What Drives the CEO Pay Ratio? a Comparison of Firms on Europe’S Main Stock Exchanges

What drives the CEO pay ratio? A comparison of firms on Europe’s main stock exchanges

Master Thesis by R. Hulshof MSc Supply Chain Management Student number S1260519

In partial fulfillment of the requirements for the degree of Master of Science in Finance At the Tilburg School of Economics and Management

Tilburg University, November 2018

Supervisors: Prof. dr. L.D.R. Renneboog Tilburg University E.J.P Engesaeth PhD Korn Ferry

ABSTRACT

This study is developed in light of the revised Dutch Corporate Governance Code which stipulates that Dutch listed firms as per 2018 (reporting year 2017) should disclose their CEO pay ratio. This ratio captures the compensation of the CEO relative to an average employee. So far, studies on this topic have mainly focused on the impact rather than the determinants of this ratio, besides being strongly focused on the US. In this research, we focus on identifying the determinants of the CEO pay ratio within a context of nine European countries. We find that the CEO pay ratio is determined by both country as well as firm-specific variables. Results indicate that firms with less dispersed ownership tend to have a lower CEO pay ratio, because they are likely more capable of controlling their CEO. Moreover, firms that are more innovative or active in the business-to- consumer market have significantly higher CEO pay ratios. In line with the general expectation that unions increase the bargaining power of employees, we find that higher levels of unionization indeed decrease the CEO pay ratio. Next to these firm characteristics, regulation plays also a significant role. Both highly regulated industries (such as the financial sector) and countries with better regulation on shareholder protection have lower CEO pay ratios. Lastly, we find support for the claim that societal characteristics matter, as higher inequality and individualistic cultures result in higher CEO pay ratios.

Keywords: CEO pay ratio; CEO compensation; employee compensation; corporate governance.

PREFACE

First and foremost, I want to thank Korn Ferry and in particular, Eric Engesaeth. I am truly grateful for the opportunities you have given me. Your passionate way of speaking about executive remuneration and all its aspects has been a huge inspiration.

Professor Renneboog, thank you for all the helpful comments, insights and your patience throughout the process.

Furthermore, I thank my colleagues within the Executive Pay & Governance team for their support, understanding and insights throughout my time as an intern. You made the end of my student life a rewarding and interesting period.

Lastly, my parents, sister and friends. Thank you for the constant support and belief, throughout this project but even more in my entire time as a student. You all mean so much to me.

Rick Hulshof Amsterdam, November 2018

TABLE OF CONTENTS

Abstract ------2 Preface ------3 Table of Contents ------4 1 Introduction ------5 2 Dynamics of the CEO pay ratio ------7 2.1 Institutional frameworks and corporate governance ------7 2.2 The CEO pay ratio ------11 2.3 CEO compensation ------17 2.4 Employee compensation ------25 2.5 Back to the CEO pay ratio ------30 3 Determinants of the CEO pay ratio: hypotheses ------33 3.1 Determinants of the CEO pay ratio between countries ------33 3.2 Determinants of the CEO pay ratio within countries ------38 4 Data and methodology ------41 4.1 Sample and data sources ------41 4.2 Variable description ------42 4.3 Methodology ------47 5 Results ------50 5.1 Descriptive statistics ------50 5.2 Regression results ------58 5.3 Robustness tests ------64 6 Conclusions ------68 6.1 Discussion ------68 6.2 Limitations ------71 6.3 Future research ------72 References ------74 Appendix A – Sample constituents ------Appendix B – Variable list ------Appendix C – Descriptive statistics ------Appendix D – Regression results ------Appendix E – Robustness tests ------

1 INTRODUCTION

According to the revised Dutch Corporate Governance Code (2016), as of fiscal year 2017 Dutch listed companies are required to report the CEO pay ratio in their annual report. In practice, this ratio captures the compensation of the CEO relative to an average employee. With this regulation, the Netherlands was a first-mover in Europe, although the European Commission intends to establish guidelines for all their members regarding CEO pay ratio disclosure within the broader set of policy guidelines called the Shareholder Rights Directive. More recently, also the United Kingdom (in early 2018) and France (in October 2018) have proposed legislation on the disclosure of the CEO pay ratio, but these laws/ codes are not finalized yet. Outside Europe, the United States have introduced the disclosure of the CEO pay ratio as part of the Dodd-Frank act (developed in 2010 but effective as of fiscal 2017).

CEO compensation has been subject of public debate for a few decades. One topic of interest has been the comparison of what a CEO earns relative to a rank-and-file employee. Now, with the introduction of legislation on CEO pay ratio disclosure, the topic receives increased attention. Especially, the ‘fairness question’ seems to get quite some attention: is the CEO pay ratio fair? This is a question that strongly relates to broader societal question about social (in)equality. It often feeds both public outcry over excessive executive compensation on the one hand and the reluctance of companies/ executives to disclose these ratios (Helmore, 2018) on the other. Our research may not be able to answer these heated questions but aims to contribute to understanding the drivers of the CEO pay ratio. In an emotional debate we try to objectify by bringing facts into the equation. What creates the differences in CEO pay ratios between firms and countries? Initial answers may help us in understanding the disclosed ratios and provide insight to assess whether certain ratios are in line with expectations or not. This is also question that comes up in Dutch general media (Tamminga, 2018), e.g. does it make sense that supermarket Ahold-Delhaize has a CEO pay ratio of 114, brewer Heineken a ratio of 215 and oil storage firm Vopak a ratio of 17? And if yes, what determines these differences?

In conclusion, the CEO pay ratio is a hot topic in the public and political debate. So far only limited academic research on its determinants exists (Faleye, Reis & Venkateswaran, 2013). Moreover, the research that does exist is mainly focused on US companies (Shin, Kang, Hyun and Kim, 2015). Examples are Balsam, Choi and Ju (2016) and Faleye et al. (2013) who use a combination of country level and firm level variables to determine the CEO pay ratio and

This study has the objective to contribute to the literature, based on European data. Given the role of the Netherlands as first-mover, in terms of the CEO pay ratio disclosure requirement, the initial data sample and focus is on the Netherlands (AEX companies). In order to provide meaningful

insights for these Dutch listed companies, within the European context, we also study the CEO pay ratio for a selection of other European countries. The main stock index of each country is used to construct our dataset, which then consists of: the Netherlands (AEX), Belgium (BEL20), France (CAC40), Germany (DAX30), Italy (FTSE MIB), Spain (IBEX35), Switzerland (SMI), Sweden (OMX30) and the United Kingdom (FTSE100). We align with existing research which uses a set of multiple variables to explain the CEO pay ratio. Hence, we look at both the difference between countries as well as firms, in order to capture as much drivers of the CEO pay ratio as possible. Based on this approach, our research and sub-question(s) boil down to the following.

Research question:

What are the determinants of the CEO pay ratio between and within countries for the largest European listed companies?

Sub questions:

1. What are the legal, societal and cultural determinants of the CEO pay ratio between countries?

2. What are firm specific determinants of the CEO pay ratio within countries?

We empirically analyzed these questions based on a predominantly hand-collected dataset of CEO pay ratios, as well various country and firm level variables, for 323 firms in the aforementioned countries. We find that both country-level variables as well as industry/ firm-level specifics drive the CEO pay ratio. Our results remain relatively stable after several robustness tests.

The remainder of this research is structured as follows. In Chapter 2, we start by introducing the practical and institutional setting regarding CEO compensation/ CEO pay ratio within Europe and particularly the Netherlands. Afterwards, we turn to the CEO pay ratio and its components from a more academic/ theoretic perspective. Chapter 3 presents the construction of the hypotheses based on the research questions and the literature/ practical setting as discussed in Chapter 2. Then, in Chapter 4, we describe the used dataset and methodology. In Chapter 5, the empirical analyses are presented. Finally, Chapter 6 concludes this research with a discussion of the obtained insights, limitations and potential future directions for research on the drivers of the CEO pay ratio.

2 DYNAMICS OF THE CEO PAY RATIO

Over the past few decades, research on executive compensation has grown significantly. Moreover, the body of literature on employee compensation has also developed at the same time. Yet, the combination of these two research paths is relatively less often undertaken by academics. Since the CEO pay ratio is the outcome of both types of compensation, we do take this approach and review work on both executive as well as employee compensation in order to understand the basic dynamics underlying the CEO pay ratio. In order to be complete and obtain a framework the specifics of compensation, we first start this chapter with an analysis of the institutional framework and corporate governance that shape many decisions in the first place. This leads to the subsequent section which deals with the CEO pay ratio and the regulation (and its implementation) around it. Next, we outline the characteristics and main theories on executive compensation, followed by a similar review for employee compensation. After these four sections we come back to the CEO pay ratio and end with the current theoretical knowledge on this topic, which forms the starting point for the next chapter.

2.1 INSTITUTIONAL FRAMEWORKS AND CORPORATE GOVERNANCE As already mentioned, one important factor of CEO compensation is often overlooked in the validation and application of the existing theories. This is the institutional framework in which the decisions are made. In this section, we briefly explain the concept of institutional framework (theory) and its importance. Next, we outline the phenomenon of corporate governance and its various forms. From this section it becomes clear that institutions and corporate governance are crucial in the compensation field.

2.1.1 Institutional frameworks Over the years, more and more academics argue that the compensation setting should not only be reviewed through an isolated lens, but rather in the broader institutional framework (Bruce, Buck & Main, 2005; Granovetter, 1985; Otten, 2007; Sun, Zhao & Yang, 2010). This institutional framework matters, because it provides the “rules of the game” in the pay setting procedure (van Essen, Heugens, Otten & Oosterhout, 2012). An institutional framework can be divided in several ways. In this paper we look at institutions in terms of their nature, which can be legal, political, social, cultural or economical. Research that has taken into account the embeddedness of executive pay setting in the institutional context have shown to be more conclusive (e.g. Jensen & Murphy, 2004). The same applies to the employee compensation setting as stated by Werner and Ward (2004). Hence, we also take this institutional framework into account in our search for the driving forces of CEO pay ratios. However, we focus on the legal and social/ cultural institutions,

because we want to obtain the insight whether the ratios are driven by set rules (legal) or by the ‘beliefs’ and ‘norms’ in a society (cultural/ social).

2.1.2 Corporate governance The concept of corporate governance is closely related to institutional frameworks, but it requires additional attention. Renneboog (2005) argues that corporate governance mechanisms secure that management (the agent) takes actions which are in the interest of the stakeholders of the firm (the principals). Often, Anglo-American literature has a more specific description of corporate governance. For instance, Zingales (1998) defines corporate governance as: “the complex set of constraints that shape the ex-post bargaining over the quasi-rents generated by a firm” (p. 4). Shleifer and Vishny (1997) describe corporate governance as “the ways in which suppliers of finance to corporations assure themselves of getting a return on their investment” (p. 737). They argue further that market competition insufficiently manages this problem and thus these corporate governance mechanisms are needed. Whereas Zingales (1998) argues that incomplete contracts (i.e. contracting everything is not possible or extremely costly) is the reason for the existence of corporate governance. In conclusion, both argue that some part of the world is not perfect and needs adjustments in the form of corporate governance.

Over the years, governance literature has grown extensively, and many perspectives are touched upon (e.g. legal, political, market). In the work by Gillan (2006), a broad framework is presented in which he makes a distinction between internal and external governance actors, but also argues that different actors may interact with each other. This framework is depicted in Figure 2.1.

Figure 2.1 Corporate governance framework as developed by Gillan (2006).1

As can be seen, many different perspectives on corporate governance comprise the framework. Key in the framework is the nexus of contracts that define the firm. As proposed by Jensen and Meckling (1976), a firm is essentially a broad set of contracts between all related parties based on complete contracting possibilities. Yet, as mentioned before, this efficient contracting view is often

1 In his research, Gillan focuses on the market and law/ regulation side of corporate governance, following general corporate governance research trends. Therefore in this figure, these terms are displayed in bold.

challenged. Therefore, several academics have established different definitions of the firm. Rajan and Zingales (1998) define the firm as a nexus of specific investments, which are irreplaceable by the market. Compared to Jensen and Meckling, this view does provide a more economic sense of the firm instead of a legal definition. The factors within this nexus of contracts/ investments provide the internal mechanisms of corporate governance. The other five actors are the external governance mechanisms. Together, these reflect the stakeholder theory of Jensen (2001), which states that firms should take all stakeholders into account during decision-making. Obviously, this concept is similar to the institutional framework, but looks at stakeholders instead of institutions. Moreover, it may be that the institutional framework actually influences corporate governance in a way (Gillan, 2006). For instance, specific laws in a country (institutions) can influence the actions taken by shareholders (corporate governance). Yet, one could also say that these are just interactions of corporate governance actors (Gillan, 2006).

As can be seen in Figure 2.1, the number of corporate governance actors is large, in turn the specific factors underneath these is even larger. Gillan (2006) continues to make a distinction and identifies numerous mechanisms that underlie these corporate governance actors. One of the most intriguing mechanisms is executive remuneration, because it is a mechanism itself as well as influenced by other corporate governance actors. The first (“being the mechanism”) is discussed under the agency view in Section 2.3 and has been the subject of a large body of research, mainly focused on validating the pay-performance relation (e.g. Tosi, Werner, Katz & Gomez-Mejia, 2000). The influence of corporate governance on compensation (and then especially CEO specific) has been established several decades ago (Williamson, 1985; Main & Johnston, 1993) and is now widely accepted. Moreover, the relationship is tested for many of the mechanisms both internal as well as external. For instance, capital structure (especially, debt) disciplines management in their risk-taking and thus reduces the need for incentive compensation (Grossman & Hart, 1982) or the market for corporate control decreases the need for incentivized pay (see Weston, Siu & Johnson, 2001 for an overview). In this research we apply a specific focus to the aforementioned legal and social/ cultural mechanisms, but also on the firm-level factors that are related to corporate governance (as discussed in Chapter 3).

The actors in the framework by Gillan are influencing executive compensation indirectly. However, this is not the only source of corporate governance. In fact, many companies are directly influenced by national and industrial corporate governance codes (Engesaeth, 2011). Listed companies are obligated to either apply/ explain why they (do not) perform principles and best practices in the prescribed way. A large portion of the corporate governance codes for the European countries deals with remuneration policies. In Table 2.1, we provide an overview of the

key principles outlined in the codes for the countries in this research, as introduced in Chapter 1. The specific rules and guidelines on the CEO pay ratio will be discussed in Section 2.2.

Table 2.1 Corporate governance code on remuneration executives (as of 2018). For each country the latest (amendments of) the codes/ laws are taken into consideration.

COUNTRY DISCLOSURE ON AGM VOTE ON DISCLOSURE DISCLOSURE CEO PAY REMUNERATION REMUNERATION REMUNERATION OF RATIO/ RECENT POLICY POLICY INDIVIDUAL DEVELOPMENTS BOARD MEMBERS THE Y (apply or Y (binding vote) Y (law) Y (apply or explain) NETHERLANDS explain)

BELGIUM Y (apply or N Y (CEO fully, other N (no debate) explain) executive directors only sub-totals e.g. base, STI etc. FRANCE Y (law) Y (advisory vote, but Y (law) Y (proposed new law) board must publish their actions if negative voting) SPAIN Y (apply or Y (advisory vote) Y (apply or explain) N (no debate) explain) ITALY Y (apply or N Y (law) N (no debate) explain) SWITZERLAND Y (apply or Y (but at the discretion Y (but only the N (rejected a proposal to cap explain) of the Board) highest paid executive pay relative to member) lowest-paid workers in 2013) GERMANY Y (apply or Y (but at the discretion Y (apply or explain) N (although it is already explain) of the Board) required that companies take it into account; the current debate is whether it should become part of disclosure) SWEDEN Y (law) Y (binding vote) Y (law) N (no debate)

UNITED Y (law) Y (binding vote) Y (law) Y (proposed new law) KINGDOM

From this overview, it stems that the CEO pay ratio is a relatively new measurement, as in our dataset only the Netherlands, France and United Kingdom have implemented ruling in their governance codes. This opposed to the other topics, which are almost everywhere laid out.

However, there is a new development within the European Union on the topic of executive compensation. The Shareholders Right Directive (SRD) entails several measures that should align European corporate governance on executive compensation. These measures relate to share- based payments, binding votes and employee representation, but also to the CEO pay ratio. The proposed SRD states that all companies should report their CEO pay ratio and also the development over three subsequent years.

Hence, since corporate governance codes are reviewed periodically, it may well be that the CEO pay ratio becomes a widely used mechanism. Thus, it is important that in subsequent research, this “legal” background is always re-considered.

2.2 THE CEO PAY RATIO As mentioned before, the Netherlands, France and the United Kingdom (proposal) started recently to require listed companies to disclose their CEO pay ratio, making it a relative unique requirement in the world. In this section we introduce the concept, followed by a review of existing regulation. Moreover, we present some case examples, which should give a broad sense of what current practices are.

Together with the new legislation, academics have developed an increased interest in exploring the subject of pay disparity for firms. This disparity may come in two ways: vertical disparity, which refers to differences across organizational ranks, and horizontal disparity, which deals with differences amongst peers (Gomez-Mejia, Berrone & Franco-Santos, 2014). A typical form of horizontal pay disparity is the gender pay gap, which measures the difference in pay for a male compared to a female for the same position or role within a company (Blau & Kahn, 2007). This topic is and has been subject of debate for a long period of time. Just recently, the United Kingdom requires their publicly listed companies to disclose this gender pay gap, according to the Equality Act 2010 (Gender Pay Gap Information) Regulations 2017, even before the introduction of legislation on the CEO pay ratio. The CEO pay ratio or CEO-employee pay ratio, (i.e. the ratio between the average/ median employee wage and the total CEO remuneration) is a type of vertical pay disparity.

