return on Assets 3.3704 0.4888 6.90 0.000 Dividend Yield -4.8738 1.4386 -3.39 0.001 Debt Ratio 0.7980 0.0237 4.13 0.000 Log of Total Assets -0.2027 0.0302 -6.70 0.000 Constant 5.3249 0.7010 7.60 0.000

Number of Firms 205 Observations per Firm 10 Number of Observations 2050 R-squared 0.4781

As can be seen above in Model 14, Environmental Scores were also positively correlated with Tobin’s Q post-crisis, with a coefficient of 0.0022, which was statistically significant (P=.049), with an R² of 0.48.

Model 15 Dependent Variable Tobin's Q

Independent Variables Coefficient Standard Errors Z-statistics P-values Social Scores 0.0035 0.0011 3.28 0.001 Return on Assets 3.3949 0.4826 7.03 0.000 Dividend Yield -4.9606 1.4329 -3.46 0.001 Debt Ratio 0.1015 0.0232 4.38 0.000 Log of Total Assets -0.2239 0.0285 -7.85 0.000 Constant 5.7249 0.6707 8.54 0.000

Number of Firms 212 Observations per Firm 10 Number of Observations 2120 R-squared 0.4964

Social Scores were also positively correlated with Tobin’s Q post-crisis, with a coefficient of 0.0035, which was highly statistically significant (P=.001), with an R² of 0.50. The results of the regression are displayed in Model 15 above.

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Model 16 Dependent Variable Tobin's Q

Independent Variables Coefficient Standard Errors Z-statistics P-values Governance Scores -0.0012 0.0008 -1.45 0.146 Return on Assets 3.4271 0.4880 7.02 0.000 Dividend Yield -4.9857 1.4115 -3.53 0.000 Debt Ratio 0.0945 0.0229 4.13 0.000 Log of Total Assets -0.1907 0.0293 -6.51 0.000 Constant 5.2534 0.6947 7.56 0.000

Number of Firms 212 Observations per Firm 10 Number of Observations 2120 R-squared 0.4805

Finally, Governance Scores were still negatively correlated with Tobin’s Q post-crisis, with a coefficient of -0.0012. However, the results were not statistically significant (P=.146). The results for the regression are depicted in Model 16 above.

4.6 Test of Change in Coefficients for ESG Scores and ROA

Table 4 Change in Coefficient: Post-crisis vs Pre-crisis ROA Mean SE Measured Variable Difference Difference P-value ESG Scores -0.0002 0.0001 0.951 Environmental 0.0000 0.0001 0.426 Social 0.0000 0.0001 0.405 Governance -0.0003 0.0001 0.993

The t-tests indicated that there has not been any statistically significant change in the impact of ESG Scores, including each individual pillar dimension, on ROA, between the pre-crisis and post-crisis periods. In addition, the same t-tests were conducted to test the change in the coefficient between each of the individual pillars of ESG Scores and ROA between the two study periods. As with the findings of the t-tests testing ESG Scores, the coefficients did not change significantly for any of the individual pillars, Environmental, Social, or Governance.

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The results of the t-tests measuring the significance of the change in coefficients between ESG Scores (and the three pillars) and ROA across the two study periods can be seen in Table 4 above. A more detailed overview of the statistics for these t-tests is presented in Appendix 9.

4.7 Test of Change in Coefficients for ESG Scores and Tobin’s Q

Table 5 Change in Coefficient: Post-crisis vs Pre-crisis Tobin's Q Mean SE Measured Variable Difference Difference P-value ESG Scores -0.0003 0.0014 0.593 Environmental 0.0003 0.0015 0.411 Social 0.0008 0.0014 0.281 Governance -0.0009 0.0011 0.792

Similar to the results of the t-tests measuring the change in coefficients between ESG Scores and ROA from the pre-crisis to post-crisis period, the results of the t-tests measuring the same change, but with Tobin’s Q as the dependent variable, also found no significant changes. Similarly, the same was true for each of the three individual pillars of ESG. The results of the t-tests measuring the significance of the change in coefficients between ESG Scores (and the three pillars) and Tobin’s Q across the two study periods can be seen in Table 5 above. Similarly to the previous section, a more detailed overview of the statistics for the t-tests measuring the change in impact on Tobin’s Q is also presented in Appendix 9.

4.8 Test of Change in Mean ESG of Scores

Table 6 Change in Mean ESG No. Period Mean Variance Δ of Mean Df. T-statistics P-value Score Observations Pre-Crisis 43.98 284.11 864 ESG Scores 23.75 1552 35.25 0.000 Post-Crisis 67.73 264.82 2120 Pre-Crisis 42.26 469.80 624 Environmental 28.22 951 29.12 0.000 Post-Crisis 70.48 381.56 2050 Pre-Crisis 43.44 398.54 864 Social 27.14 1541 34.10 0.000 Post-Crisis 70.58 365.29 2120 Pre-Crisis 51.88 499.55 864 Governance 9.52 1534 10.69 0.000 Post-Crisis 61.40 453.37 2120

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The average ESG Score was 43.9 before the crisis and 67.7 after the crisis. The Environmental pillar saw the biggest increase, from 42.3 pre-crisis to 70.5 post-crisis. The corresponding values for the Social dimension were 43.4 pre-crisis and 70.6 post-crisis. The Governance dimension saw the smallest change, from 51.9 pre-crisis to 61.4 post-crisis. The t-test results indicate that all ESG metrics increased significantly and that they were all statistically significantly higher in the post-crisis era compared to the pre-crisis era. The results can be seen in Table 6 above. A more detailed overview of the statistics for these t- tests is presented in Appendix 10.

4.9 Summary of the Results Across the regressions, the results indicated a positive correlation between ESG and firm performance across all study periods and measures, except for post-crisis accounting-based firm performance. Thus, of H1-H4, only H2 was not supported. Similarly, the Social pillar was significantly correlated for all periods except the same post-crisis period when regressed against accounting-based performance. The Environmental pillar was positively and significantly correlated with all firm performance measures across all study periods. Similarly, ESG, Social, and Environmental were positively and significantly correlated with market-based performance across both study periods. Meanwhile, the Governance pillar was not significantly correlated with firm performance in any of the regressions. Furthermore, the results of the t-tests indicated the changes in the coefficients were not significant for any of the tests. Thus, H5 and H6 were not supported, however additional t-tests confirmed that the mean of all 4 of the ESG metrics had increased significantly from the pre-crisis to post-crisis period. A summary of the results is presented in Table 6 below.

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5. Analysis In this section, the results of the statistical analyses are used to answer the research questions. The results are analysed in the context of principal-agent theory. In addition, stakeholder theory is also explored to explain the importance of ESG to firm performance.

5.1 ESG Scores and Accounting-Based Firm Performance In this section, we will analyse the results presented in Sections 4.2 and 4.3, which are relevant to hypotheses 3 and 4.

Principal-agent theory posits that management may act in their own self-interest, with shareholders bearing the costs of those managerial decisions (Friedman, 1970). A part of that pursuit of self-fulfilling goals may be to improve the ESG standing of the firm, through the socioemotional wealth or career enhancement that ESG enhancing endeavours may provide the manager with (McWilliams & Siegel, 2001). This becomes an agency cost when these resources may be better spent elsewhere, such as through improving operational efficiency (Friedman, 1970).

The results of the regressions gave mixed findings regarding the impact of ESG Scores on ROA. In the pre-crisis period, ESG Scores were positively correlated with ROA, and the relationship was statistically significant. This is in opposition to the principal-agent theory perspective of ESG, which posits that ESG engagements are a misallocation of resources (Friedman, 1970). ESG scores were found to be positively related to ROA in the period before the crisis, and the relationship was statistically significant. A similar observation can be made for the three pillars of ESG respectively, which were all positively and significantly correlated with ROA during that period. Surprisingly, in the post-crisis period, however, the relationship was not statistically significant. This finding neither supports nor goes against the principal-agent theory perspective of ESG, since the results were non-significant. To support the principal-agent theory perspective, that is, that ESG is a result of a misallocation of resources due to management (or agents) acting in self-interest (Friedman, 1970), a negative correlation between ESG performance and firm performance would be necessary.

One potential explanation for the discrepancy between the pre-crisis and post-crisis findings is derived from slack resource theory, which criticizes the notion that ESG performance has a

38 causational effect on firm performance (Wang, Dou, & Jia, 2015). Slack resource theory, as discussed in the theoretical background of this paper, suggests that firms investing in ESG- enhancing activities principally do so because excessive resources are at their disposal (ibid). In this context, firms with large amounts of resources would typically be performing at a greater level already, to allow for this resource slack (ibid). Thus, from this perspective, firms that are performing well financially may thus have the resources to invest in ESG enhancing activities because of their already good firm performance. Slack resource theory is particularly relevant to ROA, an accounting-based measure, as it is focused on the resources available to the firm. In the context of the results of the present study, firms may have had a greater degree of resource slack pre-crisis, which enabled them to engage in ESG-enhancing activities. This could therefore explain the correlation.

The strongest explanation for the discrepancy, however, is rooted in the fact that the mean ESG scores increased statistically significantly in the post-crisis period relative to the pre- crisis period. With this in mind, firms may have experienced falls in revenue during the crisis. In fact, this may have been the case for many years after the crisis, including some years of the post-crisis period of the present study. Therefore, ESG enhancing activities that may have been beneficial to accounting-based firm performance pre-crisis, may have become redundant in a time of economic hardship.

On the other hand, the fact that ESG Scores were also positively and significantly correlated with Tobin’s Q suggests that stakeholders such as investors do indeed value ESG performance. Thus, the results are more supportive of the stakeholder theory perspective of ESG, introduced earlier in this paper. Similarly, previous research studying the relationship between ESG performance and firm performance has found that the evidence is more in favour of the stakeholder perspective (Wang, Dou, & Jia, 2015). The stakeholder theory will be discussed further in the analysis of the results for ESG Scores and market-based firm performance, in the following section.

Ultimately, due to the fact that ESG Scores were positively and significantly correlated with ROA in the period before the financial crisis, the null hypothesis was rejected for hypothesis 1. The null hypothesis, however, could not be rejected for hypothesis 2, since the correlation was not significant:

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H1: ESG Scores had a statistically significant impact on ROA from 2003 to 2006- rejected the null hypothesis

H2: ESG Scores had a statistically significant impact on ROA from 2010 to 2019- failed to reject the null hypothesis

Interestingly, however, of all the three pillars that make up the ESG Scores, only the Environmental pillar was found to be statistically significantly correlated with accounting- based performance in the period after the financial crisis. Meanwhile, the Social pillar was positively and significantly correlated with accounting-based performance across both periods, whilst the Governance pillar was not significantly correlated for either period. The findings suggest that only the externally oriented pillars of Environmental and Social have a significant impact on profitability. These findings further support the stakeholder theory perspective of ESG, which posits that the positive impact of ESG engagements is derived from the positive impact that ESG has on the relationship between the firm and its wider stakeholders (Wang, Dou, & Jia, 2015). This notion, regarding the individual pillars, will be analysed further in the following section, both in relation to market-based performance, and firm performance in general.

5.2 ESG Scores and Market-based Firm Performance In this section, we will analyse the results presented in Sections 4.4 and 4.5, which are relevant to hypotheses 3 and 4.

The results of the regressions indicated that ESG Scores were a significant determinant of market-based performance in Europe both before and after the crisis, since ESG Scores had a significant and positive impact on Tobin’s Q. This finding gives further support to the notion that firms that engage in ESG-enhancing activities are viewed in a more positive light than firms that do not (McWilliams & Siegel, 2001). Analysing each ESG pillar on its own, we find that both the environmental and social pillars are correlated significantly with market- based firm performance before and after the crisis. This suggests that only the pillars that can be considered to have a direct external impact on stakeholders were valued by the investors during both periods. Meanwhile, the Governance pillar, which is more focused on internal factors of a firm, appeared to have less of an impact on market-based firm performance.