Generally, there are two calculation methods that are considered to be effective in determining the CEO pay ratio. First, total CEO compensation divided by average employee compensation. This method has the advantage of its relative simplicity, because average employee compensation is rather easy to determine. Yet, the disadvantage is the fact that it is a less accurate description of

the workforce, because an average value will include outliers’ effects, whereas the median is likely an employee in the relatively largest sub-employee group. Therefore, the second method is total CEO compensation divided by the median employee compensation. From a statistical viewpoint, this method is more viable. However, it is harder to determine the median, as it requires extensive data on all individual employee’s compensation. Hence, this method is often seen as more time- consuming and thus costly. From the calculation methods and the general definition of the CEO pay ratio, it is obvious that both employee compensation as well as CEO compensation play a crucial role in its existence. Thus, this justifies our review of both themes in Section 2.3 (CEO compensation) and Section 2.4 (employee compensation).

Whereas, existing empirical work on CEO and employee compensation packages and levels on a separate basis is rich for both US (e.g. Goergen & Renneboog, 2011; Murphy, 1999, 2013) and several European countries (e.g. Conyon & Schwalbach, 2000a, 2000b; Randøy & Nielsen, 2002), this does not apply to the CEO pay ratio. Thus far, only few academics have done an empirical study on the characteristics (for instance an overview of levels and patterns) of this ratio or (generally speaking) remuneration disparity. Further, just as for CEO and employee compensation most research is done with US data, because of the limited availability of European data.

Yet, some research within Europe exists. For instance, Conyon & Read (2000) researched the CEO pay ratio within UK firms from 1984 to 1998 and found that the ratio rose from 7 to 19. Yet, this research was done with data from the 80’s and did not include any non-cash components in the calculation of CEO compensation, which in turn gives these relatively low CEO pay ratios.

2.2.1 Corporate governance and regulation on the CEO pay ratio Obviously, theoretical approaches to the CEO pay ratio are important and interesting, but so far it has been a relatively practical matter. Therefore, we introduce in this section a framework of the current regulation (corporate governance). Next, we also discuss some practical applications in the form of case examples.

Legislation on the CEO pay ratio

Currently, in our dataset only the Netherlands, France and the United Kingdom have adopted regulation on the CEO pay ratio. In order to comprise some more reference points, we also include the US in our framework. The resulting overview is presented in Table 2.2.

Table 2.2 Regulation on the CEO pay ratio. For each country the latest (amendments of) the codes/ laws are taken into consideration.

COUNTRY CODE AND REPORT AND/ OR CALCULATION EXEMPTIONS/ INTRODUCTION TAKE INTO METHOD(S) AMBIGUITIES YEAR CONSIDERATION

THE Dutch Corporate Both. Report ratio Not specified. Not specified but apply or NETHERLANDS Governance Code and changes Often average explain policy. 2016. As of fiscal year, compared to previous employee 2017 rules apply. years. Consider ratio compensation in compensation used. setting (e.g. when CEO pay increases does employee pay as well?). FRANCE Le PACTE (passed Report only, both Both the median to Not specified. Introduction of parliament vote in current and past five- CEO compensation this law is combined with October 2018). Senate year ratios. as well as average many other measurements to will vote in January to CEO strengthen employee position 2019 compensation in listed firms (both should be reported financially as well as politically) THE UNITED The Companies Report only. Use Choose from three Only use UK employees; KINGDOM (Miscellaneous proposed table, available qualifying condition is having Reporting) which reports the methods.2 Use of at least 250 UK employees. Regulations 2018. method and the pay median employee If company is a group parent, Draft proposal, if ratio for the 25th, compensation. the ratio should apply to the accepted effective in median and 75th group (not solely the parent). fiscal 2019 percentile employee pay and benefits. THE UNITED Based on the Dodd- Report only. CEO total If changes in the method are STATES Frank Wall Street Flexibility in compensation made these need to be Reform and Consumer reporting divided by median justified. The median

2 In their calculation’s methods, the gender pay gap is sometimes mentioned: For the gender pay gap data, companies should refer to disclosures based on the Equality Act 2010 (Gender Pay Gap Information) Regulations 2017. CEO compensation is standard and equivalent to the total received package. For the pay and benefits of average employees three methods may be used: 1.) calculate for all employees their compensation and determine the needed percentiles. 2.) start with the most recent hourly rate gender pay gap to identify the best equivalent employees for the percentiles, use financial data to calculate the compensation and if needed adjust to ensure good depiction. 3.) like approach two but use additional data as a starting point to determine the percentile equivalents.

Protection Act, ruling requirements, to employee employee may be substituted was adopted in 2015. reduce costs and compensation. by a similar employee if Disclosure starts as of burdens, whilst still Freedom in otherwise unrealistic values fiscal year 2017. preserving the goal of determining are obtained. Non-US better understanding median employees may be excluded if for shareholders. compensation but they account for at most 5% explain method. of the workforce. Individual contractors may be excluded from the calculation.

2.2.2 Case examples Until now, only Dutch and American companies were actually required to present their CEO pay ratios. We provide for both countries practical examples on how companies implemented these rules.

The Netherlands

Since the Netherlands do not prescribe in their Corporate Governance Code how companies should report and/ or calculate their respective pay ratio, a wide variety of applications exist. One of the most acknowledgeable is what can be found in the annual report of the retailer Ahold- Delhaize. They calculate their pay ratio by taking the complete remuneration for the executive and divide it by the average labor costs. These average labor costs are calculated by taking the total labor costs divided by the number of all associates on an FTE basis (Ahold-Delhaize, 2018). Moreover, they calculate the pay ratio not only for the CEO (1:114), but also for the deputy-CEO Figure 2.2 Example of extensive reporting on the CEO pay ratio: Ahold Delhaize. Provide a comparison of the CEO pay ratio to AEX and peers. (Annual Report, 2017)

(1:135), CFO (1:71) and CEO Ahold Delhaize USA (1:74). In line with their remuneration policy, they also show how their CEO-pay ratio compares to their peers (AEX, European and American) with their own calculation method. This is depicted in Figure 2.2.

It may be noticed that the interpretation of the Corporate Governance Code by Ahold-Delhaize is relatively extensive. Other companies keep things simpler, of which we present some examples hereafter.

Koninklijke Philips NV uses the average employee compensation and reports a CEO pay ratio of 1:56, they mention that they also include pension benefits in employee compensation. ING Group NV uses only total direct compensation (i.e. base and variable, but no benefits like pensions) for both the CEO and average employee to calculate the ratio, which is 1:33. Moreover, to be transparent ING also reports the ratio for the CFO and CRO equal to 1:23. The use of average employee pay seems to be the dominant method for Dutch listed companies, for instance Koninklijke KPN NV, Aegon NV and Koninklijke DSM NV all use this method. Moreover, other companies (e.g. ABN AMRO Group NV and Koninklijke Vopak) use a similar approach (thus average employee pay), but do not consider the entire compensation or have a slightly different way of valuing long-term pay. Thus far, only one AEX-listed company uses the median employee compensation, namely ASR Nederland NV, who report a CEO pay ratio of 1:9. Lastly, some companies choose not to disclose the CEO pay ratio (e.g. ArcelorMittal SA) or only provide the numbers to leave the calculation up to the reader (e.g. RELX NV) and thus use the option in the Code to explain (instead of apply).

Figure 2.3 This graph displays a comparison of disclosed CEO pay ratios by AEX companies. All are essentially “obligated” to report, but as can be seen only 20 out of 25 actually reported their CEO pay ratio.

In total five companies (out of 25 AEX companies) did not disclose their CEO pay ratio. In order to get a grasp of the landscape of the CEO pay ratios for AEX companies we created a graph (Figure 2.3) that presents the disclosed CEO pay ratios relative to the size of the company as measured by market capitalization based on a percentile division (25th, median and 75th percentile).

From the graph, it becomes clear that the CEO pay ratios roughly grow relative to size. Yet, there are some outliers. For instance, ING has a relative low ratio, but is large. Whereas, Wolters Kluwer has a high CEO pay ratio, but is of smaller size. Hence, it is reasonable that size is not the only explanation for CEO pay ratios, providing us with one of the many triggers for this research.

The United States

As for US firms the rules on calculating ratios are relatively more set, we observe that these are followed and implemented. Hence, median employee compensation is used. Differences are made in the application of the exemptions as stated in Table 2.2. For instance, Best Buy Co Inc. does exclude their workers in China and Mexico as they only add up to 1.6% of the total workforce. Walmart applies a similar exclusion, accounting to 3.9% of the workforce excluded (over 35 countries, including Western countries like France and Holland). Also, AT&T excludes 53 countries from their calculation. Moreover, they use a “substitute” median employee, because the actual median employee has extreme high pension values. Chesapeake Energy Corporation in turn does not make use of any of the exemptions and calculates the CEO pay ratio given all their employees.

A general observation that follows from the brief review of reporting CEO pay ratios within the Netherlands and the US is that given the “voluntary” nature of the Dutch Corporate Governance Code, the reporting of the ratios is sometimes unclear or lacking. Whereas US companies have a specific section in their Proxy Statement dedicated to the CEO pay ratio, in which they extensively explain their calculation approach and assumptions. The same is likely to be true for UK companies when they start reporting, but as of now this cannot be determined. A consequence of this median method is that sometimes the CEO pay ratios grow to extreme large numbers, for instance Walmart has a ratio of 1:1188, because their median employee is a part-time supermarket worker. Hence, it becomes clear that it makes a huge difference how the CEO pay ratio is calculated. In this research, we choose to use the “best practice” method within the Netherlands. Thus, dividing CEO compensation by the average compensation of an FTE employee.

2.3 CEO COMPENSATION In this section, we introduce the concept of CEO compensation, which is half of the definition of the CEO pay ratio. Note that we may use different terminology in terms of compensation, remuneration or sometimes pay. Even though these do not necessarily impose the same, we use them interchangeable. In Section 2.4, we move to the employee compensation, which is the second half of the CEO pay ratio. Eventually, in Section 2.5 we move back to the CEO pay ratio and review some of its main theories.

The focus on the chief executive officer (CEO) compensation is not new and in line with existing work, because the CEO is seen as the ultimately responsible person for corporate performance (Aggarwal & Samwick, 2003). In this research we follow this line of thinking. We start by defining executive remuneration and its main components. Next, we elaborate on the basic explanatory theories and their proofing. A large part of this overview draws on work by Edmans, Gabaix and Jenter (2017); Goergen and Renneboog (2011).

2.3.1 CEO compensation components CEO compensation is simply defined as the total benefits a chief executive officer receives for acting in this role. In principle, it is determined by a special compensation committee within the Board of Directors, often advised by a specialized consultancy firm (Bender, 2011). Yet, total CEO compensation is more complex and can encompass several mechanisms and specific rules/ guidelines (Murphy, 2013). Broadly spoken, it may consist of three basic components which we discuss below. It is worth noting that the specific package may differ strongly per country, industry and/ or firms, but the basic set-up of these three components is widely observed.

Short-term compensation

The general structure of short-term compensation includes a base salary irrespective of performance and an annual bonus based on the foregoing year’s performance.

The base salary is established based on a benchmark against peer firms. Moreover, specific tasks, challenges, experience and seniority are other reappearing factors in the setting of the base salary level. Over the years, academics have acknowledged the positive relation between the firm size and CEO compensation (Kostiuk, 1990; Murphy, 1999). This effect is often explained by academics by reasoning that larger firms are more complex, create more responsibilities and have an increased hierarchy and thus require higher compensation (Firth, Tam & Tang, 1999). In conclusion, it can be said that the base salary is a result of market, industry, company, task and individual variables (Jacobsen & Skilman, 2004).

The annual bonus is more complex. Although, the initial procedure is mostly based on the same benchmarking techniques as for the base salary, the link with performance causes the complexity and diversity in plans. Basically, the bonus consists of three performance related parts: measures, standards and sensitivity of the pay-performance relationship. Measures are frequently based on accounting performance outcomes such as operating profit, revenue or earnings per share, but also for example operational/ strategic targets or scoreboard-based principles are used. Further, most companies use a combination of criteria (De Angelis & Grinstein, 2014). The actual payout is calculated by means of an “additive” or “multiplicative” approach. The additive method simply accumulates the outcomes of each target, whereas the multiplicative method multiplies outcomes for each criterion (Geiler & Renneboog, 2011). Furthermore, companies may decide to start a deferred share plan, meaning that part of the bonus is reinvested in shares of the company, to be matched and paid-out later (Khalil, Magnan & André, 2008). The standards are there to establish a certain range in which the performance criteria are met and provide a guidance on the pay-out level. It consists of a threshold, target and often a maximum level based on the specific criterium (absolute or to a peer group). Lastly, the pay-for-performance sensitivity is also expressed in a contract, which tells how much of CEO compensation is set to be the consequence of performance.

Long-term compensation

Also, long-term remuneration may consist of several components including: long-term incentive plans (LTIP), restricted stocks, phantom stock plans, stock options and stock appreciation rights. In general, long-term compensation (compared to short-term) is based on equity value and is depending on outcomes over the period of at least one year (Abowd & Kaplan, 1999).

Long-term incentive plans (LTIP’s) are performance-based programs that have pay-out/ vest criteria that span over a longer period (Conyon, Peck, Read & Sadler, 2000). Yet, the specific criteria might be very similar to the ones mentioned for the annual bonuses. Many plans incorporate relative performance measures against a peer group, e.g. total shareholder return (TSR) or sales growth (Gong, Li & Shin, 2011). According to Bettis, Bizjak and Lemmon (2005), LTIP’s can be split into two different programs. First, performance-vesting stocks provide executives a fixed number of shares if certain requirements are met (“good performance”). Second, performance shares programs award executives a variable number of shares as a function of the outlined criteria. Hence, the pay structure is essentially the same as in an annual bonus plan: a range from threshold, target and maximum (performance) vesting. For both LTIP’s, similar plans exist in which the pay-out is in options or in cash, but these are more uncommon or only used locally (Conyon & Murphy, 2000).

Restricted stocks are shares transferred if predefined conditions are met, which is often only the passage of time. Sometimes, these restricted stocks grants are tied to performance as well, but in general they are unrelated (Conyon et al., 2000).

Phantom stock plans are for the large part the same as restricted stocks, with the difference that no actual stocks are awarded. Instead, an executive ‘receives’ shadow stocks that track the price movements of the actual stock. If then the restrictions are removed (e.g. after passage of time), a cash payment is made (Brickley, Bhagat & Lease, 1985).

Stock options differ from the other long-term compensation plans as they are generally granted to executives in order to tackle risk aversion (instead of enhancing performance), thus trigger them to engage in riskier activities to increase shareholder value. Otherwise, risky actions would be limited if only ‘good’ performance plans existed (annual bonuses, LTIP, restricted stocks). Research from among others, Bebchuk and Spaman (2009); Certo, Daily, Cannella and Dalton (2003) gives evidence for the relation between stock option compensation and risk-taking behavior by CEOs. In the United States, options are mostly granted at-the-money, have often a term of ten years, a vesting period of three years which may become accelerated and are valued based on the basic Black-Scholes model (Geiler & Renneboog, 2011).

With stock appreciation rights, executives are eligible to receive the increment value of a company’s stock over a specified period (Herzel & Perlman, 1978). In essence, it triggers the same risk-taking behavior as stock options intend to do, whilst no investment has to be made by the company to provide the grant (Cohn, 1979).

Other compensation

Research has so far mainly focused on the short- and long-term compensation packages. However, there are three “other” compensation forms that the majority of the executives do receive: perks, pensions and severance pay. Perks are all types of goods and services an executive is eligible for e.g. cars, phones, laptops and memberships. Executives are also participants in the general pension plan a company has in place for all employees, but they have supplemental executive retirement plans (SERPs). These plans may come in several ways, mostly by means of years of service or variables like company performance (Murphy, 1999). Severance pay is compensation received when employment ends and exists in two forms: golden handshakes (retirement or fired) and golden parachutes (losing job after acquisition). The goal of severance packages is to encourage executives in maximizing shareholder wealth with no fear of losing their position.

2.3.2 Theoretical frameworks for CEO compensation The rapid growth of executive pay has generated increased effort by academics to come up with theoretical explanations for the structure and/ or level of CEO remuneration (Edmans et al., 2017). Whereas in the beginning the agency theory as proposed by Jensen and Meckling (1976) was long assumed to be solely responsible for determining CEO compensation, later multiple other theories have emerged and were translated in remuneration principles, which Crombie (2015) defined as “systems of belief and reasoning which individuals and organizations use to make, interpret and justify remuneration decisions” (p. 99).

Yet, the existence of consensus on the explanatory power of a single theory is absent. Especially, the strength of agency theory is questioned, even though it received the most academic attention. It is even argued that the use of this single theory drives us into a “blind alley” (Barkema & Gomez- Mejia, 1998). Moreover, the failure to explain executive compensation by this single theory has led to the notation of a “puzzle” or “anomalies” for the problem, leaving other theories relatively under developed/ researched (Bebchuk & Fried, 2004). This despite the importance of finding out theoretical explanations, since these are often guiding in the legitimization of company’s pay- practices (e.g. Wade, Porac & Pollock, 1997; Zajac & Westphal, 1995).

In this section we briefly outline the theories, guided by the overviews by Gomez-Mejia (1994), Balsam (2002) and Otten (2007). In subsequent sections, we elaborate on those theories that are especially relevant for the CEO pay ratio and this research. Yet, since research on explaining the CEO pay ratio has only received limited attention and for the sake of completeness, we also provide those theories that have only considered CEO compensation.

Agency view

Principal-agent theory builds on the fact that one party (the principal) entrust responsibilities to an agent, who then performs in name of the principal (Jensen & Meckling, 1976; Eisenhardt, 1989). According to Gomez-Mejia and Wiseman (1997), this relationship holds on four assumptions: a conflict of interest between principal and agent, risk averse agents, agents behave self-interested and principals cannot observe all agent’s actions. In turn this situation creates the possibility of agency costs. Moreover, in their paper Jensen and Meckling (1976) establish an application of agency theory in the light of the separation of ownership (shareholding) and control (executive management) within firms, which causes a misalignment. Executive compensation is in the first place seen as a key way to mitigate this problem because it aligns interest of shareholders and management if proper incentives are chosen. Yet, there exists a conflict between the optimal risk bearing for agents and the optimal incentives for the principal (Rajagopalan, 1996).

From this initial notion of agency problems and executive compensation, two streams emerged within the agency view (Bebchuk, Fried & Walker, 2002). The first stream focuses on optimal/ efficient contracting, whereas the second focuses on managerial power/ rent-seeking behavior.