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The regressions for the period after the crisis indicate that ESG Scores are still positively and significantly impacting Tobin’s Q in firms on the European market. This result is not surprising, considering that previous research suggests that ESG Scores are a relevant determinant of firm performance (Orlitzky, Schmidt, & Rynes, 2003; Wang, Dou, & Jia, 2015). The fact that the environmental pillar was significantly correlated with performance is of particular interest, as a prior meta-analysis conducted in 2003 found environmental performance to be less correlated with firm performance than the other ESG pillars, however, the time-span of the study may explain the discrepancy (Orlitzky, Schmidt, & Rynes, 2003). This suggests that the Environmental pillar has become a more important determinant of firm performance, both accounting and market-based, since the time that the meta-analysis was conducted, relatively speaking (ibid). Additionally, the same meta-analysis found managerial principles, a concept that is somewhat aligned with governance and concerns the internal ethical considerations of the firm, to have a stronger impact on firm performance than environmental factors (Orlitzky, Schmidt, & Rynes, 2003). This also conflicted with the lack of significance of the correlation between governance and both accounting-based and market- based performance in the present study.

The finding that the Social pillar is significantly correlated with market performance during both periods is in line with that of Ahmad et al (2021) who found the Social pillar to be the only pillar that was a statistically significant determinant of firm performance. Their study was conducted on the UK market, but the finding of this thesis is that European investors seem to value firms’ engagements on the social dimension. Interestingly, however, Ahmad and his colleagues' (2021) study on the UK market did not find the Environmental pillar to be significantly correlated with market-based performance, albeit using different market-based measures to the present study. Further research could explore the relationship between the different ESG pillars and firm performance while utilising different measures of performance, as well as across firms in different geographical areas.

The other interesting finding is that governance is still statistically non-significant in the post- crisis period. In the introduction of this thesis, the financial crisis was described as being fuelled by flawed corporate governance systems, with measures having been taken to amend these flaws (Sampei, 2018). However, investors do not seem to value the efforts made by firms on the Governance dimensions. A final finding on Tobin’s Q in the post-crisis era is that investors seem to value the dimensions that are more externally oriented, Environmental

41 and Social, whereas the internal dimension, Governance, is disregarded. Regarding the theoretical framework of the present study, we ascertain that the results of the regressions suggest that investors in the market do not see ESG enhancing activities as an agency cost. Further research could explore the role that greater trust and reduced information asymmetry between the firm and the market may have in the relationship between ESG and Tobin’s Q.

The results of the statistical tests are in fact in line with the stakeholder theory perspective of ESG. As discussed in Section 2 of this paper, stakeholder theory posits that ESG performance has a positive impact on firm performance (McWilliams & Siegel, 2001). This is due to the fact that ESG improves the trust that stakeholders have in the firm, which thus improves the firm’s brand equity (ibid). The positive relationship between ESG Scores and ROA pre-crisis, and ESG Scores and Tobin’s Q, for both of the study periods suggests that ESG may indeed be improving the performance of the firm through improving the relationship between the firm and its stakeholders.

A firm’s stakeholders are at the crux of stakeholder theory (McWilliams & Siegel, 2001), and the ESG pillar-specific results of the regressions give further support to stakeholder theory. Specifically, the externally focussed Environmental and Social pillars are vital elements of a firm’s interaction with many of its stakeholders. These two pillars were found to be positively associated with firm performance and value after the crisis, suggesting the impact they have on the firm is positive. Interestingly, the Governance pillar, which is more concerned with the internal workings of the firm, and less with the interaction between the firm and its stakeholders, was not significant in either study period. The fact that the Governance pillar was not significantly correlated with firm performance was particularly surprising in the post- crisis period, considering the increase in emphasis on corporate governance following the fall-out from the 2008 financial crisis (Collins, 2009; Davies, 2020; Rowe, 2019). However, the non-significant impact of Governance on Tobin’s Q is in line with the finding that Governance did not have a significant impact on ROA, suggesting that investors only value dimensions that improve profitability. Furthermore, Gerard (2019) refers to the distinction between CSR and ESG as CSR only encompassing the Environmental and Social impact of the firm. From this perspective, our results indicate that ESG may only positively impact firm performance to the extent that it encompasses CSR.

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The fact that the Environmental pillar had a significant impact on both ROA and Tobin’s Q both before and after the crisis is of particular interest as the meta-analysis by Orlitzky and colleagues (2003) found that the environmental pillar had the least impact on firm performance relative to other ESG factors. The significance of the impact of the Social and Environmental pillars and not the Governance pillar further supports the key sentiment of stakeholder theory: that ESG has a positive impact on firm performance due to the positive impact it has on the relationship between the firm and its stakeholders (McWilliams & Siegel, 2001). Ultimately, the fact that the correlation between ESG Scores and Tobin’s Q in both the period before and after the financial crisis was significant led to the null hypothesis being rejected for both hypothesis 3 and hypothesis 4:

H3: ESG Scores had a statistically significant impact on Tobin’s Q from 2003 to 2006- rejected the null hypothesis

H4: ESG Scores had a statistically significant impact on Tobin’s Q from 2010 to 2019- rejected the null hypothesis

5.3 The Change in Impact of ESG on Firm Performance In this section, we will analyse the results presented in Sections 4.6-4.8, which are relevant to hypotheses 3 and 4.

The t-tests were performed to measure the change in the coefficients for ESG on ROA, and ESG on Tobin’s Q, respectively, from the period before to the period after the financial crisis. The results of the t-tests indicated that there was no statistically significant change in the impact of ESG Scores, and the individual pillars, on ROA and Tobin’s Q respectively. The results contradict Leins’ (2020) statement that ESG became more important after the financial crisis. A possible solution for the lack of statistically significant results is that other defining events, other than the financial crisis, would have been better turning points for the rise of ESG Scores and the impact they have on firm performance. Although the financial crisis, as stated in the introduction, may partially be explained by managerial ignorance regarding ESG-related matters, the results suggest that the financial crisis had little direct impact on the environmental dimension. It is possible that we would have found a statistically significant change in impact before and after natural disasters, such as Hurricane Katrina, for example. Similar events could be singled out for the other ESG pillars, Social and Governance.

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It is possible, however, that the increasing importance of ESG is reflected in other areas than firm performance - such as the increase in ESG performance itself observed in the increasing mean ESG Scores. In fact, the t-tests performed on the means of the ESG Scores themselves, including each respective pillar, showed that they had all increased post-crisis and that the change was statistically significant. This is supported by institutional theory, which suggests that firms have paid more attention to, and improved their ESG performance post-crisis (Higgins, Stubb, & Milne, 2018). According to institutional theory, this is due to the social pressure applied on the firm by the institutional environment to engage more in ESG- enhancing activities (ibid). Interestingly, however, the results do not indicate that there was a statistically significant change in the impact of ESG Scores on firm performance, whether this was measured using ROA or Tobin’s Q. This suggests that ESG is valued by stakeholders and the market to a similar extent during both the pre-crisis and post-crisis periods. Interestingly, however, ESG Scores were not correlated significantly with ROA in the post-crisis period. This suggests that not only was the impact of ESG scores not statistically significantly higher in the post-crisis period, but it was in fact lower, however the decrease in impact was non-significant. The change in the relationship between the pillar scores of ESG was also not statistically significant. Ultimately, the lack of significance led us to fail to reject the null hypothesis for hypothesis 5:

H5: The impact of ESG scores on ROA is higher from 2010 to 2019 than it was from 2003 to 2006- failed to reject the null hypothesis

Similar to the t-tests which tested the change in the impact of ESG Scores on ROA, the t-tests that tested the impact of ESG on Tobin’s Q indicated the impact did not change significantly. The same was also true for the scores related to the three individual pillars of ESG. Thus, this led us to fail to reject the null hypothesis for hypothesis 6:

H6: The impact of ESG scores on Tobin’s Q is higher from 2010 to 2019 than it was from 2003 to 2006- failed to reject the null hypothesis

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5.4 Limitations Despite the fact that this study used a relatively large sample, there are some limitations to this study. These are discussed in this section.

Firstly, this study observes only European firms, which may make any inference to firms outside of Europe invalid. Furthermore, any inference within Europe may be misleading since regional differences may exist. In addition, some smaller European countries may not be represented in the sample group, which may reduce the validity of any inference to a firm operating in a smaller European country not represented in the sample. On the other hand, the use of the random-effects model may resolve this limitation to some extent (Bryan & Jenkins, 2013), as discussed in Section 3.4.3. The sample only consists of large, publicly listed firms from 14 European countries. Thus, the findings may not hold true for smaller firms and private firms. It is important to note, however, that ESG Scores were only available for larger publicly listed firms across much of the sample period. In fact, the choice of the beginning of the pre-crisis study period to be 2003 was made due to the availability of data- a significant number of firms only had ESG Scores available from 2003, and Refinitiv only started issuing the scores from 2002 onward. As a result of the availability of ESG Scores, the sub-samples were reduced to significantly lower numbers than the full 600 firms listed on the index.

Secondly, this study used only one measurement of ESG and its individual pillar components. While the Refinitiv measurement may be widely used, it is possible that other measurements may have yielded different results. Across different issuers of ESG Scores, the scores issued are often largely inconsistent (Barnett, 2020). This is thus not only a limitation to the present study, but of ESG Scores themselves as a construct. ESG Scores are a relatively new phenomenon (MacMahon, 2020), and their limitations may not yet be fully determined. Furthermore, there is a lack of standardisation regarding ESG information, which leads it to typically be of lesser quality and structure (MacMahon, 2020).

In addition, the financial crisis was assumed to be a watershed to mark when ESG Scores started to become more significant. The lack of a significant change in the impact of ESG Scores between the two periods indicates that other events may have been more significant in explaining the rise to prominence of ESG and its impact on firm performance. As stated in the analysis, different events may have impacted each of the ESG dimensions differently and each dimension may have different corresponding watershed events. It is therefore desirable

45 that future research focuses on different events that may have had an impact on the growth in importance of ESG, such as key legislative milestones that have occurred since the crisis or natural disasters.

Furthermore, this study utilises a quantitative research method to analyse the relationship between ESG Scores and firm performance. It is possible that this methodology limits the scope of this thesis in terms of both the results, and the ability to explore alternative theories. A qualitative approach could explore new theories regarding why certain ESG pillars were significant determinants of firm performance while others were not. A qualitative methodology could provide a deeper depth to and understanding of the phenomena explored in the present study.

Another limitation of the present study is that the study did not explore individual sectors. Of particular interest is the finance sector, due to the important role it played in the 2008 financial crisis and the significance of ESG developments in the sector as a result. Due to the sample only containing 40 firms operating in the finance sector from the STOXX Europe 600 index, tests were not conducted on the finance sector alone in the present study, however, this is highlighted as a potential avenue for future research in Section 6.1 below.

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6. Conclusion Overall, the results of the study indicated that for the most part, ESG Scores, and thus ESG performance, are indeed positively correlated with firm performance. There is, however, stronger support from the study regarding the relationship between ESG Scores and market- based performance measures, since Tobin’s Q was positively correlated with ESG Scores both before and after the crisis. On the other hand, and surprisingly, ROA ceased to be positively correlated with ESG Scores in the post-crisis period. As a result, three of the four hypotheses related to the first research question introduced in Section 1.2, “How do ESG scores impact firm performance in European firms?” were supported. The findings of the study indicate that investing in ESG-enhancing activities may well be a value enhancing activity, and that improving ESG performance is thus worthwhile for firms to engage in. On the other hand, however, the correlation between ESG performance and firm performance may also be a result of firms which are already performing well having more resources available in order to improve their ESG performance. The results of the study contradict the traditional principal-agent theory perspective of ESG activities amongst firms and are more supportive of the stakeholder theory perspective of ESG, suggesting that for the most part, ESG enhancing activities and ESG Scores themselves are positively correlated with firm performance.

Most surprisingly of all, however, was the finding that the impact of ESG Scores on performance had not increased significantly in the post-crisis period, leading to the fifth and sixth hypotheses, related to the second research question “how has the influence of ESG scores on firm performance in Europe changed in the period after the 2008 financial crisis compared to the period before the crisis?”, to be unsupported. The second research question based on the notion that the importance of ESG in the wider business environment has increased since the 2008 financial crisis. Whilst this may well be the case, the results of this study indicate that if ESG has indeed become more important, the impact that ESG performance has on firm performance has not increased significantly since that time.

Of particular note is the distinction between the pillars of ESG. The Environmental pillar was found to be significantly correlated with both market-based and accounting-based firm performance across both the pre-crisis and post-crisis periods, whilst similarly to overall ESG scores, the Social pillar was significantly correlated in every instance, except for with accounting-based firm performance in the post-crisis period. Meanwhile, Governance was not

47 significantly correlated with either measure of firm performance across both of the study periods. This finding is of particular interest from a stakeholder theory perspective, as stakeholder theory posits that ESG creates value by improving the relationship between the firm and its external stakeholders. The Social and Environmental pillars are more concerned with the external responsibilities of the firm, whereas the Governance pillar is focused on internal criteria. Thus, our findings suggest that, in line with stakeholder theory, the positive impact of ESG Scores on firm performance is related to the external impact that the scores represent and attempt to quantify. Furthermore, from a market-based perspective, the positive correlations between ESG Scores, Environmental scores, and Social scores, and Tobin’s Q suggests that investors are indeed perceiving firms’ performance in these areas as valuable in Europe.

6.1 Suggestions for Future Research In light of the lack of significance of the correlation between ESG Scores and ROA post- crisis, future research could explore this discrepancy. Future research study a more recent study period for the post-crisis period, such as one beginning from 2015, in case the results of the present study were impacted by the events of the financial crisis during the earlier part of the post-crisis study period. In addition, one particular avenue of research could be to revisit the ESG Score and ROA relationship with lagged variables to test for causality. This could provide more insight as to whether the relationship is better explained by slack resources, in the case of ROA being significantly correlated to ESG Scores for the following year, or stakeholder theory, should the relationship follow the opposite sequential pattern (Wang, Dou, & Jia, 2015).

In addition, future research could explore the relationship between ESG and firm performance across other geographical regions to test if the findings hold true. Similarly, studies exploring more recent time periods would also be useful to provide further insight into the latest developments in the importance of the individual pillars of ESG, and their impact on firm performance. This would be particularly insightful as the results of previous literature, conducted across a variety of different time periods, sometimes provide different results to both each other, and the results of the present study (Ahmad, Mobarek, & Roni, 2021; Orlitzky, Schmidt, & Rynes, 2003; Velte, 2017; Wang, Dou, & Jia, 2015). Furthermore, the present study utilised only two measures of firm performance, one accounting-based measure, ROA, and a market-based measure, Tobin’s Q. Future studies

48 could test to see if the findings hold true across other measurements of firm performance, both accounting-based and market-based.

The 2008 financial crisis originated in the finance sector, and similarly, much of the ethical concerns that arose were centred around that sector (Romer & Romer, 2017, p.3072). The present study only included 40 firms operating in the finance sector. Thus, future research could emulate the present study, whilst focussing specifically on firms in the finance sector. Due to the importance of governance in financial services in the context of the 2008 financial crisis (Sun et al, 2011), exploring whether ESG performance in relation to the Governance pillar is correlated with firm performance post-crisis in the sector could be of particular interest.

49

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Appendices Appendix 1- Firms in Samples

Total Sample

Total number of firms observed 1 Anglo American PLC 110 LVMH Moet Hennessy Louis Vuitton SE 2 Abb Ltd 111 AP Moeller - Maersk A/S 3 Anheuser Busch Inbev SA 112 Mediobanca Banca di Credito Finanziario SpA 4 Accor SA 113 Meggitt PLC 5 ACS Actividades de Construccion y Servicios SA 114 Compagnie Generale des Etablissements Michelin SCA 6 Koninklijke Ahold Delhaize NV 115 Marks and Spencer Group PLC 7 Adecco Group AG 116 Merck KGaA 8 Adidas AG 117 Muenchener Rueckversicherungs Gesellschaft AG in Muenchen 9 Aegon NV 118 Nestle SA 10 Airbus SE 119 Norsk Hydro ASA 11 Akzo Nobel NV 120 Nokia Oyj 12 Alfa Laval AB 121 Novartis AG 13 Alstom SA 122 Novo Nordisk A/S 14 Allianz SE 123 Natwest Group PLC 15 Andritz AG 124 Next PLC 16 ASML Holding NV 125 Novozymes A/S 17 Assa Abloy AB 126 OMV AG 18 Atlas Copco AB 127 Orange SA 19 Atlantia SpA 128 L'Oreal SA 20 AXA SA 129 Orkla ASA 21 AstraZeneca PLC 130 Pernod Ricard SA 22 BAE Systems PLC 131 Koninklijke Philips NV 23 Baloise Holding AG 132 Proximus NV 24 Banco BPM SpA 133 Kering SA 25 Barclays PLC 134 Prosiebensat 1 Media SE 26 BASF SE 135 Persimmon PLC 27 British American Tobacco PLC 136 Pearson PLC 28 Bayer AG 137 Publicis Groupe SA 29 Banco Bilbao Vizcaya Argentaria SA 138 Randstad NV 30 Barratt Developments P L C 139 Royal Dutch Shell PLC 31 BillerudKorsnas AB (publ) 140 Renault SA 32 Berkeley Group Holdings PLC 141 Roche Holding AG 33 British Land Company PLC 142 Rolls-Royce Holdings PLC 34 BNP Paribas SA 143 RWE AG 35 Bouygues SA 144 Safran SA 36 BP PLC 145 Sampo plc 37 Bellway PLC 146 Banco Santander SA 38 Credit Agricole SA 147 Sandvik AB 39 Capgemini SE 148 SAP SE 40 Carlsberg A/S 149 Sanofi SA 41 Carrefour SA 150 SBM Offshore NV 42 Castellum AB 151 J Sainsbury PLC 43 Commerzbank AG 152 Schibsted ASA 44 Close Brothers Group PLC 153 Svenska Cellulosa SCA AB 45 Compagnie Financiere Richemont SA 154 Schneider Electric SE 46 Clariant AG 155 Schindler Holding AG 47 Centrica PLC 156 Swisscom AG 48 Cofinimmo SA 157 Scor SE 49 Coloplast A/S 158 Schroders PLC 50 Etablissementen Franz Colruyt NV 159 Skandinaviska Enskilda Banken AB 51 Continental AG 160 Securitas AB 52 Compass Group PLC 161 Sage Group PLC 53 Credit Suisse Group AG 162 Vinci SA 54 Daimler AG 163 Compagnie de Saint Gobain SA

58

55 Dassault Systemes SE 164 Siemens Gamesa Renewable Energy SA 56 Deutsche Bank AG 165 SEGRO PLC 57 Demant A/S 166 SGS SA 58 Dnb ASA 167 Svenska Handelsbanken AB 59 Koninklijke DSM NV 168 Siemens AG 60 DSV Panalpina A/S 169 Signature Aviation PLC 61 Deutsche Telekom AG 170 AB SKF 62 Elekta AB (publ) 171 Swiss Life Holding AG 63 Elisa Oyj 172 Smiths Group PLC 64 Man Group PLC 173 Smith & Nephew PLC 65 Ems Chemie Holding AG 174 Societe Generale SA 66 Enel SpA 175 Solvay SA 67 Eni SpA 176 Sonova Holding AG 68 E.ON SE 177 Swiss Re AG 69 Telefonaktiebolaget LM Ericsson 178 Snam SpA 70 Erste Group Bank AG 179 SSE PLC 71 EssilorLuxottica SA 180 Storebrand ASA 72 Sodexo SA 181 Stellantis NV 73 Fabege AB 182 STMicroelectronics NV 74 Ferguson PLC 183 Straumann Holding AG 75 Fresenius Medical Care AG & Co KGaA 184 Severn Trent PLC 76 Assicurazioni Generali SpA 185 Swedish Match AB 77 Groep Brussel Lambert NV 186 Tate & Lyle PLC 78 Geberit AG 187 Thales SA 79 Gecina SA 188 Telefonica SA 80 Givaudan SA 189 Telenor ASA 81 GN Store Nord A/S 190 Tele2 AB 82 GlaxoSmithKline PLC 191 Telia Company AB 83 Heineken NV 192 Thyssenkrupp AG 84 Henkel AG & Co KgaA 193 Telecom Italia SpA 85 HSBC Holdings PLC 194 Tomra Systems ASA 86 Iberdrola SA 195 Total SE 87 Infineon Technologies AG 196 Travis Perkins PLC 88 InterContinental Hotels Group PLC 197 Trelleborg AB 89 3i Group PLC 198 Terna Rete Elettrica Nazionale SpA 90 ING Groep NV 199 Tesco PLC 91 Intesa Sanpaolo SpA 200 Taylor Wimpey PLC 92 Intertek Group PLC 201 Nokian Tyres plc 93 Industria de Diseno Textil SA 202 UBS Group AG 94 Johnson Matthey PLC 203 Swatch Group AG 95 KBC Groep NV 204 UPM-Kymmene Oyj 96 Kesko Oyj 205 United Utilities Group PLC 97 Kingfisher PLC 206 Veolia Environnement SA 98 Kone Oyj 207 Vivendi SE 99 Kuehne und Nagel International AG 208 Vodafone Group PLC 100 Kingspan Group PLC 209 voestalpine AG 101 Kerry Group PLC 210 Volvo AB 102 Leonardo SpA 211 Volkswagen AG 103 Deutsche Lufthansa AG 212 Vistry Group PLC 104 LafargeHolcim Ltd 213 Vestas Wind Systems A/S 105 Lloyds Banking Group PLC 214 Wienerberger AG 106 Klepierre SA 215 Wolters Kluwer NV 107 Lonza Group AG 216 Wartsila Oyj Abp 108 London Stock Exchange Group PLC 217 Whitbread PLC 109 Lundin Energy AB 218 Zurich Insurance Group AG