Optimal contracting theory builds on the economic sense of compensation incentives (Conyon & Murphy, 2000). Executives can improve corporate performance but that requires effort and risk- taking. Hence, certain reward systems need to be developed in order to incentivize executives to meet pre-determined performance criteria (Baker, Jensen & Murphy, 1988). Eventually, this reward system is established after arm’s length contracting processes between the executives and Board of Directors (Bebchuk & Fried, 2003). The alignment of executive’s interest and shareholders’ through the connection between executive compensation and firm performance is seen as crucial (Jensen & Murphy, 2010). Although this theory is seen as the “official story” (Bebchuk & Fried, 2004), empirical research on the relationship between pay and performance, which is a good proxy for the theory, does not give a consistent answer. Academics only seem to acknowledge that the relationship is significant, but not very strong (e.g. Rosen, 1990; Rost & Osterloh, 2009; Tosi et al., 2000).

Managerial rent-seeking theory states that the principal-agent relationship enables agents to be in the position to set their own pay and thus extract rents from shareholders (Bratton, 2005; Yermack, 1997). Hence, rather than being the solution to agency problems, the executive reward is an agency problem itself (Bebchuk et al., 2002; Bebchuk & Fried, 2003, 2004). Key in the theory is the relative power executives have over the board and vice versa, if there is imbalance, rent- seeking behavior occurs (since agents behave self-interested: Berle & Means, 1932). Crucial in the mitigation of this problem is a well-functioning board, which can be established by good corporate governance. The managerial rent-seeking theory has both received criticism (cf. Kaplan, 2008) and support (cf. Garvey & Milbourn, 2006; Kuhnen & Zwiebel, 2007) based on empirical work.

Value view

Where agency theory is strongly focused on how executives should be remunerated, the value view theories focus on validating the level of pay. The overarching idea is that the law of supply and demand is shaping executive compensation, thus market forces and – mechanisms are essentially setting pay (Otten, 2007). The value view stream can be split up into six theories, which we discuss next.

First, Marginal productivity theory builds on the simple assumption that executive input is seen as any other production factor’s input (Roberts, 1956). Then, the value is simply determined by the intersection of supply and demand in the market equilibrium, which is based on the marginal

value of the executive compared to the next best alternative (in terms of firm performance difference) according to Gomez-Mejia (1994).

Second, extending the marginal productivity theory has led to the human capital theory. Here the simple view on executive’s productivity is complemented by incorporating the influence of skills and knowledge (the human capital). Hence, executive compensation is truly based on the human capital, not simply 'assuming' marginal productivity (Agarwal, 1981; Combs & Skill, 2003).

Third, efficiency wage theory also builds upon the notion of the marginal productivity theory but adds a premium on top of the market level. Prendergast (1999) states that because of this premium, executives are incentivized to put in more effort and thus increase productivity.

Fourth, since the labor market for executives has become transparent, switching between employers has become possible. Then, the opportunity cost approach claims that executive remuneration should be higher or equal than for the next-best alternative, because otherwise executives will leave their positions (Thomas, 2002).

Fifth, relative bargaining power theory (Hayes & Schaefer, 1999) states that the compensation of executives (and others) is the result of relative bargaining power and constraints of the parties involved. The core idea comes from the social exchange theory developed by Blau (1964). In essence, a pay-setting procedure is also a social exchange between the board and the CEO. Although, each party makes claims about the contribution to firm’s performance eventually the relative bargaining power determines the outcome (Avent-Holt & Tomaskovic-Devey, 2010; Dencker, 2009). Specifics that create this relative bargaining power are for instance board characteristics, unionization and the executive market’s state.

Lastly, the superstar theory as first argued by Rosen (1981). In this extensive theory, he provides a structured explanation explaining the skewness of executive pay versus talent which is relatively normally distributed. Eventually, executive pay can be compared to pay for superstars (sports, media, entertainment), it is then argued that in the end, talent and effort are just a small determinant in the executive pay setting, more important is the size of the audience (e.g. spectators). For a company this can be translated in the total resources that are affected by the CEO’s decisions taken. Thus, the largest companies will pay the highest income to whoever is the highest talent, but it does not differ how much more talent this executive brings to the table (Waldenberger, 2013).

Symbolic view

The last set of theories is compared to the two streams less focused on the economic reasoning, but instead relies on the social symbolic value of executive pay based on expectations, status or

roles. Logically, the base of most of these theories lies thus in the field of behavioral economics. These symbolic insights translate into opinions what executives should have been and are actually awarded. Over the years, numerous theories have been developed, these are discussed next.

Argued by Lazaer and Rosen (1981) the tournament theory sees executive compensation as the prize in a game. Here, the highest prize is the CEO compensation, which becomes then an incentive for the lower level employees to climb up the hierarchy (Conyon & Sadler, 2001; Lee, Lev & Yeo, 2008). In turn, this trigger leads to increased productivity and likely better corporate performance. Essentially, to keep the tournament working the “symbol” of higher pay is needed, hence the CEO compensation package is set at a somewhat “extreme” level (Otten, 2007).

Stewardship/ stakeholder theory suggests that executives are collectively minded and trustworthy, thus not influenced by personal motivations in their role as an agent managing the company, which is the cynical view from the agency problem (Davis, Schoorman & Donaldson, 1997; Mitchell, Agel & Wood, 1997). Hence, compensation does not need to be aligned with company performance as this is automatically/ intrinsically happening. Moreover, Donaldson and Davis (1991) argue that motivation is not per definition generated by executive compensation but rather through non-financial rewards (e.g. recognition), which could be partially expressed with a certain compensation package (Elsayed & Elbardan, 2018).

The concept of intrinsic motivation also plays a crucial role in the crowding-out theory. This theory reasons that executive compensation may crowd-out the intrinsic incentive (Frey, 1997). As a result, wrong (i.e. high) compensation may generate too much extrinsic motivation and trigger unwanted behavior (Larkin & Pierce, 2016). Key is thus to find a balance between extrinsic and intrinsic compensation, which guides executive compensation setting.

Next, the figurehead theory suggests that part of the CEO role is to encourage cooperation amongst the different and opposing stakeholders in order to reach performance goals (Steers & Ungson, 1987). In this, the ability of a CEO to influence stakeholders is related to how they observe the CEO, which in turn can be influenced by for instance the symbol of a higher CEO salary (Gomez- Mejia & Wiseman, 1997). CEO pay is then based on this symbolism and the ability of a CEO to deal with this as they are potential drivers of firm performance (Miller & Wiseman, 2001).

Implicit or psychological contracting argues that there is a contract between two parties based on the individual beliefs concerning the nature of the exchange (Baker, Gibbons & Murphy, 2002). This translates into symbolic agreements and codes that influences individual’s behavior, based on trust and perceived fairness. In the light of CEO compensation, the characteristics and nature of the job construct this relational contract, in which remuneration is the denotation of appreciation and prestige (Hayes & Schaefer, 2000).

In the socially enacted proportionality theory as developed by Simon (1957), it is stated that executive pay is determined by their relative positioning in the firm’s ranking. This logic builds upon the societal need for internal consistency and proportionality between different organizational layers. Eventually, this leads to a pyramided pay structure, with “socially accepted” proportional steps, where the executives earn substantially more than the lowest level.

Lastly, a combination of equity fairness and social comparison theory provides reasoning for executive compensation. According to equity theory (Adams, 1965), humans compare their pay constantly with a reference group and anticipate it to reflect their relative contribution. Given this, O’Reilly, Main and Crystal (1988) and more recently Shin (2016) argue that executive’s pay is set after comparing against other executives based on normative judgments (i.e. executive pay reflects this symbolic judgment). This social comparison is possible because the CEOs in for instance a compensation committee see themselves as good references for the other CEO.

2.3.3 Conclusion From this section, it becomes clear that a wide variety of theories on executive compensation has been developed over time and all have some degree of explanatory power, which gives the possibility to approach the topic in a variety of ways. However, no single theory seems to be solely capable of explaining the numbers and patterns of CEO compensation observed in real life (Edmans, 2017; Murphy, 2013). Especially, the view that agency theory is the sole explanation has received much criticism (Tosi et al., 2000).

Instead, academics who believed in one explanatory theory have switched to a view that multiple theories co-exist and are integrated (Jensen & Murphy, 2004; Murphy, 2013). Nevertheless, attempts to develop integrated frameworks were already made (e.g. Finkelstein & Hambrick, 1998), but also more recently (Busenbark, Krause, Boivie & Graffin, 2016). Next to this “integration” movement, one other key characteristic of executive pay setting has received increased attention in the literature, namely the aforementioned institutional frameworks. Rather than an isolated look at executive compensation, the institutional framework provides a background setting in which corporate governance mechanism are set and compensation is influenced.

This section has two implications for our research. First, the absence of a sole explanatory theory, but rather a broad set of theories, justifies approaching the determinants of the CEO pay ratio from multiple angles (see Chapter 4). Next, we define CEO compensation in our research according to the line of academic work. So, total CEO compensation is the base salary plus short-term and long- term incentives (e.g. bonuses) plus all other measurable benefits.

2.4 EMPLOYEE COMPENSATION The next step after establishment of the dynamics of CEO compensation in the previous section, is to look at the second component that makes up the CEO pay ratio, which is the compensation that employees receive (on average). If we have this knowledge, we can return to the CEO pay ratio (Section 2.5) and see how both elements interact.

In this section, we review the subject in a same manner as we did for the executive compensation. First, we start by defining the subject of employee compensation by elaborating on some of its specific characteristics. Thereafter, we introduce some of the main theories and summarize some of the important driving forces on employee compensation. Existing work on this subject is numerous, see for instance Werner and Ward (2004) for an extensive review. However, more recently academics argue that the current research is not enough and that new initiatives are needed (Gupta & Shaw, 2014).

2.4.1 Employee compensation characteristics In general, employee compensation is defined as: “all forms of financial returns and tangible services and benefits employees receive as part of an employment relationship” (Milkovich & Newman, 2008: 9). Compensation is the key in the exchange within this employment relationship, which is essentially a contract (implicit or explicit) that irrevocable requires participants to perform certain actions (Rousseau, 1990; Williamson, 1975). This setup is created because an organization can only remain viable if the people want to engage and participate in the needed roles, which is stimulated by compensation (Katz & Kahn, 1966). Moreover, compensation eventually also further influences the behavior and attitude of employees, but also the effectiveness of the organization. This can be observed by for instance: attraction, retention, performance and cooperation of employees (Gerhart & Bretz, 1994). Next, the costs linked to employee compensation are in general (one of) the largest incurred by companies (Gerhart & Bretz, 1994) depending on how labor intensive their respective products or services are. From this, it becomes clear that employee compensation plays a huge role in the performance and value of a firm, either directly (via costs) or indirectly (via attitude and behavior). Obviously, not only the employers/ firms depend upon the choices regarding compensation. According to Gerhart, Minkoff and Olsen (1995), the compensation is for employees often the main source of income, financial - and health security (through benefits). Furthermore, compensation is often seen as a form of success in life and determines social standing.

Given the importance for companies and employees, both parties have sought for ways in which they could influence the compensation. Especially, employees have developed/ triggered vehicles that could influence the compensation-setting process. Most notably is the labor union, but the

actual effect on compensation remains relatively unclear and much contradicting research exists (Hirsch, Macpherson & Schumacher, 2016).

In the literature, strategic pay decisions are generally split up into five themes: form, level, structure, mix and administration (Milkovich & Newman, 2008). Next, we discuss four of these (level is left out as this is rather practical and is discussed in the descriptive statistics from our dataset in Chapter 5).

Form of employee compensation

Essentially, compensation can be split up into two forms (Gerhart et al., 1995). First, the cash/ stock compensation is the sum of all payments of base salary/ wages, bonuses and stocks. Second, benefits are all non-cash or deferred cash elements, such as health care, retirement and social security. Generally, the largest part of total compensation is in cash components, but certain benefits are capturing an increased share (e.g. health care). More importantly, especially benefits receive extensive coverage in public policies, as many see those as vital to the functioning of societies. Yet, research on benefits is relatively limited, this might be due to the fact that they are not seen as an immediate influence on firm’s performance from an academic perspective (Gerhart & Rynes, 2003).

Structure of employee compensation

The structure specifies the array of pay rates paid within a company and/ or business unit (Milkovich & Newman, 2008). These differentials may exist in various forms, but most known are those between different levels, business parts (e.g. product or region groups) and timing of pay. This leads to the three general structures: functional, divisional and knowledge-based (Gerhart et al., 1995). Research on structures has largely focused on relational properties/ ratios, this can for instance be between adjacent positions (Mahoney, 1979) or gender (Cain, 1986). Obviously, the CEO pay ratio is another measurement of pay structure but compared to other ratios has received relatively less and only more recently research interest.

Mix of employee compensation

The cash/ stock compensation parts may differ under which conditions pay-out follows. Just as for executives, it is possible to have variable and fixed pay, but the naming and working of some instruments differs. The goal of the variable components is to control labor costs and re-directing employee behavior (Gerhart et al., 1995). Essentially, the compensation plans aspects can be divided into two parts.

First, variable payments can be paid by either adding them to the base salary (merit pay) or by a one-time lump sum payment (bonus etc.). Merit pay is simply an increase that is rolled into the

base salary, thus remaining part of the base for all subsequent years. Hence, the past performances are reflected in all future years (Gerhart & Bretz, 1994). To be able to make a stronger link between present performance and pay, lump sum bonuses are awarded. Taken together, this leads to the following possible variable pay programs (of which often combinations are used), as presented in Table 2.3.

Table 2.3 Overview of different employee pay programs (Gerhart et al., 1995). INDIVIDUAL MERIT PAY MERIT GAINSHARING3 PROFIT OWNERSHIP4 SKILL INCENTIVES BONUS SHARING BASED PAY PAYMENT Bonus Changes in Bonus Bonus Bonus Equity Changes in METHOD base changes base

PAYOUT Weekly Annually Annually Monthly or Semi- When stock When skill FREQUENCY quarterly annually or sold required annually PERFORMANCE Output, Performance Performance Production or Profit Stock value Skill MEASUREMENT productivity, rating rating controllable acquisition sales costs COVERAGE Direct labor All All Production or Total Total All employees employees service unit organization organization employees

Second, the performance measurement for the variable pay can be linked to either individual or group/ team performance indicators. The decision strongly influences the ability to pay for companies, because it is possible in down-times to have individual good performance (and large bonuses), leading to a misalignment (Gerhart & Bretz, 1994).

Administration of employee compensation

The last aspect of employee compensation is the administration. This refers to who is responsible for the design, execution and communication of the pay policy. Whereas for CEO compensation, the remuneration committee is responsible, this responsibility is not as straightforward for employee pay. According to Milkovich and Newman (2008), human resource management departments together with line managers most often design the plans. However, sometimes the employees themselves may also be involved in determining their own pay. After the design of pay

3 Gainsharing is a form of pay-for-performance in which financial rewards for individual employees are determined based upon performance of an entire unit (Welbourne, Balkin & Gomez-Mejia, 1995). 4 Ownership is not always a particular form of direct compensation, but its existence may provide extra input for performance improvement. Moreover, many companies have adopted employee stock ownership plans (ESOPs) in which employees can buy shares at a discounted rate (Bova, Dou & Hope, 2015). Also, stock option plans (warrant plans), just like for executives exists (Gerhart & Bretz, 1994).

policies it is important that companies communicate the decisions in a clear way, as this is what really determines an employee’s perception of the pay, which in turn does (not) trigger the behavior wanted (Gerhart et al., 1995).

2.4.2 Theories and determinants Many theories that did apply to executive compensation do also reappear in the employee compensation literature. Moreover, again not a single theory has the power to explain the patterns in employee compensation on its own. The overarching idea behind the theories is the reasoning that the pay must influence the behavior of the employees in a desirable way.

Agency theory works in the same way as for CEO compensation (i.e. an agency problem exists). However, the agency problem exists between the managers (principals) and their employees (agents). In order to avoid minimize the agency costs (e.g. shrinking), contracts are designed that align the interests. According to Gerhart et al. (1995), this is done by either outcome – (e.g. gainsharing, merit bonus) or behavior-oriented pay (e.g. merit pay). Which particular contract needs to be chosen depends upon several factors as proposed by Eisenhardt (1989) and later by Perkins, White and Jones (2016): risk aversion, outcome uncertainty, job programmability, measurable outcomes, ability to pay and tradition.

Reinforcement theory argues that certain reactions of employees are likely to reoccur when these are rewarded (Lepper & Greene, 2015). Nevertheless, it is needed that the employee actually experiences the reward, hence communication needs to be strong.

From reinforcement theory it is small step to expectancy theory (Vroom, 1964; Milner, 2015). The core idea is the same: rewards trigger future behavior. However, the difference is that not experienced rewards matter, but rather the expected rewards.

The equity theory states that employees compare their pay in relation to their contribution to the processes in a firm and further how this ratio relates to their peers within and outside the firm (Adams, 1965; Gomez-Mejia et al., 2014). Hence, in order to trigger the wanted behavior, compensation has to be perceived as equitable, because otherwise employees may conduct harmful actions (e.g. Greenberg, 1990).

The relative bargaining theory as proposed by Hayes and Schaefer (1999) and mentioned before, explains pay from the perspective of the bargaining power each party in the procedure. In the case of employee compensation this is the employee versus the human resource – and/ or line manager. According to Faleye et al. (2013), the two main factors that influence bargaining power for the employees are their skill level (i.e. harder to replace) and the unionization (i.e. aggregating the employee claims).

The last theory conflicts with the other theories as it states that even similar firms, which should normally have then the same compensation policy can have differences based on strategic choices (Caroll, 1987). This strategy theory/ perspective can be observed if the organizational choices and effects are stable over time. In a study by Gerhart and Milkovich (1990), they find evidence that supports this view.

Marginal productivity -, human capital – and efficiency wage theory all work in the same way for “regular” employees as they do for the executives’ compensation. So, for these we refer to the previous section.

Determinants

The fact that a large set of theories exists has translated into a vast amount of research performed on specific determinants of employee compensation. It is not our intention to repeat all determinants, for this we refer to overviews created by for instance Werner and Ward (2004). Here, we only briefly touch upon the possible viewpoints one can consider.

First, environmental factors are to be split in legal, business and industry determinants. Legal factors are for instance, minimum wage (Addison & Balckburn, 1999) and layoff legislation (Friesen, 1996), which are both reported to have a positive effect on wages. Business factors deal with the general state of the economy and its development (for instance inflation as researched by Christofides and Laporte in 2002).

Second, firm specific factors are to be split in unionization level and other firm characteristics. For unionization, the effect on employee compensation is not completely clear, yet the general view seems that it has a positive effect. Under other characteristics many factors are captured. Brown and Campbell (2002) argue that the need for (more) skills and technology needed are positively linked to wages. Also, size has been the subject of several papers. For instance, Ang, Slaughter and Yee Ng (2002), and Kalleberg and van Buren (1996) find that size is positively related to compensation, as larger companies are able to compensate more, have higher monitoring costs and are in need for more expertized workers.

Third, job determinants that influence compensation are widespread and to be spread into job traits, - attributes, - tasks, -requirements, -contingency, -centrality, -seniority, -payment methods and – reengineering. One of the more important and recognized outcomes is that skills and thus job requirements (often expressed in terms of education) are positively associated with hourly starting wages (Holzer, 1998). Moreover, research on job attributes (e.g. use computers, on-call workers, shift work) shows that these factors influence compensation (Bender & Elliott, 2002).

2.4.3 Conclusion From this section, we can derive a similar conclusion as for the CEO compensation in Section 2.3. Again, multiple theories seem to explain at least a part of the employee compensation, but none seems possible to explain it solely. Moreover, we observe that many theories apply to both components of the CEO pay ratio.

Further, also several academics in this field of study argue that not an isolated view should be taken into account. Instead, one should observe the ratio through the lens of the institutional framework as mentioned in Section 2.1. This is for instance argued by Balkin (2008), they mention that in essence it is impossible to explain differences in levels, structures and mixes if one does not take the institutions into account.

This section gives us new insights for the eventual goal of this study: determining the CEO pay ratio. As it provides the dynamics of the second component of the ratio, namely the (average) employee compensation. Two important points follow from here. First, again multiple theories can explain some part of the employee compensation, but no single almighty theory exists. Hence, this advocates for our approach of using multiple angles to determine the CEO pay ratio. Especially, in combination with the same conclusion for CEO compensation, this approach seems reasonable. Second, to determine average employee compensation we use all possible elements of compensation (i.e. base with merits, bonuses, gainsharing and other benefits), which is in line with previous research. Only, stock ownership is left out, as this is relatively hard to measure, or it is not required to be disclosed for the companies in our dataset.

2.5 BACK TO THE CEO PAY RATIO In Section 2.3, we developed the background of CEO compensation, whereas in Section 2.4 we discussed the dynamics of employee compensation. Together, these are the ultimate drivers of the CEO pay ratio. Nevertheless, it is not necessary that the theories of these components also apply to the CEO pay ratio. In this light, academics have recently started to build upon the existing knowledge to also explain the CEO pay ratio. In this section, we discuss this literature, which has brought forward four different lines of reasoning. Eventually, the knowledge from this section helps us, together with the previous sections to build a theoretical background from which we can develop the hypotheses on the determinants of the CEO pay ratio, which is discussed in Chapter 3.

2.5.1 Theoretical perspectives on the CEO pay ratio Agency theory is again divided in two separate theories: managerial rent-seeking/ power and efficient contracting. Both have been reviewed in the light of the CEO pay ratio, but again no conclusive view has been established. First, efficient contracting is often argued to trigger

tournament theory effects, which in turn cause an increased CEO pay ratio (further discussed below). Also, Chang, Dasgupta and Hilary (2010) argue, a higher CEO pay ratio is often associated by shareholders as the contribution a CEO has to the firm’s performance, this performance link is at the core of efficient contracting/ agency theory. Second, managerial power theory argues again that higher CEO pay ratios are the consequence of (and not the remedy against) agency problems. Cremers and Palia (2011) argue that rent-seeking behavior will inflate the CEO compensation, which leads to an increased CEO pay ratio. Although, the difference between efficient contracting and managerial power is the arguable the most researched view on CEO pay ratio, there is still much debate which theory actually explains the phenomenon. The problem has been that the methodology used so far had difficulty in differentiating the two theories in a distinctive way. The first to tackle this problem were Vo and Canil (2016). They found evidence that supports the managerial power theory.

Second, tournament theory has been used to explain pay disparity within firms. As said before, tournament theory (Lazear & Rosen, 1981) argues that different hierarchical levels have increased pay levels (thus creating gaps) in order to stimulate employees to move up the corporate ladder by outperforming each other. Obviously, this explains the CEO pay ratio, but it does not necessarily justify the extreme gaps that are observed. Lin, Yeh and Shih (2013) argue that the more influential hierarchy levels are on a firm’s performance, the larger the pay gap. Thus, CEOs who arguably have the largest impact, will have the largest pay gap (i.e. increasing the CEO pay ratio). Research done by Shin, et al. (2015) provides evidence for the explanatory power of tournament theory (by means of proxies) for CEO pay multiples in the Korean market.

Relative bargaining power theory is used by several academics to shed light on the CEO pay ratio, because of the social exchange that is happening when determining the CEO’s and workers’ pay. This approach is researched for US CEOs by Faleye et al. (2013). They found results that are consistent with the relative bargaining theory. Moreover, Shin (2014) argues that the CEO pay ratio is affected by relative bargaining power in three ways. First, CEOs may have excess power over the board when determining pay. Second, employees may have power over their managers in setting worker wages. Lastly, employees can have influence on CEO compensation. These factors together determine the relative pay the CEO gets compared to the average worker.

Lastly, with the equity fairness and social comparison theory a more behavioral approach is chosen to determine the CEO pay ratio. Although, the core principle of the theories is that CEOs compare their compensation with peers, several academics (e.g. Bebchuk, Cremers & Peyer, 2011; Shin et al., 2015) argue that there is also a comparison from employees to their CEO’s pay (i.e. outside their peer group) to determine whether their pay is fair. This because employees strongly experience/ observe the actions of CEOs and they can easily access data about CEO compensation

(Wade, O’Reilly & Pollock, 2006). Unfair pay (i.e. a too-high CEO pay ratio) may create inequity feelings, which in turn affects cooperation, loyalty etc. Eventually, this will negatively impact firm’s performance and thus pay needs to be adjusted, to take down the inequity feeling.

2.5.2 Conclusion Just as for the theories on executive compensation, there is no sole theory that can explain the CEO pay ratio (Lin et al., 2013). Again, the most valuable approach is an integration or combination of several theories, which is done by for instance Shin et al. (2015); Balsam et al. (2016). Another reason for this approach is that in general the amount of research on determinants/ theories of the CEO pay ratio is limited, so academics are interested in establishing this knowledge first before validating theories further (Shin et al., 2015). Our research follows this line of thinking. Hence, rather than taking one explanatory theory, we derive a set of determinants based on multiple theories. Nevertheless, the theories mentioned so far should be important guidance for this and future research.

3 DETERMINANTS OF THE CEO PAY RATIO: HYPOTHESES

3.1 DETERMINANTS OF THE CEO PAY RATIO BETWEEN COUNTRIES In this section, we discuss the determinants of the CEO pay ratio that trigger differences between countries. Specifically, we focus on the legal and social/ cultural determinants. This focus is chosen, because it is, to our knowledge, not yet combined in the research on CEO pay ratios. Moreover, we believe that it is valuable to observe whether the CEO pay ratio is driven by enforced rules or by the ‘rules’ a country establishes through their norms and values.

3.1.1 Legal determinants and hypotheses development A key factor in governance systems influencing executive pay is the legal framework, which consists of statutory laws, informal codes/ rules and the enforcement of these (Hermalin & Weisbach, 2017). Shleifer and Vishny (1997) and later LaPorta, Lopez-de-Silanes, Shleifer and Vishny (LLSV, 1998) were the most impactful with their research on this relation. Both argue that other corporate governance mechanisms are only useful if the laws and legal system in place enforce shareholder and creditors protection. The metric for protection of shareholders by LaPorta et al. (1998) has become the most used in empirical research. From there, several researchers have developed advanced metrics, for instance Martynova and Renneboog (2010) build a governance index based upon three major agency conflicts, which makes it better scoped than the LLSV index.

Research on the relation between corporate governance and executive compensation does exist. Exemplary, is the research by van Essen et al. (2012), who test two different forms of formal corporate governance (investor protection and the overall development of the jurisdiction’s legal system) and informal corporate governance (e.g. ownership concentration) with relation to the pay-for-performance relation and find significant results for their associations. Yet, the CEO pay ratio is not well researched in the light of the legal corporate governance.5 Hence, we develop hypotheses that try to fill this gap in the literature. The main reasoning behind these hypotheses is the same as for the relation to executive compensation, namely the reduction of management rent extraction (i.e. the managerial power theory).

First, investor protection laws come into the pay-setting procedure to limit the possibilities for rent-extraction by managers over shareholders (e.g. Leuz, Nanda & Wysocki, 2003). Essentially, the existence of good investor protection laws downgrades the relative bargaining power a CEO

5 Balsam et al. (2016) do research this factor in combination with many other cultural factors. They find that shareholder protection laws (proxied by legal origin) is negatively related to CEO-worker pay ratio.

has, which likely reduces the excessive pay received. Hence, we believe that the CEO pay ratio will be lower if investors are better protected.

Hypothesis 1A: the CEO pay ratio is negatively related to better investor protection laws.

Essentially, corporate governance codes are a form of “soft legislation” and thus also informal (Aguilera & Cuervo-Cazurra, 2004). They are not actively enforced, but instead companies and CEOs are obligated to public justification whenever they do not follow the guidelines (Wade et al., 1997). This is likely associated with reputational loss and thus with a drop in their own and company value (Dixit, 2011). Therefore, we expect CEOs to be constrained in obtaining excessive pay and creating a larger gap if good codes of corporate governance exist.

Hypothesis 1B: the CEO pay ratio is negatively related to better developed corporate governance.

Lastly, in the literature there is not yet a consensus over whether formal and informal institutions are complementary or substitutes (van Essen et al., 2012). Hence, we will also test whether interaction between developed corporate governance and the investor protection laws has an effect on the CEO pay ratio. It is likely that the combination of these two would strengthen the impact.

3.1.2 Social/ cultural determinants and hypotheses development According to Guiso, Sapienza and Zingales (2009), culture has an effect on beliefs, preferences and economic decision-making. Moreover, the society is seen as the unwritten “legislated” environment actions are taken in. Especially, research on the effect of culture on CEO compensation is well-known and acknowledged. The most recognizable research is done by Tosi and Greckhamer (2004), who relate Hofstede’s cultural dimensions (Hofstede, 2003) to CEO compensation in 23 countries. Eventually, concluding that power differences, individualism and masculinity appear with an increase in CEO compensation. Later, some academics have started to also relate cultural factors to the CEO pay ratio or equivalents (e.g. CEO pay gap, CEO pay slice), because culture influences organizational and individual’s behavior (e.g. the acceptance of a pay gap). The first to research CEO pay gaps and the influence of culture was Grenness (2011), who found that power distance is positively and individualism negatively correlated with (CEO) pay gaps. 6 Burns and Minnick (2013) argue that tournament theory structures are steeper in countries with higher power distance, this indirectly indicates that CEO pay will be higher in those countries as well as the CEO pay ratio based on the applicability of tournament theory. Based on these two researches, Balsam et al. (2016) were the first to perform an integrated study on 44 countries over twelve year, taking into account all cultural factors and some societal factors. Next

6 Grenness (2011) found a relation between CEO pay gap and power distance and individualism, but the results were not as significant as for other pay gaps within the dataset (e.g. worker and mid-management).

to results in line with previous work on Hofstede’s dimensions, they also found that the GINI coefficient (income inequality) is positively related to pay ratio. Our hypotheses for this section are partially based on the work by Balsam et al. (2016), but we also add some new variables in order to obtain an even better picture of the societal drivers of the CEO pay ratio. Cultural factors are based on the dimension by Hofstede (2003).

Cultural

Power distance defines how less powerful people readily accept unequal power distribution within an organization/ society. It is best explained in terms of the acceptance of a hierarchical order. The more hierarchy is accepted, the larger the power distance (Hofstede, 2003). Essentially, if the power distance score is low, people like to obtain justification for the differences in hierarchy. Whereas, in high power distance countries the hierarchy will be steeper/ stronger, which gives rise to more different pay levels, which in turn increase pay gaps if measured over several hierarchical levels (Balsam et al., 2016). Moreover, high power distance societies have managers who try to maximize their income (Faleye et al., 2013). This is consistent with the managerial power theory and also indicated by previous research (Tosi & Greckhamer, 2004), which results in the following hypothesis:

Hypothesis 2A: the CEO pay ratio is positively related to the acceptance of hierarchy in a country

Individualism is the extent managers prefer to simply consider their own benefits and not directly “care” about others, compared to collectivism where persons do look after other members of the society (Hofstede, 2003). This cultural characteristic can be related to managerial power theory as it is likely that in an individualistic society the manager is more eager to search for rents and obtain a higher compensation (i.e. a higher pay ratio), as he does not care about the consequence for others. The observation between an increase in compensation for CEOs and higher levels of individualism is also concluded by Tosi and Greckhamer (2004). Even though, employees may show similar behavior, their success (i.e. higher pay) is less likely as they generally do not have a large portion of pay that is influenced by performance, especially relative to the CEO.

Hypothesis 2B: the CEO pay ratio is positively related to the level of individualism in a country

Uncertainty avoidance is present in societies that do not accept ambiguity and try to control for the unknown (de Luque & Javidan, 2004). Moreover, de Luque and Javidan argue that this avoidance creates risk aversion, which can mean that people desire compensation in a different form than variable/ equity based (as this is riskier from its nature). This is also argued by Pennings (1993), who also states that since fixed pay is a smaller portion of total pay for the CEO, it becomes likely that total CEO compensation relative to the average employee’s compensation becomes smaller (i.e. lower CEO pay ratio). Thus, we conclude that:

Hypothesis 2C: the CEO pay ratio is negatively related to the level in which people are risk-averse in a country.

Long-term orientation (vs. short-term orientation) is related to how organizations and societies focus on the future and/ or look at the past. For instance, short-term oriented societies (companies) may have more arguments against futuristic R&D projects and in turn want a short- term directly measurable outcome (Lumpkin, Brigham and Moss, 2010). Essentially, a job can also be seen as a project and it is then reasonable that for those CEOs who are short-term oriented, rather quick rewards are desired (Gomez-Mejia & Werner, 2008). This can cause rent-seeking behavior, which likely increases the CEO pay ratio. Further, it is often observed that long-term orientation is closely related to uncertainty avoidance, because both show a rather passive strategy (Zhao, 2000). Thus, our hypothesis shows the same sign as in Hypothesis 2C:

Hypothesis 2D: the CEO pay ratio is negatively related to the extent people are forward-looking/ pragmatic (long-term focused).

Masculinity (vs. femininity) deals with the role’s distribution between gender in a society. In organizations this translates into a more competitive (i.e. masculine) or a more cooperative (i.e. feminine) strategy or work-atmosphere (Hofstede, 2003). A more competitive nature is regarded as a strong encouragement for the tournament theory, particularly at the lower organizational levels (Goel & Thakor, 2008). Hence, the incumbents of these lower levels will try to increase their performance, in order to move up the corporate ladder. This upward movement is often associated with an increase in salary (i.e. average employee compensation increases as well). Whereas, a CEO is already on the top of the ladder and is not directly influenced by this masculine culture (Gomez-Mejia & Welbourne, 1991). This leads to the following hypothesis:

Hypothesis 2E: the CEO pay ratio is negatively related the observed degree of masculinity in a country

Indulgence measures the extent to which people in a society try to control impulses and desires (Hofstede, 2003). In a more indulgent society, people are less restricted and tend to act more upon their desires. Whereas in a restrained society, people act more disciplined and behave in line with social rules and norms. Therefore, it is likely that in an indulgent society, CEOs and employees behave in a way that fosters their own benefits while ignoring the effects on the company’s performance. This may cause rent-seeking behavior (by aiming for own benefits) but on the other hand destruct the effect of tournament incentives (i.e. lower pay differentials) as stated by Balsam et al. (2016). Hence, we hypothesize the following:

Hypothesis 2F: the CEO pay ratio is related to the extent people try to control their impulses and desires

Societal

The societal determinants are closely linked to social comparison and equity fairness theory as discussed in Chapter 2. Key here is the feeling of (in)justice people obtain from the relative income distribution in a country/ society. This statement is underlying to the two hypotheses we formed related to this subject, as discussed next.

In essence, there exists various ways to look at the relative (income) inequality within a country or society. We base our hypothesis on three such measures and their relation to the CEO pay ratio. First, the Gini Index7 measures income distribution inequality. Based on equity fairness theory, we argue that the Gini coefficient implies a macro-justice for income distribution. This is likely to influence the micro-justice of the income distribution at a company level (of which CEO pay ratios are one of the direct outcomes). Hence, we think that a country with large inequality (i.e. a large Gini coefficient) is also likely to have higher CEO pay ratios. Moreover, Burns and Minnick (2013) argue that the Gini coefficient relates to the existence of tournament structures. In a general tournament structure, it is observed that pay differentials tend to increase when moving up the corporate ladder. Second and third, unemployment and poverty rates are both recognized as proxies of (income) inequality. Although, a society can create wealth efficient and equitably, thus lowering poverty and unemployment rates (Judge, Fainshmidt & Brown III, 2014). Yet, CEOs will always search for their own opportunities to become richer, leaving unemployment/ poverty as is and thus increasing CEO pay ratios. Furthermore, if unemployment or poverty is relatively higher, employees have a relative worse position on the job market, leading to lower wages and higher ratios (Bellman & Blien, 2001). All three proxies for (income) inequality within a country or society come to argue the same relation with the CEO pay ratio, thus we hypothesize:

Hypothesis 2G: the CEO pay ratio is positively related to the degree of (income) inequality in a country.