59

Pre-crisis Sub-sample

Firms in the pre-crisis Scores Group 1 Anglo American PLC 109 LVMH Moet Hennessy Louis Vuitton SE 2 Abb Ltd 110 AP Moeller - Maersk A/S 3 Anheuser Busch Inbev SA 111 Mediobanca Banca di Credito Finanziario SpA 4 Accor SA 112 Meggitt PLC 5 ACS Actividades de Construccion y Servicios SA 113 Compagnie Generale des Etablissements Michelin SCA 6 Koninklijke Ahold Delhaize NV 114 Marks and Spencer Group PLC 7 Adecco Group AG 115 Merck KGaA 8 Adidas AG 116 Muenchener Rueckversicherungs Gesellschaft AG in Muenchen 9 Aegon NV 117 Nestle SA 10 Airbus SE 118 Norsk Hydro ASA 11 Akzo Nobel NV 119 Nokia Oyj 12 Alfa Laval AB 120 Novartis AG 13 Alstom SA 121 Novo Nordisk A/S 14 Allianz SE 122 Natwest Group PLC 15 Andritz AG 123 Next PLC 16 ASML Holding NV 124 Novozymes A/S 17 Assa Abloy AB 125 OMV AG 18 Atlas Copco AB 126 Orange SA 19 Atlantia SpA 127 L'Oreal SA 20 AXA SA 128 Orkla ASA 21 AstraZeneca PLC 129 Pernod Ricard SA 22 BAE Systems PLC 130 Koninklijke Philips NV 23 Baloise Holding AG 131 Proximus NV 24 Banco BPM SpA 132 Kering SA 25 Barclays PLC 133 Prosiebensat 1 Media SE 26 BASF SE 134 Persimmon PLC 27 British American Tobacco PLC 135 Pearson PLC 28 Bayer AG 136 Publicis Groupe SA 29 Banco Bilbao Vizcaya Argentaria SA 137 Randstad NV 30 Barratt Developments P L C 138 Royal Dutch Shell PLC 31 BillerudKorsnas AB (publ) 139 Renault SA 32 Berkeley Group Holdings PLC 140 Roche Holding AG 33 British Land Company PLC 141 Rolls-Royce Holdings PLC 34 BNP Paribas SA 142 RWE AG 35 Bouygues SA 143 Safran SA 36 BP PLC 144 Sampo plc 37 Bellway PLC 145 Banco Santander SA 38 Credit Agricole SA 146 Sandvik AB 39 Capgemini SE 147 SAP SE 40 Carlsberg A/S 148 Sanofi SA 41 Carrefour SA 149 SBM Offshore NV 42 Castellum AB 150 J Sainsbury PLC 43 Commerzbank AG 151 Schibsted ASA 44 Close Brothers Group PLC 152 Svenska Cellulosa SCA AB 45 Compagnie Financiere Richemont SA 153 Schneider Electric SE 46 Clariant AG 154 Schindler Holding AG 47 Centrica PLC 155 Swisscom AG 48 Cofinimmo SA 156 Scor SE 49 Coloplast A/S 157 Schroders PLC 50 Etablissementen Franz Colruyt NV 158 Skandinaviska Enskilda Banken AB 51 Continental AG 159 Securitas AB 52 Compass Group PLC 160 Sage Group PLC 53 Credit Suisse Group AG 161 Vinci SA 54 Daimler AG 162 Compagnie de Saint Gobain SA

60

55 Dassault Systemes SE 163 Siemens Gamesa Renewable Energy SA 56 Deutsche Bank AG 164 SEGRO PLC 57 Demant A/S 165 SGS SA 58 Dnb ASA 166 Svenska Handelsbanken AB 59 Koninklijke DSM NV 167 Siemens AG 60 DSV Panalpina A/S 168 Signature Aviation PLC 61 Deutsche Telekom AG 169 AB SKF 62 Elekta AB (publ) 170 Swiss Life Holding AG 63 Elisa Oyj 171 Smiths Group PLC 64 Man Group PLC 172 Smith & Nephew PLC 65 Ems Chemie Holding AG 173 Societe Generale SA 66 Enel SpA 174 Solvay SA 67 Eni SpA 175 Sonova Holding AG 68 E.ON SE 176 Swiss Re AG 69 Telefonaktiebolaget LM Ericsson 177 Snam SpA 70 Erste Group Bank AG 178 SSE PLC 71 EssilorLuxottica SA 179 Storebrand ASA 72 Sodexo SA 180 Stellantis NV 73 Fabege AB 181 STMicroelectronics NV 74 Ferguson PLC 182 Straumann Holding AG 75 Fresenius Medical Care AG & Co KGaA 183 Severn Trent PLC 76 Assicurazioni Generali SpA 184 Swedish Match AB 77 Groep Brussel Lambert NV 185 Tate & Lyle PLC 78 Geberit AG 186 Thales SA 79 Gecina SA 187 Telefonica SA 80 Givaudan SA 188 Telenor ASA 81 GN Store Nord A/S 189 Tele2 AB 82 GlaxoSmithKline PLC 190 Telia Company AB 83 Heineken NV 191 Thyssenkrupp AG 84 Henkel AG & Co KgaA 192 Telecom Italia SpA 85 HSBC Holdings PLC 193 Tomra Systems ASA 86 Iberdrola SA 194 Total SE 87 Infineon Technologies AG 195 Travis Perkins PLC 88 InterContinental Hotels Group PLC 196 Trelleborg AB 89 3i Group PLC 197 Tesco PLC 90 ING Groep NV 198 Taylor Wimpey PLC 91 Intesa Sanpaolo SpA 199 Nokian Tyres plc 92 Industria de Diseno Textil SA 200 UBS Group AG 93 Johnson Matthey PLC 201 Swatch Group AG 94 KBC Groep NV 202 UPM-Kymmene Oyj 95 Kesko Oyj 203 United Utilities Group PLC 96 Kingfisher PLC 204 Veolia Environnement SA 97 Kone Oyj 205 Vivendi SE 98 Kuehne und Nagel International AG 206 Vodafone Group PLC 99 Kingspan Group PLC 207 voestalpine AG 100 Kerry Group PLC 208 Volvo AB 101 Leonardo SpA 209 Volkswagen AG 102 Deutsche Lufthansa AG 210 Vistry Group PLC 103 LafargeHolcim Ltd 211 Vestas Wind Systems A/S 104 Lloyds Banking Group PLC 212 Wienerberger AG 105 Klepierre SA 213 Wolters Kluwer NV 106 Lonza Group AG 214 Wartsila Oyj Abp 107 London Stock Exchange Group PLC 215 Whitbread PLC 108 Lundin Energy AB 216 Zurich Insurance Group AG

Pre-crisis Environmental Sub-sample

61

Firms in the pre-crisis Environmental Scores Group 1 Anglo American PLC 79 LVMH Moet Hennessy Louis Vuitton SE 2 Abb Ltd 80 Compagnie Generale des Etablissements Michelin SCA 3 Accor SA 81 Marks and Spencer Group PLC 4 ACS Actividades de Construccion y Servicios SA 82 Merck KGaA 5 Adidas AG 83 Muenchener Rueckversicherungs Gesellschaft AG in Muenchen 6 Aegon NV 84 Nestle SA 7 Airbus SE 85 Norsk Hydro ASA 8 Akzo Nobel NV 86 Nokia Oyj 9 Alstom SA 87 Novartis AG 10 Andritz AG 88 Novo Nordisk A/S 11 ASML Holding NV 89 Natwest Group PLC 12 Atlas Copco AB 90 Next PLC 13 AXA SA 91 Novozymes A/S 14 AstraZeneca PLC 92 OMV AG 15 BAE Systems PLC 93 Orange SA 16 Baloise Holding AG 94 L'Oreal SA 17 Barclays PLC 95 Orkla ASA 18 BASF SE 96 Pernod Ricard SA 19 British American Tobacco PLC 97 Koninklijke Philips NV 20 Bayer AG 98 Proximus NV 21 Banco Bilbao Vizcaya Argentaria SA 99 Kering SA 22 Barratt Developments P L C 100 Pearson PLC 23 BillerudKorsnas AB (publ) 101 Royal Dutch Shell PLC 24 Berkeley Group Holdings PLC 102 Renault SA 25 British Land Company PLC 103 Roche Holding AG 26 BNP Paribas SA 104 Safran SA 27 Bouygues SA 105 Banco Santander SA 28 BP PLC 106 Sandvik AB 29 Bellway PLC 107 Sanofi SA 30 Credit Agricole SA 108 J Sainsbury PLC 31 Carrefour SA 109 Svenska Cellulosa SCA AB 32 Castellum AB 110 Schneider Electric SE 33 Commerzbank AG 111 Schindler Holding AG 34 Clariant AG 112 Swisscom AG 35 Centrica PLC 113 Schroders PLC 36 Coloplast A/S 114 Vinci SA 37 Etablissementen Franz Colruyt NV 115 Compagnie de Saint Gobain SA 38 Continental AG 116 SEGRO PLC 39 Credit Suisse Group AG 117 Svenska Handelsbanken AB 40 Daimler AG 118 Siemens AG 41 Deutsche Bank AG 119 Signature Aviation PLC 42 Dnb ASA 120 AB SKF 43 Koninklijke DSM NV 121 Swiss Life Holding AG 44 Deutsche Telekom AG 122 Smiths Group PLC 45 Man Group PLC 123 Smith & Nephew PLC 46 Ems Chemie Holding AG 124 Solvay SA 47 Enel SpA 125 Swiss Re AG 48 Eni SpA 126 Snam SpA 49 E.ON SE 127 SSE PLC 50 Telefonaktiebolaget LM Ericsson 128 Storebrand ASA 51 EssilorLuxottica SA 129 Stellantis NV 52 Sodexo SA 130 STMicroelectronics NV 53 Fabege AB 131 Severn Trent PLC

62

54 Ferguson PLC 132 Tate & Lyle PLC 55 Fresenius Medical Care AG & Co KGaA 133 Thales SA 56 Geberit AG 134 Telefonica SA 57 Gecina SA 135 Telenor ASA 58 Givaudan SA 136 Thyssenkrupp AG 59 GlaxoSmithKline PLC 137 Telecom Italia SpA 60 Heineken NV 138 Tomra Systems ASA 61 Henkel AG & Co KgaA 139 Total SE 62 HSBC Holdings PLC 140 Travis Perkins PLC 63 Iberdrola SA 141 Trelleborg AB 64 Infineon Technologies AG 142 Tesco PLC 65 3i Group PLC 143 Taylor Wimpey PLC 66 ING Groep NV 144 Nokian Tyres plc 67 Industria de Diseno Textil SA 145 UBS Group AG 68 Johnson Matthey PLC 146 UPM-Kymmene Oyj 69 KBC Groep NV 147 United Utilities Group PLC 70 Kesko Oyj 148 Veolia Environnement SA 71 Kingfisher PLC 149 Vivendi SE 72 Kerry Group PLC 150 Vodafone Group PLC 73 Deutsche Lufthansa AG 151 Volvo AB 74 LafargeHolcim Ltd 152 Volkswagen AG 75 Lloyds Banking Group PLC 153 Vestas Wind Systems A/S 76 Klepierre SA 154 Wienerberger AG 77 Lonza Group AG 155 Wartsila Oyj Abp 78 London Stock Exchange Group PLC 156 Whitbread PLC

63

Post-crisis Sub-sample

Firms in the post-crisis Scores Group 1 Anglo American PLC 107 AP Moeller - Maersk A/S 2 Abb Ltd 108 Mediobanca Banca di Credito Finanziario SpA 3 Anheuser Busch Inbev SA 109 Meggitt PLC 4 Accor SA 110 Compagnie Generale des Etablissements Michelin SCA 5 ACS Actividades de Construccion y Servicios SA 111 Marks and Spencer Group PLC 6 Koninklijke Ahold Delhaize NV 112 Merck KGaA 7 Adecco Group AG 113 Muenchener Rueckversicherungs Gesellschaft AG in Muenchen 8 Adidas AG 114 Nestle SA 9 Aegon NV 115 Norsk Hydro ASA 10 Airbus SE 116 Nokia Oyj 11 Akzo Nobel NV 117 Novartis AG 12 Alfa Laval AB 118 Novo Nordisk A/S 13 Alstom SA 119 Natwest Group PLC 14 Allianz SE 120 Next PLC 15 Andritz AG 121 Novozymes A/S 16 ASML Holding NV 122 OMV AG 17 Assa Abloy AB 123 Orange SA 18 Atlas Copco AB 124 L'Oreal SA 19 Atlantia SpA 125 Orkla ASA 20 AXA SA 126 Pernod Ricard SA 21 AstraZeneca PLC 127 Koninklijke Philips NV 22 BAE Systems PLC 128 Proximus NV 23 Baloise Holding AG 129 Kering SA 24 Banco BPM SpA 130 Prosiebensat 1 Media SE 25 Barclays PLC 131 Persimmon PLC 26 BASF SE 132 Pearson PLC 27 British American Tobacco PLC 133 Publicis Groupe SA 28 Bayer AG 134 Randstad NV 29 Banco Bilbao Vizcaya Argentaria SA 135 Royal Dutch Shell PLC 30 Barratt Developments P L C 136 Renault SA 31 BillerudKorsnas AB (publ) 137 Roche Holding AG 32 Berkeley Group Holdings PLC 138 RWE AG 33 British Land Company PLC 139 Safran SA 34 BNP Paribas SA 140 Sampo plc 35 Bouygues SA 141 Banco Santander SA 36 BP PLC 142 Sandvik AB 37 Bellway PLC 143 SAP SE 38 Credit Agricole SA 144 Sanofi SA 39 Capgemini SE 145 SBM Offshore NV 40 Carlsberg A/S 146 J Sainsbury PLC 41 Carrefour SA 147 Schibsted ASA 42 Castellum AB 148 Svenska Cellulosa SCA AB 43 Commerzbank AG 149 Schneider Electric SE 44 Close Brothers Group PLC 150 Schindler Holding AG 45 Compagnie Financiere Richemont SA 151 Swisscom AG 46 Clariant AG 152 Scor SE 47 Centrica PLC 153 Schroders PLC 48 Cofinimmo SA 154 Skandinaviska Enskilda Banken AB 49 Coloplast A/S 155 Securitas AB 50 Etablissementen Franz Colruyt NV 156 Sage Group PLC 51 Continental AG 157 Vinci SA 52 Compass Group PLC 158 Compagnie de Saint Gobain SA 53 Credit Suisse Group AG 159 Siemens Gamesa Renewable Energy SA