Lastly, we look at the relative bargaining power that employees may have. As stated before, this relative bargaining power provides opportunities in wage negotiations for employees (i.e. higher average employee wage). Moreover, a stronger employee position also echoes in the CEO compensation setting process (i.e. lower CEO compensation), as threats are more viable. Especially, if employees unionize their relative bargaining power increases (Faleye et al., 2013). The unions are principally against a too large pay gap and believe it is unfair (Barringer & Milkovich, 1998). Given the two opposite effects the presence of unions in a country may have on

7 The Gini index is a measurement for inequality in income distributions. The index is based on the Lorenz curve, which outlines the percentage of income (of the population) that is cumulatively gained by the bottom %. The Gini index takes a value between 0 (complete equality) and 1 (complete inequality), in which the value states how much the difference between bottom and top earners is.

both the CEO compensation and the (average) employee compensation, we draw the following hypothesis:

Hypothesis 2H: the CEO pay ratio is negatively related to the unionization level.

3.2 DETERMINANTS OF THE CEO PAY RATIO WITHIN COUNTRIES So far, we have discussed the factors that influence CEO pay ratios on a country level. Yet, many of the factors that shape this ratio are internal (governance) mechanisms or characteristics. In this section, we discuss briefly the limited existing work on firm-level determinants of the CEO pay ratio. Next, we develop the hypotheses in the same fashion as before.

3.2.1 Overview Literature on the determinants of executive compensation is extremely extensive and a stand- alone study to review. Since, we are focusing on the CEO pay ratio we do not review all this “regular” executive compensation literature here. Though, some work exists on the determinants of the ratio. Academics seem to agree that the CEO pay ratio is heavily dependent on economic conditions within a firm (e.g. Henderson & Fredrickson, 2001; Kale, Reis & Venkateswaran, 2009). Size is found to be a huge factor as with the increase of a company, tournament incentives become larger/ pay ratio increases (Siegel & Hambrick, 2005). It is also argued that, if it becomes harder to identify individual effort, the pay disparity grows. This because tournament incentives may restrain individual’s effort-dampening attitude (Henderson & Fredrickson, 2001). According to Cowherd and Levine (1992), productivity, human capital and status also affect pay disparity. The study by Bebchuk et al. (2011) researched the CEO pay slice (CPS) and its determinants (for instance ROA, CEO-chair duality and company age). The most comprehensive study on firm-level factors is done by Shin et al. (2015) with Korean data.8 With this research, we also aim at including a wider spectrum of variables that potentially determine the CEO pay ratio.

3.2.2 Firm-level hypotheses Generally spoken, when the skill-set required for acting as CEO needs to expand, the CEO pay (and likely the ratio) increases as well (Core, Holthausen & Larcker, 1999). As stated by Bloom and Michel (2002), highly innovative companies require more skillful managers and employees (for instance, being capable of working under pressure, in new-markets or with high-tech). It is then likely that, in order to attract talent, these companies pay higher average compensation (Core et al., 1999). However, the total compensation is set to increase even more when moving up the corporate ladder to the executive level/ CEO (Balkin, Markman & Gomez-Mejia, 2000). One

8 Korea has rules that each publicly listed company must disclose the average annual employee costs and the CEO compensation in their annual report. Hence, it was relatively easy to obtain the data/ calculate the ratios.

potential other argument, next to the skillset-based view (i.e. must grow exponentially for the CEO in innovative firms to keep up with his employees) is that often CEOs have part of their variable compensation linked to the achievement of certain innovative goals (Balkin & Gomez-Mejia, 1990). Hence, it is likely that CEO compensation is relatively higher (or at least increases faster) than the average employee compensation, leading to the following hypothesis:

Hypothesis 3A: the CEO pay ratio is positively associated with the degree of innovation within a firm

Obviously, the outside-world impression of CEO compensation is valuable for companies. However, the CEO pay ratio is in this regard even more important, because it directly gives a tool to the public to form an opinion about the remuneration of a specific company and its policy’s perceived “fairness”.

Research by Dyck and Zingales (2002) and later Dyck, Volchkova and Zingales (2008), shows that the media guides/ triggers companies to act “socially acceptable”. Intuitively, we believe that this can imply that the CEO pay ratios may not become too excessively large and that companies might adjust executive compensation (i.e. have no increase for base salaries or target incentives). The effects of the public opinion about a company may especially be eminent for certain types of companies. First, companies that are in direct contact with end-consumers i.e. the business-to- consumer market (“B2C”) have a stronger incentive to keep up their reputation, because consumers can directly associate the company (and its “bad” CEO pay ratio) with their brands and products, which in turn may cause them to switch, boycott etc. Second, companies that employ a sustainable strategy/ culture or sell sustainable products are likely to also act more “sustainable” and socially accepted in terms of their remuneration, because if not, the consumer market may punish them for the misalignment between strategy/ communication and their remuneration policy. However, the same effect as innovation regarding the connection between incentive/ variable pay and performance (here on sustainability), may increase the CEO compensation relatively faster. Given these two opposite effects we do not form a sign hypothesis about the relation between sustainability and the CEO pay ratio. Thus, from this paragraph the following two hypothesis are drawn:

Hypothesis 3B: the CEO pay ratio is negatively associated with firms in the business-to-customer market

Hypothesis 3C: the CEO pay ratio is associated with the degree of sustainability of a firm

Lastly, next to formal (country) legal institutions, informal institutions influence pay-setting as well (van Essen et al., 2012). These informal institutions appear randomly as a response to certain problems and are maintained by self-re-establishing certain behavior instead of third-party

enforced formal rules (e.g. a government). One of the most widely known is the existence of block holders or ownership concentration, which is seen as a substitute for worse investor protection rights (Hartzell & Starks, 2003; Peng & Jiang, 2010; Schleifer & Vishny, 1997). Essentially, the same logic as for formal institutions does then apply (i.e. better investor protection decreases CEO compensation and consequently the CEO pay ratio), forming the following hypothesis:

Hypothesis 3D: the CEO pay ratio is negatively related to larger ownership concentration

3.2.3 Conclusion The review of literature in Chapter 2 and this chapter tells us that the existing body of research on both executive compensation and employee pay is large, but not unambiguously. Instead, a widespread range of theories exists of which none has consistently proven its value. Further, the theories on the CEO pay ratio are limited, due to the novelty of the theme or the lack of data. This is also the case for the studies for determinants of CEO pay ratios. Hence, a gap in the literature exists and this is where our research should fit in. First, we provide an analysis of a comprehensive set of determinants of the CEO pay ratio both at country as well as firm-level. Second, we perform this research with European data (including the UK). This as opposed to previous research which mainly used either US or UK data solely. Altogether, this can provide practitioners and academics with new insights in the CEO pay ratio.

4 DATA AND METHODOLOGY

In this chapter, we outline the used data and methodology. In Section 4.1, we discuss the sample selection and the combination of databases/ sources accessed to establish this dataset. Section 4.2 elaborates on the specific variables used based on the hypotheses in Chapter 3 and introduces the control variables. Lastly, Section 4.3 describes the methodology in terms of the regressions used and further potentially related statistical problems.

4.1 SAMPLE AND DATA SOURCES Our sample consists of listed firms at the end of July 2018 on the main indices of nine European countries: the Netherlands (AEX), Belgium (BEL20), France (CAC40), Spain (IBEX35), Switzerland (SMO), Italy (FTSE-MIB), Germany (DAX), Sweden (OMX) and the United Kingdom (FTSE100). This sample is selected because it provides us with context regarding the focus of our research, the Netherlands. Due to the limited availability of accurate and complete data(bases) on CEO compensation in Europe, we perform a cross-sectional analysis over the year 2017 only.

To construct our final sample, we excluded a limited number of data points. For a multi-listed company, there is the dilemma of selection the listing being used in the dataset. We selected the listing in either the country of origin or their arguable ‘primary’ listing. For instance, Arcelor Mittal SA is listed on the AEX, CAC40 and the IBEX35, but has a main listing in the Netherlands (outside Luxembourg), hence we kept the listing on the AEX. Performing this analysis for all multi-listed companies yields a sample that consists of 325 companies. We also excluded Tenaris SA (FTSE MIB) and Scottish Mortgage Investment Trust (FTSE 100), respectively because no CEO compensation was disclosed and it being a trust company build around partnership (with no “real” CEO), leaving the final sample to consist of 323 companies. In Appendix A, we provide an overview of the sample companies and their scope parameters (turnover, assets, market capitalization, headcount and GICS subindustry).

The data is collected from a variety of sources, as we approach the CEO pay ratio from various angles. Since we calculate the CEO pay ratio ourselves (as discussed in Section 4.2), we need various inputs. The most-known database for CEO compensation is BoardEx. Yet, the quality and consistency of the data for European companies (except a large part of the UK companies) is lacking. Thus, to create a qualitatively strong dataset we hand-collected most of the compensation data (except for approximately half of the FTSE 100 that came from BoardEx) from annual reports or remuneration reports over the latest reporting/ fiscal year. For most of the companies this was the calendar year 2017, but for those applying a broken financial year this meant data from e.g. June/ July 2017 – 2018. Second, the total employee costs and the number of (full-time) employees

is in principal obtained through Thomson Reuters Datastream and were needed complemented by hand-collected data (for approximately 80 companies).

Next, all other financial variables for the firms are collected mainly from Thomson-Reuters Datastream and where needed complemented with data from Orbis (part of Bureau van Dijk). For the cultural variables we use the classification by Hofstede as disclosed on his corporate website: www.hofstede-insights.com. Social variables are derived from the database of the OECD (Organization for Economic Co-operation and Development) or the World bank. Data on corporate governance and law are based on previous academic work (LaPorta et al., 1998; Martynova & Renneboog, 2010).

4.2 VARIABLE DESCRIPTION In this section, we outline the variables used in our analysis based on the described hypotheses in Chapter 3. We start by introducing the definition used for our dependent and independent variables. Next, we outline the control variables or fixed effects variables that make our results more robust. For an overview of all the variables included in our regressions we refer to Appendix B.

4.2.1 Dependent variable The CEO pay ratio is our key variable of interest. As became clear from our framework on corporate governance (Chapter 2), different methods exist to calculate the CEO pay ratio. In this research, we follow the common practice in the Netherlands. Thus, we calculate the CEO pay ratio as follows:

푡표푡푎푙 퐶퐸푂 푐표푚푝푒푛푠푎푡푖표푛 퐶퐸푂 푝푎푦 푟푎푡푖표 = 푎푣푒푟푎푔푒 푒푚푝푙표푦푒푒 푐표푚푝푒푛푠푎푡푖표푛

The total CEO compensation consist of the actual base salary, annual incentives (e.g. bonuses), the grant fair value (based on IFRS29) of long-term incentives (e.g. stock awards) and other benefits (such as pensions and perks). This is in line with the definition of total compensation as defined in Chapter 2. The average employee compensation is based on the total employee costs of a company, consisting of the base salary and all types of variable compensation and other benefits (e.g. pension, social security), minus the total CEO compensation; then divided by the total number of employees (preferably full-time equivalent) minus one (the CEO).

9 IFRS2 are accounting standards originally published in 2004, with regard to the disclosure of share-based payments in the financial statements. It requires companies to not only mention actual pay-outs to beneficiary, but also the fair value at grant to account for the costs/ expense in the relevant reporting period. For more information, we refer to: https://www.ifrs.org/issued-standards/list- of-standards/ifrs-2-share-based-payment/

This approach means that we do not use the disclosed ratios (for the AEX or legally Dutch incorporated companies) as these may vary strongly in either the calculation method or the determination of the elements. Instead, we do follow the standard practice and the industry preferred approach, which creates uniformity in the output and analyses.

4.2.2 Independent variables

Here, we further elaborate on the variables that are hypothesized to (partially) determine the CEO pay ratio. In line with Chapter 3, we start with the country specific variables related to legal and then societal/ cultural factors, followed by the firm level factors.

Legal variables

To measure investor protection, we use the anti-director index as developed by LaPorta et al. (1998). Next, we also use the shareholder rights protection framework from Martynova and Renneboog (2010), as it is arguable more in depth than the basic framework by LLSV. The shareholder rights protection framework assigns scores in four subcategories: appointment-, decision-, trusteeship- and transparency rights, which eventually results in a total (shareholder protection) score between zero and 32.

Country’s corporate governance can be measured in several ways. The World bank initiated the Worldwide Governance Indicators (WGI) project, which measures governance in six dimensions. We use the variable “regulatory quality” as a proxy for the ‘goodness’ of governance. According to the World bank (2018) this reflects: “perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development”. A score is provided from -2.5 (weak) to 2.5 (strong) governance performance in terms of “regulatory quality”.

Another proxy for country’s corporate governance is the corporate tax rate, which is measured as the highest marginal rate applicable to companies (Desai & Dharmapala, 2008). For instance, in the Netherlands the corporate tax rate is 20% until a gross profit of 200,000, followed by a 25% rate for all income above this amount. Then, in our data we take 25% as the applicable percentage. Furthermore, some countries in our dataset like Switzerland have a significant portion of their taxes delegated to local authorities (e.g. cantons). In these cases, we took the average of these rates and added this to the federal tax rate.

Societal and cultural variables

Our cultural variables are derived from the theory by Hofstede (2003). Thus, power distance, individualism, uncertainty avoidance, long-term orientation, masculinity and indulgence are based on the most recent (2010) scores provided on Hofstede’s website. The scores range from

zero to hundred, with the higher score meaning the higher degree of a certain measure. Although, the scores are not very recent, we still believe they (approximately) illustrate the cultural dimensions for 2017 as well. This because cultural change is a rather slow process and thus likely not influencing these inputs.

Since the OECD database is one of the largest databases in the world regarding macroeconomic statistics, we use it as our main input for societal factors. First, as a proxy for inequality we obtain the GINI coefficient, which is the common proxy for inequality in comparable research. Second, the unemployment rate, which according to OECD measures the number of unemployed people as a percentage of the total labor force (unemployed plus the paid or self-employed). Wherein an unemployed person is someone who reports to be out of a job, available and actively seeking for a job (i.e. searched in the last four weeks). Third, the poverty rate is the ratio of the number of people whose income is below a certain threshold (according to OECD: half the median household income for the population). Fourth, unionization characteristics data is gathered from OECD. In this research, we use trade union density as the proxy for the ‘number of people affiliated with unions’, which serves as a determinant of relative bargaining power of employees. Trade union density represents the ratio of the wage and salary earners, which are union members, respective to the total labor force (i.e. all wage and salary earners).

Firm level variables

Innovation is measured through the intangible assets of a firm scaled by total assets. In the literature, intangibles are often referred to as “knowledge- or intellectual assets”. These two definitions also touch upon the key of innovation: development, which often requires special intellectual insights. Moreover, intangibles consist of for instance patents and R&D developments, which can be direct outputs of innovation. Later, academics (Canibano, García-Ayuso, Sánchez & Olea, 1999; Kaufman & Schneider, 2004) established this relation empirically, which makes it justified to use as a proxy.

For the variable that tells if a company operates in the business-to-consumer market (“B2C”), we developed a dummy variable that takes value one if a company is B2C or zero if not. Our initial step was to classify those sectors and industries (within the GICS classifications), based on industry descriptions that had (the majority of) their constituents in the B2C market:

- Consumer Discretionary: GICS code 2510 to 2540, excluding Auto Parts & Equipment and Tires & Rubbers. - Consumer Staples: GICS code 3010 to 3030. - Healthcare: GICS code 3510.

- Financials: GICS code 4010 and 4030, excluding Insurance Brokers and Reinsurance. Further, include Consumer Finance (part of GICS 4020). - Communication Services: GICS code 5010 to 5020, excluding Advertising and Publishing. - Utilities: GICS 5510.

Moreover, we included some additional subindustries based upon companies’ descriptions or the industry description, namely: Integrated Oil & Gas, Oil and Gas Refining & Marketing (both GICS 1010), Industrial Conglomerates (GICS 2010), Human Resources & Employment Services (GICS 2020), various Transportation based upon company description (GICS 2030) and Technology Hardware, Storage & Peripherals (GICS 4520).

To capture a firm’s effort with regards to sustainability, we employ two ESG scores10. The first score is the workforce score, which captures the effectiveness of companies to obtain job satisfaction, supporting workplaces, diversity, equality and development opportunities for all employees. Next, the total sustainability score considers all ESG scores measures (14 categories) and thus provides an integrated view on the sustainability efforts from a company.

Theoretically, the definition of a blockholder is arbitrary and not pinned down to one “right” number (Edmans & Holderness, 2017). Yet, common practice is to define the blockholder as any shareholder that possesses at least five percent of the company’s outstanding shares. Yet, since ownership data is rather limited in for instance Italy, we choose another measure that serves as a proxy for ownership concentration. The free-floating percentage is the percentage of common shares that is freely tradeable for the public at defined exchange markets. This means that holdings by either family, institutional investors, governments are not part of this ‘free float’. With the exclusion of these shareholders, one captures the existence of ownership concentration as in general all remaining free-floating shares are rather dispersed (see for instance Pedersen & Thomson, 1997; Gamerschlag, Möller & Verbeeten, 2011).

4.2.3 Control variables In this study, a selection of control variables is used, based on prior research in the field of CEO compensation. For the complete list of variables, we refer to Appendix B.

Based on basic agency theory and the ever-existing debate in the regular executive compensation literature, we control for the effect performance may have on the CEO pay ratio. Naturally, we expect a positive relationship between firm’s performance and CEO pay ratio, because of the way

10 ESG scores are determined by analyst from an analysis on company’s exposure and management of key sustainability theses. The scores are built of environmental, social and government matters, where the first two may have different weights related to industry. Eventually, an outcome score is delivered which may range between 0 and 100. For the overall/ aggregated score a distraction for ESG controversies is taken into account from global media that materially impact the corporations. For more information see https://financial.thomsonreuters.com/content/dam/openweb/documents/pdf/financial/esg-scores-factsheet.pdf

CEO compensation incentives (i.e. tied to performance objectives) are structured and the relatively higher portion of incentive pay for executives than for average employees. Following previous similar financial research (e.g. Chhaochharia & Grinstein, 2009), we also make use of two proxies: one accounting based and another market-based. The first, is the return on assets (ROA), which is measured as the net income divided by the firm’s total assets. Yet, this measure solely might not be fully explanatory, hence we also test a market-based return measure. For that purpose, one of the most common measures is the (average) monthly total shareholder return (TSR), which we measured from January 1, 2017 to December 31, 2017.

Size is a determinant of complexity and requires additional skills for both executives and employees (Core et al., 1999). Hence, we need to control for this factor as it is very likely that it influences the CEO pay ratio. Given the nature of the CEO pay ratio, we use the number of employees as a proxy for size. To generate robustness, we also test the inclusion of the natural logarithm of sales (revenue or banking-revenue for financials11) as another proxy for size. This is a generally accepted method (Smith and Watts, 1992).

Further, we include firm risk and leverage in our analysis based on the study by Core et al. (1999), who state that more environmental noise (i.e. here we assume that risk and leverage are forms of noise), trigger higher monitor costs and thus the need for higher incentives especially for executives relatively to employees. Risk is measured with two different proxies. First, we used the levered beta of companies which shows the relative movement of the stock returns in relation to the market return. Second, we calculated the standard deviation based on the monthly returns in order to be aligned with the measure of performance. The chosen measures are in line with previous research related to CEO compensation (see for instance: Bugeja, Matolcsy & Spiropoulos, 2012). For the leverage of a firm, which may also represent the solvability, we work with the debt- to-equity ratio. This measure is a widely accepted instrument in both the industry as well as for academics (Chemmanur, Cheng & Zhang, 2013). Although, most of the companies also report this ratio in their annual report, we calculated the ratio based on the reported balance sheet.

Growth potential of companies (Bloom & Michel, 2002) is a rather difficult to capture factor as it will always be based upon expectations and forecasts but is also seen as a potential factor in CEO pay ratio determination. Fortunately, throughout the years, the industry and academics have widely accepted the use of the price-earnings ratio to capture growth potential (Penman, 1996). If the ratio has a large value (i.e. stock price is high relative to earnings) it is argued that investors belief in the future earnings potential of the company, which is thus captured in the relatively

11 Banking revenue consists of all interest income and non-interest income for banks. It is chosen as Thomson Reuters uses this as the only measure for banks’ revenue.

higher stock price. On the other hand, if earnings are already high and little growth is anticipated, the stock price will likely be relatively lower (i.e. less ‘potential’ has to be captured).

As suggested by for instance Diener, Tay and Oishi (2013), Tosi and Greckhamer (2003), the general economic development may affect the levels of CEO compensation and employee compensation and thus the CEO pay ratio. Hence, we use a macro-economic indicator, gross domestic product (GDP) per capita as a proxy to control for general economic development.

Next, we control for the size of the board (i.e. the number of directors) effect as reported in various related research (Shin et al., 2015). It is stated that smaller boards pose better control over the CEO and also over potential agency (rent-seeking) problems, which may increase CEO compensation (Core et al., 1999; Yermack, 1996). To control for this specific effect, we extracted the number of directors from BoardEx for the used reporting period.

We include gender, age and tenure for each company’s CEO. These are all acknowledged factors that may affect CEO compensation and thus the CEO pay ratio. Gender is included as a dummy that takes value 1 if it the CEO is a male and zero if female. Although, the literature has found evidence for a gender pay gap for most (executive) levels (e.g. a European data study by Arulampalam, Booth & Bryan, 2007), the results for the CEO are conflicting. Moreover, it is argued that the compensation of female increased relatively faster than their male equivalent until the end of 1900, but that this has slowed down over the past decade (Blau & Kahn, 2007). Even though, different views on the sign of the gender effect on pay exists, the consensus is that there is an effect and thus necessary to take into account as a control variable. Also, age and tenure are according to literature both positively related to CEO pay (Chalmers, Koh & Stapledon, 2006) and therefore included as control variable.

Lastly, in line with most financial related research and thus also CEO or employee compensation research, we include eleven sector dummies based on GICS codes to control for sector/ industry fixed effects (O’Reilly et al., 1998; Hartzell & Starks, 2003).

4.3 METHODOLOGY We develop multiple cross-sectional models to determine the CEO pay ratio on a country-, firm- and firm-country level by using ordinary least squares regressions (OLS). In the country-level regression, we examine how social, cultural and legal factors affect the CEO pay ratio. This regression is controlled for industry specific fixed effects. In the firm-level regression, we investigate the impact of the firm specific factors (e.g. innovation, sustainability), while being controlled for industry specific fixed effects. Eventually, we combine the firm-level and country specific factor, which is the final goal of this research. Moreover, we also add integrated variables

to create difference-in-difference estimates, but these are not part of our core regression. Again, we also control for industry fixed effects by sector dummy variables. Our final estimated model reads then as follows:

퐶퐸푂 푝푎푦 푟푎푡푖표 = 훼 + 훽푖푋푖 + 퐼푛푑푢푠푡푟푦푖 + 휀푖

Where the CEO pay ratio is the total CEO compensation divided by the average employee compensation, x is the specific variable at either a country or firm level, i is the company, Industry the dummy for a specific industry and 휀 the error term

Working with an OLS regression imposes several key assumptions for our model. First, we assume a population model that is linear in parameters, that our sample is random and has sample variation (i.e. no perfect collinearity), the error term has an expected value of zero and a constant/ finite variance (i.e. homoscedasticity). Generally, a concern with assumptions is that it might give rise to certain statistical errors, in particular: multi-collinearity and heteroscedasticity. We address these problems here by first applying independent tests followed by commonly accepted solutions.

Multicollinearity occurs when independent variables within one model are strongly correlated and it is possible to accurately predict the independent variable by linear regression on the other independent variable. If it is observed in a dataset it causes estimates to be biased (i.e. sign and/ or magnitude). We test for this problem with the Variance Inflation Factor as proposed by Mansfield & Helms (1982). We then use the commonly used cutoff at ten to determine whether variables are multicollinear (above ten) or not (below ten). If multicollinearity exists, one solution is to remove the correlating variables. Given the large number of variables in our regressions, this is a logical step.

If the assumption of homoscedasticity fails to hold or heteroscedasticity is observed, the standard errors are biased which in turn may cause type I errors (i.e. falsely rejecting null hypotheses). To analyze heteroscedasticity, we use the Breusch-Pagan test which tests whether the error variance of the regression is dependent on the independent variables (Breusch & Pagan, 1979). In the case of heteroscedasticity, we use Hubert-White standard errors (“robust standard errors”) to test our hypotheses, because even in disturbed regression models these errors remain valid (White, 1980).

4.3.1 Robustness tests Next, to the tests and solutions for the potential existence of the preceding statistical problems, we also construct several additional robustness tests that should verify the obtained results.

As the CEO pay ratio consists of two elements (CEO compensation and average employee compensation), movements of the ratio can be explained by both elements (e.g. an increase in CEO compensation may cause an increased pay ratio but if average employee compensation rises relatively more, the CEO pay ratio decreases). Therefore, we also perform OLS regressions separately for CEO compensation and average employee compensation. Then, the expected effect of the division of the estimates (i.e. CEO estimate divided by the average employee estimate) should show a similar sign as is the case for CEO pay ratio regressions. For instance, if CEO compensation is positively related to power distance and average employee compensation negatively related to power distance, it can be concluded that the CEO pay ratio should be negatively related to power distance.

Furthermore, we perform a sensitivity analysis in which we alter alternatively data points by a 1% increase or decrease. This is done for the CEO compensation as well as the average employee compensation. If our results are robust, the small alterations should not pose a large influence on the obtained estimates. Moreover, we test our model by the inclusion of different control variables for size, performance and risk and report whether this significantly impacts our results.

The last concern when estimating the causal effect of several variables on the CEO pay ratio is the potential existence of endogeneity within certain variables. The problem occurs when an independent variable at the same time determines one of the other independent variables and the dependent variable (i.e. the CEO pay ratio). In this research we outline endogenous variables by logical reasoning. To control for endogeneity and identify the unbiased causal effect of the independent variables on the CEO pay ratio, we establish an instrumental variable (IV) for each potential endogenous variable and perform two stage least square regressions (2SLS). Key for valid instruments is that they are relevant and exogenous, whereas the latter is not rigorously testable (Verbeek, 2008).

5 RESULTS

In this chapter, we discuss both the univariate as well as multivariate results from our research. Section 5.1 starts by discussing the descriptive statistics of our variables, with a particular focus on the compensation landscape for employees and CEOs. Moreover, this section includes correlations and tests of differences to get a better initial grasp of the dataset and the dynamics of the CEO pay ratio. The majority of the tables and figures corresponding to this section is reported in Appendix C. In Section 5.2, we present the results of the regressions as discussed in Chapter 4. We conclude with Section 5.3, which contains the results of our robustness tests. For tables and figures related to these latter two sections we refer to Appendix D and E respectively.

5.1 DESCRIPTIVE STATISTICS Table C1, Panel A and B, display the final sample distribution over countries and industries respectively. It shows that a total of 17 potential company observations is excluded from the dataset, which were either multi-listed or had no remuneration disclosure as discussed in Chapter 4. The largest part of the sample is represented by the United Kingdom (FTSE 100) which accounts for 30.03% of the sample. Followed by Italy and France with 12.07% and 11.15% of the sample respectively. The smallest part is represented by Belgian companies with 5.57%. Industry-wise most observations occur in Financials (21.67%), followed by Industrials (15.48%) and Consumer Discretionary (14.24%). The relatively large part of Financials in this sample is one of the reasons to perform an additional regression with at the one hand a heavy-regulated sub-sample (Financials and Utilities) and on the other hand an all other industry (excluding Financials and Utilities) sample.

Summary statistics on all variables (including the control variables) are reported in Table C2. In order to diminish the effect of outliers, we Winsor sized all continuous variables at the 1st and 99th percentiles. From the country variables it follows that the legal context companies act in differs, which is observed by the dispersion in P25, Median and P75 for all separate measures. Culturally, the countries appear to have less power distance (i.e. accept hierarchies less easily), with a bandwidth that is goes from 35 to 57. Moreover, an individualistic society is typically observed with respect to our sample countries, as the observed bandwidth is entirely above 50. Uncertainty avoidance shows the widest spread of all cultural variables, whereas in Sweden (29) and the United Kingdom (35) this avoidance is relatively low, on the other end Belgium (94) shows a high avoidance for uncertain situations. Further, it appears that long-term orientation, masculinity and indulgence follow a somewhat similar distribution over the P25-P75 bandwidth. Social variables show that the GINI coefficient is only marginally different between the countries, yet from a globally perspective these countries differ significantly. On the other hand, the unionization rate

shows a more dispersed pattern. For instance, France has a low rate (7.9%)12, whereas Sweden has a relatively high rate with 66.8%. The average firm in our sample has sales of € 22 billion, 60,000 employees and is heavy leveraged (4.6), all these variables show a positive distribution. This is a logical consequence of sampling the main-indices, as on average the firms in these will be larger (e.g. more employees, higher sales). Firms have an average P/E ratio (growth) of 18.682 and relative intangibles are around 10% of total assets on average. Average accounting performance (ROA) is 5.2%, whereas market performance (TSR) is 14.23% 13. The companies have an average beta of 0.979, where approximately half (53.3%) of the companies operates in the business-to-consumer market (B2C). On average the observed workforce scores are higher than the corporate social responsibility (CSR) score, which indicates that other factors in the CSR score are graded lower. The control variables on the CEO and the board show us that the average CEO is 57 years old, a male (95.4%) and has a tenure of 7.2 years. The average board size is approximately 13 (which includes both executive and non-executive directors).

5.1.1 Compensation landscape In Table C3 we report the compensation landscape descriptive statistics in tabular format. This includes the CEO compensation; average employee compensation and the CEO pay ratio on a country level (Panel A) and firm level (Panel B). For the CEO compensation we made a split in the four basic components: base salary, short-term incentives, long-term incentives and other compensation (e.g. pension). For employees, the average total compensation is reported, which include salary, pension, social security costs, bonuses, etc. and no split is made based on the type, because companies, do not disclose this.

CEO compensation

CEO compensation can roughly be divided into two broad themes. First, the level of compensation which talks about the total numerical value of the package. Second, the structure of pay, which deals with the division between the different components of CEO compensation. Here, we also look at both components, although for the calculation of the basic CEO pay ratio only the levels are important. However, if one looks at different types of CEO pay ratios (discussed in Section 5.2) also the structure comes into the equation.

12 Although France is often assumed to have a large unionization (e.g. based on news-reports), the actual rate is rather low. However, their strength is still significant, because French law requires employee representation within companies’ boards, which is often performed by unions. A further practical explanation follows from: https://www.economist.com/the-economist- explains/2014/03/17/why-french-trade-unions-are-so-strong 13 This is the result of taking the natural logarithm of returns and includes all dividends received in the year, which is one of the commonly accepted methods of calculating stock returns (Fama & French, 1988).

Figure 5.1 These graphs displays the total CEO compensation box-Whisker plots within the bandwidth of 1st percentile to 99th percentile for both country and industry levels respectively. The box displays P25 to P75 and the median observation. Reported amounts are in thousands of €.

From Figure 5.1, we highlight some observations. The highest paid countries are Switzerland, the United Kingdom and Germany, because of respectively their stretch bandwidth (UK and Switzerland) and high median (Germany). Especially, the bandwidth is relevant for the United Kingdom given the larger sample size and thus the inclusion of small companies (with typically lower pay). The lowest paying countries are Belgium and Sweden (both median and stretch payout). However, for Belgium it is important to note that a large number of companies (20%) is financial and thus relatively lower paid (as also follows from the right-hand figure). The stretch of the pay level is the highest in France, Switzerland and the United Kingdom and the lowest for Belgium, Germany and Sweden, which likely indicates (dis)similarities between firms. From the industry figure, it follows that Financials and Utilities are the lowest paid based on the median, with Information Technology only slightly above that level. For the first two sectors this can partially be explained by the regulation that is often in place (e.g. caps on variable pay). High paying industries are Consumer Discretionary and the Consumer Staples, which have the highest stretch pay-out. However, the Health Care industry does report a higher median level and comes also close in terms of the stretch/ bandwidth.

From the above, it follows that CEO compensation (in terms of level) differs considerably between countries and industries. Yet, we do observe some general trends/ patterns for the different scopes in terms of the structure of pay (see Figure 5.2 and 5.3 for a graphical depiction). First, the largest part of total compensation is accounted for by the long-term incentives (apart from Sweden). Second, the smallest part of total compensation is delivered by the other compensation. Although, in countries such as Germany and Spain, and in the utilities sector the relative importance increases, it still remains the smallest portion (Sweden is again an exception, as it is 1% higher than the lowest portion of total compensation). Third, base salary roughly accounts for

20-25% of total compensation across industries and countries, with outliers in Sweden (46%), Italy (36%) and financials (31%), where it should be noted that in Italy a large part of the index is occupied by financials.

Figure 5.2 This graph shows the components of total CEO compensation per country. These figures display the average observed package within the countries in our sample.

100% 3% 5% 9% 7% 7% 13% 11% 18% 18%

80% 38% 17% 38% 49% 42% 35% 39% 29% 55% 60% 19%

21% 40% 26% 25% 25% 25% 30% 25% 20% 46% 20% 36% 27% 29% 23% 22% 24% 25% 18%

0% Belgium France Germany Italy Netherlands Spain Sweden Switzerland United Kingdom

Base STI LTI Other

Figure 5.3 This graph shows the components of total CEO compensation per industry. These figures display the average observed package within the industries in our sample.

100% 6% 6% 4% 7% 7% 12% 10% 10% 9% 7% 14%

80% 44% 44% 49% 38% 53% 41% 49% 39% 44% 46% 38% 60%

40% 16% 26% 28% 22% 23% 28% 24% 20% 24% 24% 25% 20% 26% 31% 25% 26% 25% 26% 23% 18% 22% 19% 22% 0%

Base STI LTI Other

Next to the general trends, we highlight some additional insights that follow from these figures. In France and the United Kingdom, the total percentage of variable compensation is the highest with

74% and 75% respectively. Compared to Sweden which has the lowest part linked to variable pay, namely 36%. The relatively large percentages for France and the UK stem from the large portion of long-term incentives (LTI), whereas in Sweden both base and other take up a larger portion. On the industry-level, we observe that Financials have the lowest percentage of variable pay (57%), which seems to be accounted for by the relative lower short-term incentives (STI) which in turn is subject to European ruling or capped by law in specific countries.14 Within the Consumer Staples (77%), variable pay is for the largest part responsible for total pay.

Overall, if we try to make a connection between the levels and the structure of CEO compensation, we may observe one trend: for both the industries and countries with relatively high (low) variable pay, we see relatively higher (lower) total compensation levels. For instance, the United Kingdom has high levels and a high percentage of variable pay and Financials have a lower variable pay percentage and report lower total compensation levels.

Average employee compensation

The second component of the CEO pay ratio is the average employee compensation. In our sample this is calculated as all attributable compensation to employees (minus the CEO’s compensation) divided by the total number of employees minus the CEO. All attributable benefits are generally reported in annual reports as employee/ personnel costs, wages & salaries or workforce benefits. Given the fact that most companies only disclose a single total number (or only split between

Figure 5.4 These graphs depict the average employee compensation box-Whisker plots within the bandwidth of 1st percentile to 99th percentile for both country and industry levels respectively. The box displays P25 to P75 and the median observation.

14 For instance Belgium has a 50% variable pay cap for financials, whereas the Netherlands have an even smaller cap set of 20%. European guidelines prescribe a variable/ bonus pay cap of 100% of fixed income (for more information, we refer to https://www.eba.europa.eu/regulation-and-policy/remuneration).

pension/ social security costs and salaries/ wages) we do not have a ‘structure’ analysis as for the CEO‘s compensation 15. With respect to the levels, our observations are displayed in Figure 5.4.