64

54 Daimler AG 160 SEGRO PLC 55 Dassault Systemes SE 161 SGS SA 56 Deutsche Bank AG 162 Svenska Handelsbanken AB 57 Demant A/S 163 Siemens AG 58 Dnb ASA 164 Signature Aviation PLC 59 Koninklijke DSM NV 165 AB SKF 60 DSV Panalpina A/S 166 Swiss Life Holding AG 61 Deutsche Telekom AG 167 Smiths Group PLC 62 Elekta AB (publ) 168 Smith & Nephew PLC 63 Elisa Oyj 169 Societe Generale SA 64 Man Group PLC 170 Solvay SA 65 Ems Chemie Holding AG 171 Sonova Holding AG 66 Enel SpA 172 Swiss Re AG 67 Eni SpA 173 Snam SpA 68 Telefonaktiebolaget LM Ericsson 174 SSE PLC 69 Erste Group Bank AG 175 Storebrand ASA 70 EssilorLuxottica SA 176 Stellantis NV 71 Sodexo SA 177 STMicroelectronics NV 72 Fabege AB 178 Straumann Holding AG 73 Ferguson PLC 179 Severn Trent PLC 74 Fresenius Medical Care AG & Co KGaA 180 Tate & Lyle PLC 75 Assicurazioni Generali SpA 181 Thales SA 76 Groep Brussel Lambert NV 182 Telefonica SA 77 Geberit AG 183 Telenor ASA 78 Gecina SA 184 Tele2 AB 79 Givaudan SA 185 Telia Company AB 80 GN Store Nord A/S 186 Thyssenkrupp AG 81 Heineken NV 187 Telecom Italia SpA 82 Henkel AG & Co KgaA 188 Tomra Systems ASA 83 HSBC Holdings PLC 189 Total SE 84 Iberdrola SA 190 Travis Perkins PLC 85 Infineon Technologies AG 191 Trelleborg AB 86 3i Group PLC 192 Terna Rete Elettrica Nazionale SpA 87 ING Groep NV 193 Tesco PLC 88 Intesa Sanpaolo SpA 194 Taylor Wimpey PLC 89 Intertek Group PLC 195 Nokian Tyres plc 90 Industria de Diseno Textil SA 196 UBS Group AG 91 Johnson Matthey PLC 197 Swatch Group AG 92 KBC Groep NV 198 UPM-Kymmene Oyj 93 Kesko Oyj 199 United Utilities Group PLC 94 Kingfisher PLC 200 Veolia Environnement SA 95 Kone Oyj 201 Vivendi SE 96 Kuehne und Nagel International AG 202 Vodafone Group PLC 97 Kingspan Group PLC 203 voestalpine AG 98 Kerry Group PLC 204 Volvo AB 99 Leonardo SpA 205 Volkswagen AG 100 Deutsche Lufthansa AG 206 Vistry Group PLC 101 LafargeHolcim Ltd 207 Vestas Wind Systems A/S 102 Lloyds Banking Group PLC 208 Wienerberger AG 103 Klepierre SA 209 Wolters Kluwer NV 104 Lonza Group AG 210 Wartsila Oyj Abp 105 London Stock Exchange Group PLC 211 Whitbread PLC 106 LVMH Moet Hennessy Louis Vuitton SE 212 Zurich Insurance Group AG

65

Post-crisis Environmental Sub-sample

Firms in the post-crisis Environmental Scores Group 1 Anglo American PLC 104 AP Moeller - Maersk A/S 2 Abb Ltd 105 Meggitt PLC 3 Anheuser Busch Inbev SA 106 Compagnie Generale des Etablissements Michelin SCA 4 Accor SA 107 Marks and Spencer Group PLC 5 ACS Actividades de Construccion y Servicios SA 108 Merck KGaA 6 Koninklijke Ahold Delhaize NV 109 Muenchener Rueckversicherungs Gesellschaft AG in Muenchen 7 Adecco Group AG 110 Nestle SA 8 Adidas AG 111 Norsk Hydro ASA 9 Airbus SE 112 Nokia Oyj 10 Akzo Nobel NV 113 Novartis AG 11 Alfa Laval AB 114 Novo Nordisk A/S 12 Alstom SA 115 Natwest Group PLC 13 Allianz SE 116 Next PLC 14 Andritz AG 117 Novozymes A/S 15 ASML Holding NV 118 OMV AG 16 Assa Abloy AB 119 Orange SA 17 Atlas Copco AB 120 L'Oreal SA 18 Atlantia SpA 121 Orkla ASA 19 AXA SA 122 Pernod Ricard SA 20 AstraZeneca PLC 123 Koninklijke Philips NV 21 BAE Systems PLC 124 Proximus NV 22 Baloise Holding AG 125 Kering SA 23 Barclays PLC 126 Persimmon PLC 24 BASF SE 127 Pearson PLC 25 British American Tobacco PLC 128 Publicis Groupe SA 26 Bayer AG 129 Randstad NV 27 Banco Bilbao Vizcaya Argentaria SA 130 Royal Dutch Shell PLC 28 Barratt Developments P L C 131 Renault SA 29 BillerudKorsnas AB (publ) 132 Roche Holding AG 30 Berkeley Group Holdings PLC 133 RWE AG 31 British Land Company PLC 134 Safran SA 32 BNP Paribas SA 135 Banco Santander SA 33 Bouygues SA 136 Sandvik AB 34 BP PLC 137 SAP SE 35 Bellway PLC 138 Sanofi SA 36 Credit Agricole SA 139 SBM Offshore NV 37 Capgemini SE 140 J Sainsbury PLC 38 Carlsberg A/S 141 Schibsted ASA 39 Carrefour SA 142 Svenska Cellulosa SCA AB 40 Castellum AB 143 Schneider Electric SE 41 Commerzbank AG 144 Schindler Holding AG 42 Close Brothers Group PLC 145 Swisscom AG 43 Compagnie Financiere Richemont SA 146 Scor SE 44 Clariant AG 147 Schroders PLC 45 Centrica PLC 148 Skandinaviska Enskilda Banken AB 46 Cofinimmo SA 149 Securitas AB 47 Coloplast A/S 150 Sage Group PLC 48 Etablissementen Franz Colruyt NV 151 Vinci SA 49 Continental AG 152 Compagnie de Saint Gobain SA 50 Compass Group PLC 153 Siemens Gamesa Renewable Energy SA 51 Credit Suisse Group AG 154 SEGRO PLC 52 Daimler AG 155 SGS SA 53 Dassault Systemes SE 156 Svenska Handelsbanken AB

66

54 Deutsche Bank AG 157 Siemens AG 55 Demant A/S 158 Signature Aviation PLC 56 Dnb ASA 159 AB SKF 57 Koninklijke DSM NV 160 Swiss Life Holding AG 58 DSV Panalpina A/S 161 Smiths Group PLC 59 Deutsche Telekom AG 162 Smith & Nephew PLC 60 Elekta AB (publ) 163 Societe Generale SA 61 Elisa Oyj 164 Solvay SA 62 Man Group PLC 165 Sonova Holding AG 63 Ems Chemie Holding AG 166 Swiss Re AG 64 Enel SpA 167 Snam SpA 65 Eni SpA 168 SSE PLC 66 Telefonaktiebolaget LM Ericsson 169 Storebrand ASA 67 Erste Group Bank AG 170 Stellantis NV 68 EssilorLuxottica SA 171 STMicroelectronics NV 69 Sodexo SA 172 Straumann Holding AG 70 Fabege AB 173 Severn Trent PLC 71 Ferguson PLC 174 Tate & Lyle PLC 72 Fresenius Medical Care AG & Co KGaA 175 Thales SA 73 Assicurazioni Generali SpA 176 Telefonica SA 74 Geberit AG 177 Telenor ASA 75 Gecina SA 178 Tele2 AB 76 Givaudan SA 179 Telia Company AB 77 GN Store Nord A/S 180 Thyssenkrupp AG 78 Heineken NV 181 Telecom Italia SpA 79 Henkel AG & Co KgaA 182 Tomra Systems ASA 80 HSBC Holdings PLC 183 Total SE 81 Iberdrola SA 184 Travis Perkins PLC 82 Infineon Technologies AG 185 Trelleborg AB 83 3i Group PLC 186 Terna Rete Elettrica Nazionale SpA 84 ING Groep NV 187 Tesco PLC 85 Intesa Sanpaolo SpA 188 Taylor Wimpey PLC 86 Intertek Group PLC 189 Nokian Tyres plc 87 Industria de Diseno Textil SA 190 UBS Group AG 88 Johnson Matthey PLC 191 Swatch Group AG 89 KBC Groep NV 192 UPM-Kymmene Oyj 90 Kesko Oyj 193 United Utilities Group PLC 91 Kingfisher PLC 194 Veolia Environnement SA 92 Kone Oyj 195 Vivendi SE 93 Kuehne und Nagel International AG 196 Vodafone Group PLC 94 Kingspan Group PLC 197 voestalpine AG 95 Kerry Group PLC 198 Volvo AB 96 Leonardo SpA 199 Vistry Group PLC 97 Deutsche Lufthansa AG 200 Vestas Wind Systems A/S 98 LafargeHolcim Ltd 201 Wienerberger AG 99 Lloyds Banking Group PLC 202 Wolters Kluwer NV 100 Klepierre SA 203 Wartsila Oyj Abp 101 Lonza Group AG 204 Whitbread PLC 102 London Stock Exchange Group PLC 205 Zurich Insurance Group AG 103 LVMH Moet Hennessy Louis Vuitton SE

67

Appendix 2- Correlation Matrices

Pre-crisis ESG Scores

(obs=864)

ESGCom~e ROA Dividend Debt LogAss~s

ESGCombine~e 1.0000 ROA -0.0956 1.0000 Dividend 0.1972 -0.1912 1.0000 Debt -0.0011 -0.1823 -0.0268 1.0000 LogAssets 0.3706 -0.4886 0.2616 0.1725 1.0000 ESG Combined Score refers to ESG Score. This applies throughout the appendix. LogMTB refers to Tobin’s Q throughout the appendix.