First, compared to CEO compensation levels, we observe a less widely spread of plots on the country level. It seems that average employee compensation levels are more similar on a country level, as on the industry level a bit more variation is observed. Second, compared to the CEO compensation, the industries that had the least CEO compensation, pay the highest for their average employee (i.e. Financials, Health Care and Real Estate, for Utilities this does not apply). The other way around, the same follows, as Consumer Discretionary and Consumer Staples have the lowest median employee compensation and even their P75 is below the medians of the majority of the other industries. Third, at the country-level Belgium and Switzerland have the highest median and stretch pay, which is for Belgium likely because of the relative importance of the high-paid financials and for Switzerland based on their purchasing power (higher price index compared to the other European countries) Fourth, the bandwidth for France, Germany and Spain is relatively the same and lies around the € 30,000.

Overall, it may be concluded that we observe opposite trends compared to the CEO compensation. Hence, this may indicate movements in the CEO pay ratio. For instance, if the average employee compensation (denominator) is high in the Financials and the CEO’s earnings (nominator) relatively low compared to other industries, the result is logically a lower pay ratio.

The CEO pay ratio

Figure 5.5 These graphs depict the CEO pay ratios box-Whisker plots within the bandwidth of 1st percentile to 99th percentile for both country and industry levels respectively. The box displays P25 to P75 and the median observation.

Belgium Communication Services Consumer Discretionary France Consumer Staples Germany Energy

Italy Financials

Netherlands Health Care

Spain Industrials Information Technology Sweden Materials Switzerland Real Estate

United-Kingdom Utilities

0 50 100 150 200 250 0 100 200 300 400 CEO pay ratio CEO pay ratio

15 Yet, for the average employee it may be noticed that he or she is likely not eligible for long-term incentives. Also, short-term incentives are not typically observed at lower levels (the average employee) in the organization. Other benefits and especially pension, social security and perks are commonly observed at all levels though.

With the conclusion drawn above in mind, we here look at the summary statistics for the CEO pay ratio, which simply translates as the amount of times a CEO earns the average employees’ compensation and is calculated here as the total CEO compensation divided by the average employee compensation. As before, the results are displayed (Figure 5.5) in a box-Whisker plot.

From the graphs it does follow that roughly the distribution of the CEO pay ratio follows from the distributions of CEO compensation and average employee compensation. The lowest pay ratios are observed in Belgium. In Sweden and Italy, the pay ratios are slightly above Belgium but are still relatively low (for the P25-P75 bandwidth). These countries are followed by the Netherlands, Spain and Switzerland, who can be seen as the middle of the pack (position around the median overall, although slightly below). France, Germany, and the United Kingdom have relatively higher CEO pay ratios and have an observed bandwidth that is almost entirely above the median of the other countries. What is also noteworthy, is that it appears that the bandwidth for these latter countries is almost completely higher than for the countries with ‘average’ or low CEO pay ratios. When focusing on the different industries, we observe high pay ratios for the Consumer Discretionary and Consumer Staples industries (the entire bandwidth is above the majority of the other medians), which is in line with the prediction that follows from the CEO compensation (higher) and average employee compensation (lower). Low pay ratios are observed for the ‘expected’ industries, namely Real Estate, Financials and Utilities. However, also the Energy, Industrials and Materials have bandwidths that are similar to preceding three, although the median is higher.

The natural conclusion is that the CEO pay ratio seems to show a significant connection between the patterns observed in the total compensation and the average employee compensation. Hence, a next step is to dive deeper into the underlying drivers of the CEO pay ratio.

5.1.2 Mean-differences and correlations To get an initial view of whether variables show expected variation in line with our hypotheses we performed a mean-difference T-test in which we split the entire sample in two groups: below median and above median CEO pay ratio (i.e. below or under 63.707). The results of this analysis are reported in Table C4, which shows the mean statistics and the test of difference.

On the country level the legal framework differs as governance mechanisms (AntiDirectorIndex and WGIScore) are significantly higher for high CEO pay ratios. For the cultural variables: Individualism and Masculinity are significantly higher, whereas PowerDistance and UncertaintyAvoidance are significantly lower. However, Indulgence has no significant difference between for low and high CEO pay ratios, which is in somewhat in line with Hypothesis 2F, as this provides preliminary evidence that no sign is directly observable. GINI is significantly higher when

pay ratios are higher, which is in line with Hypothesis 2G, as it predicts that more unequal societies have a higher CEO pay ratio. On the other hand, Unemployment and Poverty show an opposite pattern to what is expected by Hypothesis 2G. The last country-level variable Unionization is significantly lower for high CEO pay ratios.

With regards to the firm-level variables, we observe significantly lower Ownership for higher CEO pay ratios, providing preliminary evidence in favor of Hypothesis 3D, which argues that these blockholders protect against rent-seeking behavior by CEO’s. Next, Innovation is significantly higher for companies with a high CEO pay ratio. B2C is significantly higher for high CEO pay ratios, which is a discrepancy with Hypothesis 3B. Lastly, both WorkforceScore as well as CSRScore (our proxies for sustainability) are significantly higher for firms with high CEO pay ratios.

From the tests on control variables it follows that ln(Sales) and Employees are significantly higher for high CEO pay ratios, this is in line with the expectation that larger companies have either more blue-collar employees or have more coordination needs (i.e. relatively higher CEO compensation). Further, Leverage is significantly lower for high pay ratios. Surprisingly, measures of risk (SD and Beta), return (TSR and ROA) and Growth are not significantly different for low versus high CEO pay ratio companies. Board and CEO level control variables show that both Boardsize and Tenure are relatively higher for companies with a high pay ratio, the test for Tenure is in line with literature and the Boardsize test is in favor of theories predicting that larger boards have difficulty in decision-making, which triggers an ‘uncontrolled’ CEO into rent-seeking behavior (Harris & Raviv, 2006).

Next, we also analyze whether variables are correlated with the CEO pay ratio and/ or if they show unexpected correlations with other independent variables. The correlation matrix for the main variables related to our hypotheses (i.e. excluding the control variables) is reported in Table C5.

We find correlations that are in line with our hypotheses, but also correlations that predict a different sign for the specific relation, but obviously these prove no valid causal relation yet. We highlight some of the outcomes. First, we observe a positive correlation between shareholder protection measures (AntiDirectorIndex, ShareholderRightsIndex and WGIscore), which is not in line with our reasoning (Hypotheses 1A and 1B), a potential way of understanding this is that better investor/ shareholder protection increases motivation for CEOs and their performance, which results in larger equity pay (keeping average employee pay constant). The results for cultural variables show a similar pattern as was reported for the mean-difference test in Table C4. The social variables show that GINI is positively correlated with CEO pay ratio, providing evidence in favor of Hypothesis 2G. We observe a strong negative correlation between Unionization and the CEO pay ratio, which provides backing to Hypothesis 2H and the argument that higher

unionization provides larger bargaining power and thus a lower CEO pay ratio (i.e. average employee compensation goes up). The negative correlation (although not strong) between Ownership and CEO pay ratio provide additional evidence in favor of our argument that larger blockholders would lower CEO compensation (and thus decrease CEO pay ratio). Both CSRScore and WorkforceScore are positively correlated with the ratio, supporting the ‘rewarding effect’ hypothesis for CEOs if sustainability scores are higher. Yet, B2C is positively related to CEO pay ratio, which is a discrepancy with our hypothesis. Interestingly, both sustainability proxies (CSRScore and WorkforceScore) are positively correlated with GINI. A possible explanation for this might be that in order to address income inequality at the country level (which can be observed as unsustainable), firms try to mitigate this by taking actions that improve their own contribution to society.

Overall, the correlations in Table C5 show us that our measures for either country- or firm-level effects are not highly correlated with the CEO pay ratio. This suggests that all capture some different element of the CEO pay ratio. The highest correlations occur for Unionization, GINI, B2C and Individualism. This supports our claim that the CEO pay ratio is not solely defined at the firm or at the country level, but likely on both at the same time, which is in favor of also running integrated regressions (e.g. country variables * firm-specific variables). Another conclusion from the correlations matrix is that cultural, social and legal variables are sometimes highly correlated (e.g. CorporateRate with all cultural variables). This might give suspicion to multicollinearity and requires close attention in the subsequent regressions. Moreover, it indicates that some variables (likely) must be dropped in order to obtain statistical valid results. In the next section, this is discussed further for the relevant regressions.

5.2 REGRESSION RESULTS In this section, we present and analyze the results to the several forms of regression we performed. We start with our basic model that gradually includes the country and firm specific factors based on the hypotheses from Chapter 3. Next, we use integrated variables to test difference-in-difference/ integrated effects that bridge the country and firm specific influences. With the input from these sub-sections, we eventually perform regressions based on different measures of the dependent variable (e.g. natural logarithm) and with a sample split (the regulated firms Financials/ Utilities versus all other ‘less-regulated’ firms). All outcomes related to the regressions are reported in Appendix D.

5.2.1 Baseline results The start of our analysis is in observing the relationship between the basic CEO pay ratio and the variables from our main hypotheses. The build-up is as described before, first we look at the

country level, then the firm level and finally we combine these two. Yet, the real integration of country and firm variables is done in the following sub-section. We perform both fixed-effects (controlling for industry effects) and regular regressions. Further, we use Hubert-White (1980) robust standard errors that allow for clustering at the firm level and mitigate heteroscedasticity issues.

The tests on multicollinearity prove our suspicions (as mentioned in the previous section) to be right. Especially, on the country level we observe high VIF test values. In particular, the combination of legal and social variables provides potential issues. However, this is not a surprising result, since a large part of these variables are somewhat substitutes of one another. For instance, the ShareholderRightsIndex as developed by Martynova & Renneboog (2010) is essentially an extension of the LLSV AntiDirectorIndex (1998), which makes these variables linearly dependent on each other. Moreover, social and legal variables often show similar patterns and are related (i.e. the legal framework shapes the way the society will develop (Schur, 1968)). Lastly, cultural variables are also related to the social setting (and thus likely also with the legal setting). When testing the VIF values for the cultural variables, we found significantly high values for LongTermOrientation and UncertaintyAvoidance. In order to construct statistical reliable results, we therefore drop several variables in our main regressions. However, in light of providing evidence for our hypotheses, we do report the results with inclusion of the two mentioned cultural and the governance variable (WGIscore) for a selection of regressions.

Table D1 presents the results of the regressions on a country level only. Specification (1) to (3) show the coefficients for all legal, cultural and social variables respectively. In specification (4) and (5) we include (next to the ‘non-issue’ variables) those variables that are reporting high VIF values but are relevant for our hypotheses. Lastly, specification (6) and (7) report the country variables that are included in our regressions after selection to prevent multi-collinearity. These regressions are performed including industry fixed-effects, except for specification (6). Yet, the results of this regression do show us that on the country level much explanation stems from industry fixed effects (argued based on a substantially higher adjusted R2). On the country level, the ShareholderRightsIndex and Unionization show a negative relation, whereas Individualism and GINI have a positive coefficient.

In Table D2, the basic regression is further developed, first only the firm specific variables are included. Next, we include the control variables on the firm level (e.g. Employees) and individual- level (e.g. Gender) as seen under specifications (3) and (4). Finally, we re-include the country specific variables both without and with all control variables as depicted in specification (5) and (6). Noteworthy, for the firm level variables we did not find any evidence from VIF values that give rise to suspicion for the existence of multicollinearity.

From the regressions, it follows that when solely looked at firm level variables we observe a statistically significant effect for both Innovation and B2C. Yet, when control variables are added and in particular industry specific effects are included, this significance decreases for Innovation and vanishes for B2C. With the inclusion of country variables, the firm-specific factors do not show any statistically significant effects.

Furthermore, in specification (7) which is the all-inclusive model. We find supporting evidence for Hypothesis 2G, which proposes that the CEO pay ratio is positively related to (income) inequality, which is here proxied by the GINI coefficient. The coefficient tells us that when the GINI increase with one standard deviation, the CEO pay ratio increase with 14.511. This is also an economically significant result, considering the observed magnitude of the relation. For the other hypotheses we do not find statistically significant results in our all-inclusive regression. However, when we look at the model without firm-specific controls (e.g. specification (6)) we do find a statistically significant negative relation between ShareholderRightsIndex and the CEO pay ratio (in line with Hypothesis 1A), which indicates that the CEO pay ratio decrease with 5.909 point if the ShareholderRightsIndex increase with one point (i.e. stronger shareholder protection decrease CEO pay ratio). Also, the measure of Individualism is in this setting statistically significant and shows the expected positive relation that more individualistic societies will have a higher degree of rent-seeking behavior by CEOs and thus higher CEO pay ratios (in line with Crossland & Hambrick, 2007). For the majority of our control variables, we find no discrepancies with our expected signs or these prove to be statistically and/ or economically insignificant.

Overall, we observe from the adjusted R-squares that our model has an increasing power to explain the variation in the data, when variables and/ or controls are added. Yet, the inclusion of more variables does reduce the statistical significance of most variables and leaves room to only a few significant explanatory variables.

5.2.2 Difference-in-difference: integrating firms and countries Table D3 exhibits the results of the regressions in which we looked at the integration of variables. Specification (1) and (2) deal with integration on the country level. The third specification on firm level integration. Finally, specification (4) and (5) provides the results of merging country and firm level variables.

As mentioned before, culture and social variables are related from within their definitions (e.g. Alexander & Reed, 2009). Hence, we expect that to some extent certain cultural characteristics may foster income inequality. In this light, we create interaction variables between the GINI coefficient and the measures of culture that are not multicollinearity vulnerable, which is in this case only the PowerDistance*GINI. Next, we also look at the integration of good corporate

governance (measured by WGIscore) and the ShareholderRightsIndex. This essentially captures, whether the acting of governments (e.g. informal governance) matters for the effects of formal governance (here, the shareholder protection rights). This relationship is often argued to be positive (van Essen et al., 2012).

The result from the social and cultural interaction show us that both the PowerDistance as well as the integrated variable are statistically significant. The interpretation of the coefficients reads as follows: when a culture is more power distanced (i.e. accept more power distance) the positive effect that GINI has on the CEO pay ratio is reduced. The integration of formal and informal governance (WGIscore*ShareholderRightsIndex) produces also a statistically significant result, which is not in line with our initial expectation. If the WGIscore is higher (i.e. better government effectiveness or informal governance), this reduces the negative impact of ShareholderRightsIndex. Hence, this might imply that government effectiveness may reduce CEO pay ratios via different dimensions.

Next, we obtain results on firm level variable interaction. Specifically, we test whether a company that acts more sustainable (CSRScore) is influenced by the market it serves (B2C). The regression result indicates that a higher CSRScore reduces the impact of B2C on the CEO pay ratio. This seems logical, as one could argue that more sustainable firms already have a better “brand image”, which then makes adjustments to the CEO pay ratio based on market-attention (i.e. being in the consumers market) less necessary. However, the effect of the interaction variable is not statistically significant, both with and without industry fixed-effects.

Lastly, we performed an analysis of the interaction of firm and country variables. In particular, we looked at the interplay between B2C and GINI. Argued by the logic that whenever CEOs are in the public eye and more aware of their reputation (proxied by B2C), this effect is increased when the society in general shows less inequality (e.g. lower GINI). Another interaction of interest is between the WorkforceScore and Unionization, where one would expect that higher employee satisfaction (proxied by the score) reduces the effect of unions (Schriesheim, 1978). B2CGINI has a positive coefficient (statistically insignificant), which indicates the opposite of our reasoning. From the social perspective it can be concluded that when a firm is B2C oriented, the effect of the GINI coefficient is higher. In line with our considerations, we find that higher WorkforceScore reduces the negative effect that Unionization has on the CEO pay ratio, however both values are statistically insignificant.

5.2.3 Alternative measures of the CEO pay ratio One potential way to improve the explanatory power of the regression results in terms of statistical significance is to use the natural logarithm of the CEO pay ratio, as it likely reduces the

impact outliers have (especially in the context of a positively skewed variable like this ratio). In the literature, we also document the use of the natural logarithm approach, next to the regular CEO pay ratio (see for instance Faleye et al., 2013). This approach is also reasonable when looked at the (beyond the shown 75th percentile) descriptive statistics on CEO compensation and average employee compensation. Whereas, the first shows large bandwidths, the latter has substantially smaller bandwidths. Thus, we likely observe an exponentially increasing CEO pay ratio as the denominator (the average employee salary) remains relatively the same, whereas the numerator (the CEO compensation) exponentially increases. This makes the regression on the ratio close to a regression with the CEO compensation as a dependent variable. Given the fact that a large part of the existing research (additionally) uses the natural logarithm in CEO compensation studies (e.g. Sapp, 2008), it can be argued that this validates a similar approach here.

Our results with dependent variable the ln(CEO pay ratio) are depicted in Table D4, specifications (1) and (2). It turns out that a few more variables prove to be statistically significant both without and with the inclusion of fixed effects and control variables. First, we find that the ShareholderRightsIndex is negatively related to the natural log of the CEO pay ratio, which is in line with hypothesis 1A. It also economically significant, because a one standard deviation upward move of the ShareholderRightsIndex leads to an approximate 31% decrease in the CEO pay ratio. Second, in line with hypothesis 2B, we find that the CEO pay ratio is positively related to the degree of individualism in a society. Third, also with the natural logarithm of CEO pay ratio as dependent variable, we conclude that the GINI coefficient is statistically and economically significant related to the CEO pay ratio (Hypothesis 2G). Fourth and last, we find that Unionization is negatively related to the natural log of CEO pay ratio (as proposed in Hypothesis 2H). It turns out that one standard deviation movement upwards (in terms of the unionization rate) causes a 12.31% decrease in the CEO pay ratio, which is observed to be economic significant. Yet, overall the results on the natural log regressions, prove once again that a large part of the variation is captured by the industry fixed effects and other firm specific control variables. Although, the significance of independent variables does increase in the logarithmic model, the total power of the regression model remains almost the same (e.g. the adjusted R-square does only increase with 1.6 percent point)

To further obtain evidence or support for hypotheses, we also test what the regression model would look like if we chose a different CEO pay ratio. Here, we test two additional measures of the CEO pay ratio, namely:

(푡표푡푎푙 퐶퐸푂 푐표푚푝푒푛푠푎푡푖표푛 − 퐿푇퐼) 퐶퐸푂 푝푎푦 푟푎푡푖표 (푛표 퐿푇퐼) = 푎푣푒푟푎푔푒 푒푚푝푙표푦푒푒 푐표푚푝푒푛푠푎푡푖표푛

(푡표푡푎푙 퐶퐸푂 푐표푚푝푒푛푠푎푡푖표푛 − 퐿푇퐼 − 푆푇퐼) 퐶퐸푂 푝푎푦 푟푎푡푖표 (푛표 푖푛푐푒푛푡푖푣푒푠) = 푎푣푒푟푎푔푒 푒푚푝푙표푦푒푒 푐표푚푝푒푛푠푎푡푖표푛

We deliberately choose these pay ratios, as both are close approximations of what is possibly observed in real-life practice, because incentives are generally not part of the compensation package of the average employee. Given the lack of specification of the distribution of employee compensation (e.g. no components division is known from company reporting), this is a reasonable assumption.