Pre-crisis Environmental

(obs=624)

Enviro~l ROA Dividend Debt LogAss~s

Environmen~l 1.0000 ROA 0.0891 1.0000 Dividend 0.1003 -0.1296 1.0000 Debt -0.0583 -0.2044 -0.0396 1.0000 LogAssets 0.0732 -0.4324 0.2068 0.1677 1.0000

Pre-crisis Social

(obs=864)

Social ROA Dividend Debt LogAss~s

Social 1.0000 ROA -0.0968 1.0000 Dividend 0.1507 -0.1912 1.0000 Debt 0.0322 -0.1823 -0.0268 1.0000 LogAssets 0.3287 -0.4886 0.2616 0.1725 1.0000

68

Pre-crisis Governance

(obs=864)

Govern~e ROA Dividend Debt LogAss~s

Governance 1.0000 ROA -0.0927 1.0000 Dividend 0.1486 -0.1912 1.0000 Debt 0.0702 -0.1823 -0.0268 1.0000 LogAssets 0.2945 -0.4886 0.2616 0.1725 1.0000

Post-crisis ESG Scores

(obs=2,120)

ESGCom~e ROA Dividend Debt LogAss~s

ESGCombine~e 1.0000 ROA -0.0201 1.0000 Dividend 0.1111 -0.1340 1.0000 Debt -0.0654 -0.3010 0.0911 1.0000 LogAssets 0.0880 -0.4821 0.2249 0.4352 1.0000

Post-crisis Environmental

(obs=2,050)

Enviro~l ROA Dividend Debt LogAss~s

Environmen~l 1.0000 ROA -0.1695 1.0000 Dividend 0.1945 -0.1356 1.0000 Debt 0.1555 -0.3006 0.0971 1.0000 LogAssets 0.4569 -0.4767 0.2339 0.4471 1.0000

Post-crisis Social

(obs=2,120)

Social ROA Dividend Debt LogAss~s

Social 1.0000 ROA -0.0615 1.0000 Dividend 0.0744 -0.1340 1.0000 Debt 0.0456 -0.3010 0.0911 1.0000 LogAssets 0.3253 -0.4821 0.2249 0.4352 1.0000

69

Post-crisis Governance

(obs=2,120)

Govern~e ROA Dividend Debt LogAss~s

Governance 1.0000 ROA -0.1890 1.0000 Dividend 0.1533 -0.1340 1.0000 Debt 0.0657 -0.3010 0.0911 1.0000 LogAssets 0.3381 -0.4821 0.2249 0.4352 1.0000

70

Appendix 3 – Normality test on error terms Pre-crisis ESG Scores - ROA

Shapiro-Wilk W test for normal data

Variable Obs W V z Prob>z

residual_res 864 0.97606 13.216 6.354 0.00000

20

15

10

Density

5 0 -.05 0 .05 .1 .15 Linear prediction

Linear prediction

Percentiles Smallest 1% -.0053151 -.0312382 5% .0036011 -.0197256 10% .0200303 -.0087408 Obs 864 25% .0436052 -.0085038 Sum of Wgt. 864

50% .0619424 Mean .0586435 Largest Std. Dev. .0267674 75% .0776206 .1133552 90% .0891389 .1141839 Variance .0007165 95% .0974319 .1145973 Skewness -.5121256 99% .1103522 .1156213 Kurtosis 2.933271

71

Pre-crisis ESG Scores Tobin’s Q

Shapiro-Wilk W test for normal data

Variable Obs W V z Prob>z

residual_r~2 864 0.95003 27.590 8.166 0.00000

1.5

1

Density

.5 0 -1 0 1 2 3 4 Linear prediction

Linear prediction

Percentiles Smallest 1% .2803959 -.4936805 5% .4700422 -.4931521 10% .5594966 -.4258214 Obs 864 25% .7772088 -.2973688 Sum of Wgt. 864

50% .9687366 Mean .9806764 Largest Std. Dev. .3551984 75% 1.183635 2.252502 90% 1.389256 2.273146 Variance .1261659 95% 1.523606 2.31177 Skewness .792395 99% 2.065056 3.962148 Kurtosis 9.725234

Pre-crisis Environmental - ROA

72

Shapiro-Wilk W test for normal data

Variable Obs W V z Prob>z

residual_r~2 624 0.97075 12.020 6.036 0.00000

25

20

15

Density

10

5 0 -.05 0 .05 .1 Linear prediction

Linear prediction

Percentiles Smallest 1% .0005902 -.0264636 5% .0100596 -.0051197 10% .0250314 -.0032244 Obs 624 25% .0425445 -.0022288 Sum of Wgt. 624

50% .0585461 Mean .0553105 Largest Std. Dev. .0220405 75% .0697231 .1034738 90% .0807588 .1042659 Variance .0004858 95% .0864479 .1068341 Skewness -.6037776 99% .0977516 .1068819 Kurtosis 3.230375

.

73

Pre-crisis Environmental – Tobin’s Q

Shapiro-Wilk W test for normal data

Variable Obs W V z Prob>z

residual_res 624 0.92173 32.164 8.426 0.00000

1.5

1

Density

.5 0 0 1 2 3 4 Linear prediction

Linear prediction

Percentiles Smallest 1% .3035108 -.3815802 5% .462976 -.3618491 10% .5457358 -.3529176 Obs 624 25% .7410502 -.2250486 Sum of Wgt. 624

50% .905984 Mean .9153661 Largest Std. Dev. .3132663 75% 1.089648 1.660594 90% 1.272303 1.673094 Variance .0981358 95% 1.404359 1.70271 Skewness 1.058167 99% 1.610904 3.861851 Kurtosis 15.86176

74

Pre-crisis Social - ROA

Shapiro-Wilk W test for normal data

Variable Obs W V z Prob>z

residual_r~3 864 0.97691 12.747 6.265 0.00000

20

15

10

Density

5 0 -.05 0 .05 .1 .15 Linear prediction

Linear prediction

Percentiles Smallest 1% -.0053536 -.0295364 5% .0040271 -.0156946 10% .0210352 -.0100513 Obs 864 25% .0428549 -.0096477 Sum of Wgt. 864

50% .0618039 Mean .0586435 Largest Std. Dev. .0266674 75% .0779405 .1144335 90% .0893724 .1149236 Variance .0007111 95% .0965807 .1168961 Skewness -.4973584 99% .1120283 .1181745 Kurtosis 2.919702

75

Pre-crisis Social – Tobin’s Q

Shapiro-Wilk W test for normal data

Variable Obs W V z Prob>z

residual_r~4 864 0.95009 27.557 8.163 0.00000

1.5

1

Density

.5 0 -1 0 1 2 3 4 Linear prediction

Linear prediction

Percentiles Smallest 1% .3014348 -.5333793 5% .4620505 -.4566047 10% .5649689 -.4102681 Obs 864 25% .7724246 -.2044834 Sum of Wgt. 864

50% .9717023 Mean .9806764 Largest Std. Dev. .3570976 75% 1.185353 2.277359 90% 1.388206 2.279057 Variance .1275187 95% 1.516357 2.348211 Skewness .8222381 99% 2.099915 3.989434 Kurtosis 9.757863

76

Pre-crisis Governance - ROA

Shapiro-Wilk W test for normal data

Variable Obs W V z Prob>z

residual_r~5 864 0.97767 12.331 6.184 0.00000

20

15

10

Density

5 0 -.05 0 .05 .1 .15 Linear prediction

Linear prediction

Percentiles Smallest 1% -.0042916 -.0295754 5% .0049577 -.0174003 10% .0219923 -.0097679 Obs 864 25% .0449167 -.0096927 Sum of Wgt. 864

50% .0614685 Mean .0586435 Largest Std. Dev. .0263932 75% .0774288 .1133888 90% .0888663 .1134307 Variance .0006966 95% .097153 .1135647 Skewness -.5012233 99% .1100314 .1157163 Kurtosis 2.939725

77

Pre-crisis Governance – Tobin’s Q

Shapiro-Wilk W test for normal data

Variable Obs W V z Prob>z

residual_r~6 864 0.94392 30.962 8.450 0.00000

1.5

1

Density

.5 0 -1 0 1 2 3 4 Linear prediction

Linear prediction

Percentiles Smallest 1% .2916833 -.5272849 5% .4719306 -.4772912 10% .5605226 -.448938 Obs 864 25% .7749504 -.2649391 Sum of Wgt. 864

50% .9655612 Mean .9806764 Largest Std. Dev. .3512175 75% 1.181386 2.317866 90% 1.390479 2.346506 Variance .1233538 95% 1.518233 2.366255 Skewness .8670034 99% 2.074913 3.970701 Kurtosis 10.32986

78

Post-crisis ESG Scores - ROA

Shapiro-Wilk W test for normal data

Variable Obs W V z Prob>z

residual_res 2,120 0.92343 95.755 11.629 0.00000

20

15

Density

10

5 0 -.1 -.05 0 .05 .1 Linear prediction

Linear prediction

Percentiles Smallest 1% -.0298827 -.073573 5% -.0001545 -.0676159 10% .0178788 -.0675092 Obs 2,120 25% .0391135 -.0602154 Sum of Wgt. 2,120

50% .0565355 Mean .0516755 Largest Std. Dev. .0255765 75% .0696195 .0976596 90% .0781112 .0980342 Variance .0006542 95% .0839262 .0987172 Skewness -1.21187 99% .0913031 .1001189 Kurtosis 4.956347

79

Post-crisis ESG Scores – Tobin’s Q

Shapiro-Wilk W test for normal data

Variable Obs W V z Prob>z

residual_r~2 2,120 0.99386 7.678 5.196 0.00000

1

.8

.6

Density

.4

.2 0 -.5 0 .5 1 1.5 2 Linear prediction

Linear prediction

Percentiles Smallest 1% -.4057196 -.5679263 5% -.1407405 -.5418049 10% .0383851 -.5368088 Obs 2,120 25% .3848254 -.5190672 Sum of Wgt. 2,120

50% .7466786 Mean .7147205 Largest Std. Dev. .4831075 75% 1.053132 2.198801 90% 1.272263 2.199576 Variance .2333928 95% 1.481546 2.203249 Skewness -.0836194 99% 1.918639 2.235205 Kurtosis 2.967314

80

Post-crisis Environmental - ROA

Shapiro-Wilk W test for normal data

Variable Obs W V z Prob>z

residual_res 2,050 0.91036 108.731 11.938 0.00000

20

15

10

Density

5 0 -.1 -.05 0 .05 .1 Linear prediction

Linear prediction

Percentiles Smallest 1% -.0333111 -.074879 5% -.0017807 -.0673655 10% .0172735 -.0670735 Obs 2,050 25% .0416479 -.0669423 Sum of Wgt. 2,050

50% .0572441 Mean .0525835 Largest Std. Dev. .0259418 75% .0710874 .0982511 90% .0793167 .0996128 Variance .000673 95% .0833393 .0996596 Skewness -1.325379 99% .0932626 .103202 Kurtosis 5.343903

81

Post-crisis Environmental – Tobin’s Q

Shapiro-Wilk W test for normal data

Variable Obs W V z Prob>z

residual_r~2 2,050 0.99257 9.009 5.597 0.00000

1

.8

.6

Density

.4

.2 0 -.5 0 .5 1 1.5 2 Linear prediction

Linear prediction

Percentiles Smallest 1% -.3188981 -.5141855 5% -.0861163 -.4395626 10% .1111424 -.4222379 Obs 2,050 25% .4582409 -.4212441 Sum of Wgt. 2,050

50% .7674852 Mean .7408893 Largest Std. Dev. .4563858 75% 1.0516 2.171134 90% 1.270018 2.177133 Variance .208288 95% 1.452997 2.178025 Skewness -.0821726 99% 1.899879 2.183218 Kurtosis 3.083362

82

Post-crisis Social - ROA

Shapiro-Wilk W test for normal data

Variable Obs W V z Prob>z

residual_r~3 2,120 0.92207 97.451 11.674 0.00000

20

15

Density 10

5 0 -.1 -.05 0 .05 .1 Linear prediction

Linear prediction

Percentiles Smallest 1% -.0301932 -.0748128 5% -.0022299 -.0651328 10% .0162697 -.0648988 Obs 2,120 25% .0391972 -.062057 Sum of Wgt. 2,120

50% .0568207 Mean .0516755 Largest Std. Dev. .0264691 75% .0704553 .0980239 90% .0786482 .0984134 Variance .0007006 95% .0840089 .100756 Skewness -1.190281 99% .093568 .101628 Kurtosis 4.726593

83

Post-crisis Social – Tobin’s Q

Shapiro-Wilk W test for normal data

Variable Obs W V z Prob>z

residual_r~4 2,120 0.99374 7.832 5.247 0.00000

1

.8

.6

Density

.4

.2 0 -1 0 1 2 Linear prediction

Linear prediction

Percentiles Smallest 1% -.4197291 -.61856 5% -.1640847 -.5885612 10% .0352695 -.5842336 Obs 2,120 25% .3873136 -.544046 Sum of Wgt. 2,120

50% .7488228 Mean .7147205 Largest Std. Dev. .4924349 75% 1.054885 2.228023 90% 1.285242 2.275416 Variance .2424922 95% 1.48323 2.284365 Skewness -.0837305 99% 1.960824 2.291389 Kurtosis 3.018447

84

Post-crisis Governance - ROA

Shapiro-Wilk W test for normal data

Variable Obs W V z Prob>z

residual_r~5 2,120 0.92476 94.092 11.584 0.00000

20

15

10

Density

5 0 -.1 -.05 0 .05 .1 Linear prediction

Linear prediction

Percentiles Smallest 1% -.0291713 -.0732781 5% .0000928 -.0677464 10% .0182607 -.0675206 Obs 2,120 25% .0391476 -.0603978 Sum of Wgt. 2,120

50% .0564398 Mean .0516755 Largest Std. Dev. .0254641 75% .069663 .0978632 90% .0778347 .0982317 Variance .0006484 95% .0839082 .0989147 Skewness -1.20619 99% .0911223 .1009027 Kurtosis 4.973462

85

Post-crisis Governance – Tobin’s Q

Shapiro-Wilk W test for normal data

Variable Obs W V z Prob>z

residual_r~6 2,120 0.99476 6.554 4.793 0.00000

1

.8

.6

Density

.4

.2 0 -.5 0 .5 1 1.5 2 Linear prediction

Linear prediction

Percentiles Smallest 1% -.3512438 -.5106897 5% -.1096788 -.4956154 10% .0718043 -.4825728 Obs 2,120 25% .4161701 -.4679322 Sum of Wgt. 2,120

50% .7372414 Mean .7147205 Largest Std. Dev. .4671119 75% 1.037132 2.11501 90% 1.26331 2.180182 Variance .2181935 95% 1.455195 2.194763 Skewness -.0206145 99% 1.91732 2.205736 Kurtosis 3.052043

86

Appendix 4 - Descriptive Statistics

Descriptive Statistics Pre-crisis Normal Sample

Variable Obs Mean Std. Dev. Min Max

ESGCombine~e 864 42.0995 15.85572 3.546238 84.87546 Social 864 43.4389 19.9634 2.561475 98.02899 Governance 864 51.87991 22.35068 2.407407 98.36987 ROA 864 .0586435 .0562715 -.1274835 .3360288 LogMTB 864 .9806764 .649235 -.5595566 4.717066

. Descriptive statistics pre-crisis Environmental sample

Variable Obs Mean Std. Dev. Min Max

Environmen~l 624 42.25871 21.675 .5681818 94.03922 ROA 624 .0553105 .0511658 -.1274835 .2544894 LogMTB 624 .9153661 .6180903 -.5595566 4.717066 Descriptive statistics post-crisis normal sample

Variable Obs Mean Std. Dev. Min Max

ESGCombine~e 2,120 61.77198 15.88709 2.843408 93.51074 Social 2,120 70.58413 19.11256 2.380129 98.62769 Governance 2,120 61.39667 21.29244 6.641975 98.30409 ROA 2,120 .0516755 .0562161 -.2151975 .412884 LogMTB 2,120 .7147205 .8146786 -1.95021 3.586644

Descriptive statistics post crisis Environmental sample

Variable Obs Mean Std. Dev. Min Max

Environmen~l 2,050 70.47785 19.53371 .770077 99.10245 ROA 2,050 .0525835 .0567233 -.2151975 .412884 LogMTB 2,050 .7408893 .7965815 -1.926973 3.586644

87

Appendix 5 – Regressions on ROA Pre-crisis

Return on Assets ESG Scores

Random-effects GLS regression Number of obs = 864 Group variable: FirmID Number of groups = 216

R-sq: Obs per group: within = 0.0087 min = 4 between = 0.3236 avg = 4.0 overall = 0.2589 max = 4

Wald chi2(4) = 199.45 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

(Std. Err. adjusted for 216 clusters in FirmID)

Robust ROA Coef. Std. Err. z P>|z| [95% Conf. Interval]

ESGCombined~e .0002235 .0000944 2.37 0.018 .0000384 .0004085 Dividend -.1271601 .0836338 -1.52 0.128 -.2910793 .0367592 Debt -.0004758 .0000565 -8.43 0.000 -.0005864 -.0003651 LogAssets -.0139892 .0018088 -7.73 0.000 -.0175344 -.0104439 _cons .3809934 .0436453 8.73 0.000 .2954501 .4665367

sigma_u .03871044 sigma_e .02909804 rho .63896557 (fraction of variance due to u_i)

Environmental

88

Random-effects GLS regression Number of obs = 624 Group variable: FirmID Number of groups = 156

R-sq: Obs per group: within = 0.0213 min = 4 between = 0.2678 avg = 4.0 overall = 0.2166 max = 4

Wald chi2(4) = 188.81 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

(Std. Err. adjusted for 156 clusters in FirmID)

Robust ROA Coef. Std. Err. z P>|z| [95% Conf. Interval]

Environmental .0002208 .0000683 3.23 0.001 .0000869 .0003547 Dividend -.0912958 .0741956 -1.23 0.219 -.2367165 .0541249 Debt -.0004353 .0000549 -7.94 0.000 -.0005429 -.0003278 LogAssets -.0114043 .0016949 -6.73 0.000 -.0147262 -.0080824 _cons .3195491 .0411729 7.76 0.000 .2388517 .4002465

sigma_u .03673057 sigma_e .02650246 rho .65762846 (fraction of variance due to u_i)

Social

Random-effects GLS regression Number of obs = 864 Group variable: FirmID Number of groups = 216

R-sq: Obs per group: within = 0.0087 min = 4 between = 0.3196 avg = 4.0 overall = 0.2557 max = 4

Wald chi2(4) = 185.82 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

(Std. Err. adjusted for 216 clusters in FirmID)

Robust ROA Coef. Std. Err. z P>|z| [95% Conf. Interval]

Social .0001493 .0000721 2.07 0.038 7.96e-06 .0002905 Dividend -.1219419 .0840021 -1.45 0.147 -.2865829 .0426991 Debt -.0004831 .0000582 -8.30 0.000 -.0005972 -.0003691 LogAssets -.0138004 .001832 -7.53 0.000 -.017391 -.0102098 _cons .3793596 .0439521 8.63 0.000 .2932151 .465504

sigma_u .03887601 sigma_e .02909754 rho .64094017 (fraction of variance due to u_i)

89

Governance

Random-effects GLS regression Number of obs = 864 Group variable: FirmID Number of groups = 216

R-sq: Obs per group: within = 0.0060 min = 4 between = 0.3173 avg = 4.0 overall = 0.2531 max = 4

Wald chi2(4) = 181.27 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

(Std. Err. adjusted for 216 clusters in FirmID)

Robust ROA Coef. Std. Err. z P>|z| [95% Conf. Interval]

Governance .0000439 .0000752 0.58 0.559 -.0001035 .0001913 Dividend -.1200817 .0840424 -1.43 0.153 -.2848018 .0446385 Debt -.0005001 .000059 -8.48 0.000 -.0006157 -.0003845 LogAssets -.0133486 .0017604 -7.58 0.000 -.0167988 -.0098983 _cons .3729659 .0432523 8.62 0.000 .2881929 .4577388

sigma_u .03883164 sigma_e .02912899 rho .63991672 (fraction of variance due to u_i)

90

Appendix 6 – Regressions on ROA Post-crisis

Return on Assets ESG Scores

Random-effects GLS regression Number of obs = 2,120 Group variable: FirmID Number of groups = 212

R-sq: Obs per group: within = 0.0254 min = 10 between = 0.3090 avg = 10.0 overall = 0.2314 max = 10

Wald chi2(4) = 98.85 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

(Std. Err. adjusted for 212 clusters in FirmID)

Robust ROA Coef. Std. Err. z P>|z| [95% Conf. Interval]

ESGCombined~e .0000155 .0000834 0.19 0.853 -.000148 .0001789 Dividend .037646 .0924751 0.41 0.684 -.1436018 .2188938 Debt -.0084043 .0018956 -4.43 0.000 -.0121196 -.0046889 LogAssets -.0104524 .0015313 -6.83 0.000 -.0134537 -.0074511 _cons .3088881 .0373219 8.28 0.000 .2357385 .3820376

sigma_u .03842172 sigma_e .03051687 rho .61317695 (fraction of variance due to u_i)

Environmental

91

Random-effects GLS regression Number of obs = 2,050 Group variable: FirmID Number of groups = 205

R-sq: Obs per group: within = 0.0300 min = 10 between = 0.3003 avg = 10.0 overall = 0.2259 max = 10

Wald chi2(4) = 91.48 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

(Std. Err. adjusted for 205 clusters in FirmID)

Robust ROA Coef. Std. Err. z P>|z| [95% Conf. Interval]

Environmental .0002469 .0001222 2.02 0.043 7.26e-06 .0004865 Dividend .0363004 .0945363 0.38 0.701 -.1489874 .2215882 Debt -.0084429 .0019568 -4.31 0.000 -.0122782 -.0046077 LogAssets -.0115561 .0017072 -6.77 0.000 -.0149022 -.0082099 _cons .3193292 .0389946 8.19 0.000 .2429011 .3957572

sigma_u .03887281 sigma_e .03083761 rho .61375401 (fraction of variance due to u_i)

Social

92

Random-effects GLS regression Number of obs = 2,120 Group variable: FirmID Number of groups = 212

R-sq: Obs per group: within = 0.0270 min = 10 between = 0.3202 avg = 10.0 overall = 0.2400 max = 10

Wald chi2(4) = 101.97 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

(Std. Err. adjusted for 212 clusters in FirmID)

Robust ROA Coef. Std. Err. z P>|z| [95% Conf. Interval]

Social .0001822 .0001162 1.57 0.117 -.0000456 .0004099 Dividend .0402515 .0923509 0.44 0.663 -.1407529 .2212559 Debt -.0080361 .0018519 -4.34 0.000 -.0116658 -.0044064 LogAssets -.0116514 .0015914 -7.32 0.000 -.0147706 -.0085323 _cons .3252173 .0369176 8.81 0.000 .25286 .3975745

sigma_u .03810819 sigma_e .03050807 rho .60942026 (fraction of variance due to u_i)

Governance

Random-effects GLS regression Number of obs = 2,120 Group variable: FirmID Number of groups = 212

R-sq: Obs per group: within = 0.0254 min = 10 between = 0.3097 avg = 10.0 overall = 0.2319 max = 10

Wald chi2(4) = 95.81 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

(Std. Err. adjusted for 212 clusters in FirmID)

Robust ROA Coef. Std. Err. z P>|z| [95% Conf. Interval]

Governance -.0000219 .0000769 -0.28 0.776 -.0001727 .0001289 Dividend .0383303 .0926635 0.41 0.679 -.1432868 .2199473 Debt -.0084376 .0018932 -4.46 0.000 -.0121481 -.004727 LogAssets -.0102804 .0015849 -6.49 0.000 -.0133868 -.007174 _cons .3070794 .0375087 8.19 0.000 .2335638 .3805951

sigma_u .03837343 sigma_e .03051881 rho .61255004 (fraction of variance due to u_i)

93

Appendix 7 – Regressions on Tobin’s Q Pre-crisis ESG Scores

Random-effects GLS regression Number of obs = 864 Group variable: FirmID Number of groups = 216

R-sq: Obs per group: within = 0.2024 min = 4 between = 0.4999 avg = 4.0 overall = 0.4430 max = 4

Wald chi2(5) = 705.67 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

(Std. Err. adjusted for 216 clusters in FirmID)

Robust LogMTB Coef. Std. Err. z P>|z| [95% Conf. Interval]

ESGCombined~e .0030477 .0010842 2.81 0.005 .0009227 .0051727 ROA 3.129938 .4977739 6.29 0.000 2.154319 4.105557 Dividend -8.0588 3.413385 -2.36 0.018 -14.74891 -1.368687 Debt .0184364 .0007569 24.36 0.000 .016953 .0199199 LogAssets -.0854301 .01839 -4.65 0.000 -.1214739 -.0493864 _cons 2.878611 .3966263 7.26 0.000 2.101238 3.655984

sigma_u .3758175 sigma_e .26897614 rho .66127068 (fraction of variance due to u_i)

Environmental Scores

94

Random-effects GLS regression Number of obs = 624 Group variable: FirmID Number of groups = 156

R-sq: Obs per group: within = 0.2295 min = 4 between = 0.4494 avg = 4.0 overall = 0.3972 max = 4

Wald chi2(5) = 2107.80 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

(Std. Err. adjusted for 156 clusters in FirmID)

Robust LogMTB Coef. Std. Err. z P>|z| [95% Conf. Interval]

Environmental .0019056 .0009619 1.98 0.048 .0000203 .0037908 ROA 3.341812 .6231032 5.36 0.000 2.120553 4.563072 Dividend -7.165747 3.558284 -2.01 0.044 -14.13986 -.1916375 Debt .018264 .0004951 36.89 0.000 .0172937 .0192344 LogAssets -.0599721 .0193166 -3.10 0.002 -.097832 -.0221122 _cons 2.266005 .4377291 5.18 0.000 1.408071 3.123938

sigma_u .36951524 sigma_e .27021279 rho .65157386 (fraction of variance due to u_i)

Social

95

Random-effects GLS regression Number of obs = 864 Group variable: FirmID Number of groups = 216

R-sq: Obs per group: within = 0.2027 min = 4 between = 0.5080 avg = 4.0 overall = 0.4494 max = 4

Wald chi2(5) = 736.41 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

(Std. Err. adjusted for 216 clusters in FirmID)

Robust LogMTB Coef. Std. Err. z P>|z| [95% Conf. Interval]

Social .0026722 .0009164 2.92 0.004 .0008761 .0044683 ROA 3.143454 .4991912 6.30 0.000 2.165058 4.121851 Dividend -8.014838 3.396861 -2.36 0.018 -14.67256 -1.357113 Debt .0184118 .0007455 24.70 0.000 .0169506 .0198729 LogAssets -.085468 .0186113 -4.59 0.000 -.1219455 -.0489905 _cons 2.889665 .4013699 7.20 0.000 2.102994 3.676335

sigma_u .37205385 sigma_e .26889463 rho .65688381 (fraction of variance due to u_i)

Governance

96

Random-effects GLS regression Number of obs = 864 Group variable: FirmID Number of groups = 216

R-sq: Obs per group: within = 0.1938 min = 4 between = 0.4955 avg = 4.0 overall = 0.4376 max = 4

Wald chi2(5) = 698.50 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

(Std. Err. adjusted for 216 clusters in FirmID)

Robust LogMTB Coef. Std. Err. z P>|z| [95% Conf. Interval]

Governance -.00024 .0007973 -0.30 0.763 -.0018027 .0013227 ROA 3.205159 .5022045 6.38 0.000 2.220857 4.189462 Dividend -7.922361 3.397816 -2.33 0.020 -14.58196 -1.262764 Debt .0182083 .0007547 24.13 0.000 .0167291 .0196875 LogAssets -.0725762 .0173732 -4.18 0.000 -.1066271 -.0385253 _cons 2.710561 .3841288 7.06 0.000 1.957682 3.46344

sigma_u .37699618 sigma_e .26995507 rho .66104601 (fraction of variance due to u_i)

97

Appendix 8 – Regressions on Tobin’s Q Post-crisis ESG Scores

Random-effects GLS regression Number of obs = 2,120 Group variable: FirmID Number of groups = 212

R-sq: Obs per group: within = 0.1351 min = 10 between = 0.5549 avg = 10.0 overall = 0.4876 max = 10

Wald chi2(5) = 155.47 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

(Std. Err. adjusted for 212 clusters in FirmID)

Robust LogMTB Coef. Std. Err. z P>|z| [95% Conf. Interval]

ESGScore .0025219 .0013229 1.91 0.057 -.000071 .0051147 ROA 3.422521 .4847249 7.06 0.000 2.472477 4.372564 Dividend -5.033316 1.434942 -3.51 0.000 -7.845749 -2.220882 Debt .0977103 .0229812 4.25 0.000 .052668 .1427525 LogAssets -.2137158 .0295151 -7.24 0.000 -.2715643 -.1558673 _cons 5.562054 .6891148 8.07 0.000 4.211414 6.912694

sigma_u .4462566 sigma_e .31302576 rho .67022817 (fraction of variance due to u_i)

Environmental

98

Random-effects GLS regression Number of obs = 2,050 Group variable: FirmID Number of groups = 205

R-sq: Obs per group: within = 0.1330 min = 10 between = 0.5475 avg = 10.0 overall = 0.4781 max = 10

Wald chi2(5) = 143.00 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

(Std. Err. adjusted for 205 clusters in FirmID)

Robust LogMTB Coef. Std. Err. z P>|z| [95% Conf. Interval]

Environmental .0022406 .0011402 1.97 0.049 5.84e-06 .0044755 ROA 3.370367 .4887723 6.90 0.000 2.412391 4.328343 Dividend -4.873832 1.438577 -3.39 0.001 -7.693392 -2.054272 Debt .0980282 .0237366 4.13 0.000 .0515054 .144551 LogAssets -.2026943 .0302446 -6.70 0.000 -.2619727 -.143416 _cons 5.324915 .7010379 7.60 0.000 3.950906 6.698924

sigma_u .43914145 sigma_e .31197197 rho .66458996 (fraction of variance due to u_i)

Governance

99

Random-effects GLS regression Number of obs = 2,120 Group variable: FirmID Number of groups = 212

R-sq: Obs per group: within = 0.1366 min = 10 between = 0.5468 avg = 10.0 overall = 0.4805 max = 10

Wald chi2(5) = 143.99 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

(Std. Err. adjusted for 212 clusters in FirmID)

Robust LogMTB Coef. Std. Err. z P>|z| [95% Conf. Interval]

Governance -.0011566 .0007962 -1.45 0.146 -.002717 .0004038 ROA 3.427114 .4879918 7.02 0.000 2.470668 4.38356 Dividend -4.985678 1.411542 -3.53 0.000 -7.75225 -2.219107 Debt .0944569 .0228786 4.13 0.000 .0496156 .1392982 LogAssets -.1906723 .0292978 -6.51 0.000 -.2480949 -.1332497 _cons 5.253404 .694714 7.56 0.000 3.89179 6.615018

sigma_u .45233864 sigma_e .31261126 rho .67676432 (fraction of variance due to u_i)

Social

100

Random-effects GLS regression Number of obs = 2,120 Group variable: FirmID Number of groups = 212

R-sq: Obs per group: within = 0.1391 min = 10 between = 0.5640 avg = 10.0 overall = 0.4964 max = 10

Wald chi2(5) = 169.17 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

(Std. Err. adjusted for 212 clusters in FirmID)

Robust LogMTB Coef. Std. Err. z P>|z| [95% Conf. Interval]

Social .0034839 .0010624 3.28 0.001 .0014017 .0055661 ROA 3.394934 .4826347 7.03 0.000 2.448987 4.34088 Dividend -4.960577 1.432932 -3.46 0.001 -7.769072 -2.152082 Debt .1014987 .0231649 4.38 0.000 .0560964 .146901 LogAssets -.2238702 .0285284 -7.85 0.000 -.2797848 -.1679556 _cons 5.724899 .6707219 8.54 0.000 4.410308 7.03949

sigma_u .43930011 sigma_e .31239345 rho .66414896 (fraction of variance due to u_i)

101

Appendix 9 - T-tests of change in coefficients

0.79

0.28

0.41

0.59

0.99

0.40

0.43

0.95

P-value

Df

2740

2740

2670

2740

2740

2740

2670

2740

2744

2744

2674

2744

2744

2744

2674

2744

N observations N

0.58

0.22

0.24

0.19

-0.81

-0.23

-2.44

-1.65

t stat t

SE SE

0.001127

0.001403

0.001492

0.001445

0.000108

0.000137

0.000140

0.000126

(Difference)

0.000812

0.000335

0.000033

0.000026

-0.000917

-0.000339

-0.000263

-0.000208

Difference

Crisis

0.000796

0.001062

0.001140

0.000955

0.000077

0.000116

0.000122

0.000083

Standard Error Post Standard

crisis

0.003484

0.002241

0.002709

0.000182

0.000247

0.000016

-0.001157

-0.000219

BetaPost-

0.000000

0.000000

0.000000

0.000000

0.000000

0.000000

0.000000

0.000000

crisis

0.000797

0.000916

0.000962

0.001084

0.000075

0.000072

0.000068

0.000094

Standard Error Pre- Standard

0.002672

0.001906

0.003048

0.000044

0.000149

0.000221

0.000224

-0.000240

BetaPre-crisis

Governance

Social

Environmental

ESG Scores ESG

Tobin's Q Tobin's

Governance

Social

Environmental ESG Scores ESG ROA

102

Appendix 10 - T-tests of change in average ESG Score between pre-crisis and post-crisis t-test: Two samples assuming unequal variances

ESGScore ESGScore Mean 67.7259717 43.98028935 Variance 264.8210299 284.1073219 Observations 2120 864 Assumed mean difference 0 df 1552 t-stat 35.25164971 P(T<=t) one-tail 9.1085E-201 t-critical one-tail 1.645836027 P(T<=t) two-tail 1.8217E-200 t-critical two-tail 1.961493682

t-test: Two samples assuming unequal variances

Environmental Environmental Mean 70.47802927 42.25876603 Variance 381.5648256 469.7970888 Observations 2050 624 Assumed mean difference 0 df 951 t-stat 29.12130029 P(T<=t) one-tail 4.2293E-134 t-critical one-tail 1.646457479 P(T<=t) two-tail 8.4586E-134 t-critical two-tail 1.96246161

t-test: Two samples assuming unequal variances

Social Social Mean 70.584 43.43900463 Variance 365.2858909 398.5443714 Observations 2120 864 Assumed mean difference 0 df 1541 t-stat 34.10264447 P(T<=t) one-tail 1.0759E-190 t-critical one-tail 1.645843044 P(T<=t) two-tail 2.1517E-190 t-critical two-tail 1.96150461

103 t-test: Two samples assuming unequal variances

Governance Governance Mean 61.39666038 51.87993056 Variance 453.3657108 499.5535337 Observations 2120 864 Assumed mean difference 0 df 1534 t-stat 10.69336857 P(T<=t) one-tail 4.31655E-26 t-critical one-tail 1.645847561 P(T<=t) two-tail 8.6331E-26 t-critical two-tail 1.961511646

104