Our results are twofold. First, we find that the CEO pay ratio (no LTI) is not well explained by our model. Even the previously (always) significant GINI is statistically insignificant. Only, the control for the number of employees has a statistically significant effect (which is natural as it is incorporated in the function of the CEO pay ratio). This result might be because our model exhibits a significant dimension for equity pay (as many variables are partially validated in that way). Second, when also excluding the short-term incentives our model is more capable of estimating the coefficients of our variables. We find, statistically significant coefficient for the negative relation between both PowerDistance (at the 5% level) and Masculinity (at the 10% level) with the CEO pay ratio. The result for PowerDistance is somewhat surprising, as we hypothesized a different sign. A potential reason for this might be that especially variable compensation is what makes CEO pay significantly higher than lower levels employees pay. If this is then removed, the rent-seeking behavior by CEOs is less rewarding (fixed base pay does not increase/ decrease, and other pay is less substantial and stronger monitored) and might become negatively related to power distance (Garvey & Milbourn, 2006). In line with the other estimates, we also find that GINI is statistically significant related to CEO pay ratio. However, for Unionization, we find the surprising result of a positive coefficient (significant at the 5% level). A possible explanation could be that unions accept a higher base salary if no LTI is included, because variable pay is according to unions “too risky and aggressive”. From the control variables, it is interesting to observe that Beta has a positive significant effect on the CEO pay ratio in the last setting. Indicating that CEOs get rewarded in their base salary for risk relatively more than the average employee, which makes sense from the perspective of rewarding for accountability (which is naturally higher for CEOs).

5.2.4 Less regulated industries vs. heavily regulated industries As already mentioned before, the Financial sector and the often (partially) state-owned/ quasi- private Utilities sector are both subject to substantially different regulation on executive compensation than other companies in other industries (we here refer to as ‘less regulated industries’). Moreover, especially Financials have regularly different standards for reporting, which in our case may influence the accounting-based measures and their estimates. Even though we did control for the fixed-effects of industries in our previous regressions (and hence to some

extent for the effect the Financials/ Utilities sector had), it is still relevant to also look at our estimates for these different sub-samples. The results of these regressions are reported in Table D5, in which we also included the results if the dependent variable was the natural logarithm of the CEO pay ratio.

A general conclusion is that especially for the ‘ heavy regulated industries’ we find more statistically significant results compared to our previous regressions. Yet, the adjusted R-squares are considerably lower, which indicates that for the overall image our previous models perform (slightly) better. We highlight some observations from Table D5. First, Masculinity is statistically significant for financial firms only and shows a positive sign (for both type of dependent variables). This seems in line with the general opinion of the Financial sector, which is perceived to be more masculine (which explains the significance), moreover it provides contradicting evidence for Hypothesis 2E. Second, somewhat surprisingly, we find only statistically significant effects for GINI in the less regulated market and only insignificancy for the Financials/ Utilities. However, the observed sign is for all estimates positive and thus in line with expectations. Third, the Unionization has a significant negative result on the ln(CEO pay ratio) for the less regulated market, in line with the previous observed. On the other hand, the relation with normal CEO pay ratio is positive for the Financials/ Utilities. Yet, this result is in line with the relative low importance of variable pay in these sectors (see Section 5.1) and from the previous sub-section it follows that the relation between CEO pay ratio (without incentives) and Unionization is positive indeed. Fourth, in line with our hypothesis 3D, but not previously established, we find a statistically significant relationship between Ownership and the CEO pay ratio (both natural log as well as generic format) in the Financial/ Utilities industry. We observe estimates that imply that a movement of one standard deviation in Ownership would cause a 7.5-point decrease in the CEO pay ratio for Financials/ Utilities. Fifth, for the less regulated firms we find a statistically significant and negative coefficient for the B2C dummy variable on CEO pay ratio, this is in line with our hypothesis 3B. However, for the Financials/ Utilities, we find an opposite effect (statistically significant). This might be caused by the fact that relative to the less regulated market, especially the Financials’ executives compare themselves to other peers (as argued in the equity fairness theory, Chapter 2). When these are then also in the spotlight of media-attention and thus at risk of reputation loss, this relationship might get magnified (Otten, 2010) which might explain the positive sign for B2C.

5.3 ROBUSTNESS TESTS In this section, we discuss the results of the additional robustness tests. Although, we have already explored a considerable amount of variations on our basic model (e.g. sub-samples, different

dependent variable), which already give a strong sense of robustness, we here present some additional tests that could further validate our results.

First, we discuss the results of the separate regression of the CEO compensation and average employee compensation, with extracting the expected sign for the CEO pay ratio. Second, we see whether further inclusion of controls has an impact on the explanatory power of our base model. Third, we show our approach to endogeneity and the potential solutions.

The proposed sensitivity analysis is done on the nominal values that might get an inflation correction at some point. The obtained results are very similar with before, which shows robustness (or little sensitivity) of our estimates. Hence, we do not tabulate these results. All other outcomes from the robustness regressions are reported in Appendix E.

5.3.1 CEO compensation and employee compensation Table E1 reports the results of the separate regressions for CEO compensation and average employee compensation. We performed the analysis both with the actual values and the natural log values (as is generally done in the literature). If our model performs robust, we expect the signs of the variables to be predicted from the effect of both CEO and employee compensation, because the CEO pay ratio is based on their relative movements.

From a check on the obtained signs (column 3 and column 6), we can conclude that for most of the variables, our CEO pay ratio model is robust. For instance, the ShareholderRightsIndex is negatively related to CEO compensation (as proposed by theory) and positively to average employee compensation. Also, GINI has a positive effect on CEO compensation and a negative effect on employee compensation, which indicates that the joint effect (i.e. the CEO pay ratio) is positively related to the GINI, a conclusion we have drawn numerous times.

Further, we do not observe differences (except for Indulgence) between the expected sign if we use the natural log instead of the actual value or vice versa. This again, shows robustness of our model.

5.3.2 Further controls Table E2 shows the result of including additional control variables, which should capture a rather similar effect as the ones already included. First, we included the ln(Sales), which on itself has significant explanatory power (statistically significant at the 1% level), but it adds little value to our model in terms of adjusted R-square (as Employees already captures the same effect). Second, the inclusion of TSR does not drastically change or improve our model and the same follows for including SD. Third, the inclusion of GDP does improve the explanatory power of the model, implies changes to certain signs and is statistically significant. However, the VIF values for GDP

give a strong indication that multicollinearity is a potential issue (e.g. strongly related to cultural/ societal factors). Hence, we should not include GDP in our regression model to create more robustness, as an reversed/ weakening effect on the robustness is more likely.

5.3.3 Endogeneity The final robustness check is to address the problem of endogeneity, which we belief is present in our analysis. Specifically, Unionization and ROA (firm performance) are likely to be endogenous determined. First, Unionization is likely to be higher among blue-collar/ lower-educated employees than white-collar/ higher-educated workers. This because, the latter group has for instance higher mobility across firms and markets, greater skill-differentiation and greater identification with the management, all of which cause them to benefit less from union membership (Hirsch, 1980; Schnabel, 2002). However, the educational level is also likely to influence the CEO pay ratio as lower/ higher educated workers generally earn less/ more, the CEO pay ratio will be higher/ lower. Second, ROA may be endogenous, because of reverse causality. In particular, productivity may affect the firm’s performance and at the same time trigger an increase in CEO compensation greater than the increase in employee compensation (because of a lower portion of variable pay), which eventually increases the CEO pay ratio. This problem essentially boils down to the question, whether the CEO pay ratio is determined by performance or performance through the CEO pay ratio?

Although endogeneity is said to be notoriously hard to control for (especially in cross-sectional studies), we do perform 2SLS regressions with instrumental variables to (try to) mitigate the endogeneity problem. First, for Unionization we use inflation as instrumental variable. This is based on literature that states that inflation increases union membership as workers are concerned about higher prices or want to “catch-up” with an increased salary (Visser, 2002). Second, for ROA we use the natural logarithm of the total cash holdings of a firm as instrument. This is likely a relevant variable, because more cash implies larger working capital which does not add value to firm’s performance (i.e. correlation between independent variable performance and cash holdings but is not directly correlated with the dependent variable CEO pay ratio).

The results from the 2SLS regressions are presented in Table E3. Again, we included industry fixed effects and the standard errors are adjusted for (potential) heteroscedasticity by using Hubert- White (1980) robust standard errors. In specification (1), we explored the effects of including the inflation rate as instrument for Unionization. We find that Inflation is indeed a relevant instrument for Unionization. Moreover, the results of the regression show that our results remain largely the same as in previous regressions, indicating that our model is considerably robust. However, the predicted sign for Unionization does change, but this estimator is not statistically significant. This shift may occur because of the relative low variation observed in the inflation rates (for our

dataset) compared to the variation in unionization rates. In the second specification, the results for the instrumented ROA (by ln(Cash)) are displayed. Again, the chosen instrument is statistically relevant. The resulting estimates suggest that in general our model remains robust when controlled for potential endogeneity in ROA. Only one sign of the independent variables appears to change (PowerDistance) in terms of direction, whereas some others gain/ lose some explanatory power in terms of economic/ statistical significance.

Overall, these instrumental variable 2SLS regressions for robustness show that our model remains relatively consistent when controlled for endogeneity. However, it should be noted that although the instruments have shown to be relevant and the results to remain stable, it does not necessarily imply that our model is completely exogenous.

6 CONCLUSIONS

In this final chapter, we conclude our research on the drivers of the CEO pay ratio. In Section 6.1, we discuss the results and draw the five main conclusions. Then, in Section 6.2, we outline the main limitations of this research and from there we move to Section 6.3, where we provide potential directions for future research.

6.1 DISCUSSION The amended Dutch Corporate Governance Code (2016) stipulates that companies should include the CEO pay ratio in their annual (remuneration) report over the year 2017 published in 2018. How this should be calculated and explained is up to the companies. Based on the first year of publications, it turns out that the majority of the AEX companies uses the total CEO compensation divided by the average employee compensation as their CEO pay ratio.

Based on the annual reports it becomes clear that indeed this pay ratio is disclosed but there is only a very limited amount of added explanation. What does the ratio actually tell us? What are the drivers of the CEO pay ratio? This research has the objective to contribute to the public and academic debate by starting with the later question and trying to find (initial) answers through literature research and empirical research based on a (predominantly) hand-collected dataset for the Netherlands within the broader European context (i.e. Belgium, France, Germany, Italy, Spain, Sweden, Switzerland and the United Kingdom). The focus of the research is on the constituents of the main index in these countries.

The CEO pay ratio itself, basically, has two components: CEO compensation and average employee compensation.

CEO compensation

The average CEO in our dataset is 57-year-old, has a tenure of 7.2 years and is likely to be male (95.4%). He earns the most in Germany, Switzerland and the United Kingdom (median pay around 5 to 7.5 million euro) and the least in Belgium and Sweden (median pay slightly above 2 million euro). In sectors like Consumer Discretionary and Staples he is likely to earn more than in heavily regulated markets like the Financials and Utilities. In general, it can be concluded that CEO pay differs strongly between countries industries and companies.

Employee compensation

The average employee compensation has a smoother and less fluctuating distribution. However, there are some interesting dynamics if compared to CEO compensation. At the country level, for example, we observe relatively higher CEO compensation in the United Kingdom but relatively

lower employee compensation. The opposite is true for Belgium: i.e. relatively lower CEO compensation but higher employee compensation. These dynamics can also be observed at the industry level. The Financials, for example, have relatively lower CEO compensation but relatively higher employee compensation.

These dynamics within the building blocks of the CEO pay ratio are notable and contribute to explaining variations. To obtain more detailed insights into what really drives the CEO pay ratio, regression analyses have been executed and the results were presented in Chapter 5. It can be concluded that country-level, industry-level as well as firm-level factors may contribute to the understanding of the underlying drivers of the CEO pay ratio. Below, we highlight five overarching themes.

1. Ownership: do blockholders influence the CEO pay ratio?

2. Firm characteristics and the CEO pay ratio

3. Unions and the CEO compensation mix

4. Industry effects: impact of regulation on the CEO pay ratio?

5. Country effects: governance standards, culture and the GINI coefficient

1. Ownership: do blockholders influence the CEO pay ratio?

Control over the CEO and his actions is likely stricter when there is one or there are several large shareholders (blockholders). Given the larger interest there will be a greater incentive to put effort into monitoring. Economic theory then dictates that this would negatively influence CEO compensation as CEOs will perform less rent-seeking behavior (van Essen et al., 2012) and in turn the CEO pay ratio would decrease (given the average employee compensation is not affected). We find particularly significant evidence for this in the setting of regulated firms (Financials and Utilities).

2. Firm characteristics and the CEO pay ratio

We tested whether certain firm characteristics have an influence on the CEO pay ratio. We looked at innovation, market served translated as business-to-business (B2B) or business-to-consumer (B2C) and sustainability.

- Innovation: we do find a correlation in which the CEO pay ratio is higher for more innovative firms. When controlled for industries, this effect is still there but less significant. This makes sense, as innovation is somewhat inherent to the respective industries. The captured positive

effect on the CEO pay ratio is understandable as more innovation requires CEOs to show higher capabilities. - Market served: we argue that companies that serve the B2C market have lower CEO pay ratios, based on the assumed effectiveness of the ‘outrage costs’ hypothesis (i.e. the fear for public outrage over CEO compensation results in a lower CEO pay ratio). Instead, we find for various settings that this relation is positive. One potential way to look at this, from the perspective of risk and scarcity, is the fact that CEOs who are more “in the picture”, which is inherent to the B2C market, desire a higher reward for the potential of reputation damage (demanded risk premium/ there will be less CEO candidates at a given compensation level) Also, it may well be that these CEOs compare themselves to other CEOs more frequently (as they are more frequently covered in the media), which in turn may lead to renegotiations of their remuneration. - Sustainability: it seems that sustainability is not a driver of the CEO pay ratio in our tested settings.

3. Unions and the CEO compensation mix

Public outcry over executive remuneration has been substantial over the years and counts numerous examples (e.g. Unilever’s CEO Paul Polman: whose bonus increase received much negative attention or Ralph Hamers, CEO of ING Group: where the proposition to increase his base salary eventually got withdrawn after negative public attention). Often, unions are mentioned in such reporting as strongly opposed to the (increase) of the already “excessive” remuneration. Moreover, unions strive for a better (higher) package for the average employee. Together, these two effects should jointly influence the CEO pay ratio in a negative way. Indeed, we find evidence for this relation. However, when we measure the CEO pay ratio without incentives (i.e. fixed compensation) we observe a positive relation. This may sound surprising but could actually be understandable. In practice, unions tend to have a stronger opposition towards variable payment for CEOs, as these are often perceived as aggressive and resulting in payouts that challenge legitimacy. Therefore, the influence of unions seems to result in a lower overall pay ratio and a higher fixed compensation pay ratio at the detriment of variable pay (i.e. they seem to favor a relatively small increase in fixed compensation at the expense of a relatively large decrease in variable compensation).

4. Industry effects: impact of regulation on the CEO pay ratio?

In one of our analyses, we have made a separation between heavily regulated firms (Financials and Utilities) and less regulated firms. Within Financials, for example, bonuses in Europe are capped at a maximum of 100% of fixed salary. The effects of these regulations are directly visible

in the observed pay ratios, which are significantly lower. A possible explanation that is not tested in our model might be that after the financial crisis and the introduction of strict regulation, this mostly impacted the top earners (including the CEO) as they had packages that were significantly above the cap percentage. Average employees, but also higher ranked managers who might have been already below the 100% cap were not or less impacted by these regulations. This would result in a lower CEO pay ratio and indeed we observe considerably lower numbers. This indication result and also the observed strong findings based on industry fixed effects are signs that in the drivers of the CEO pay ratio the type of industry is of relevance.

5. Country effects: governance standards, culture and the GINI coefficient

Generally, everything should be considered in its particular environment (Gillan, 2006), hence we also looked at how country characteristics influence the CEO pay ratio. We observe three strong effects.

- First, as argued by Renneboog (2005): governance matters. We find evidence that the CEO pay ratio is negatively impacted by better shareholder rights protection, which is likely as this gives more instruments to shareholders to control their CEOs and reduce potential agency problems. - Second, cultural factors do influence the CEO pay ratio in different ways. For instance, a more individualistic society has higher CEO pay ratios, which is understandable from the respective behavior that follows (e.g. tournament structures are encouraged with bigger pay gaps). - Third, income inequality (here proxied by the GINI coefficient) is positively related to the CEO pay ratio, in line with the expectation that more inequal societies are also more inequal within firms as essentially measured by the CEO pay ratio.

6.2 LIMITATIONS Our research is limited in the total number of observations. This is because the dataset needed to be hand-collected for the largest part. Particularly outside the United Kingdom data availability and quality from public databases turned out to be poor in terms of CEO compensation data. This reduces the degrees of freedom in our model, which in turn (likely) reduces the statistical explanatory power of the model. Moreover, because we only collected data for one-year, potential time-effects are not accounted for, whereas these probably exist (Gabaix & Landier, 2008).

The ESG score as a proxy for sustainability is limiting our research. Although, the Thomson Reuters database provides us with up-to-date scores for all firms, the measure is inherently subjective as human-beings (analysts) determine it. This makes that ESG scores are not accepted

as the worldwide reporting standard (Cheng, Ioannou & Serafeim, 2014). Hence, using ‘ESG scores’ from other data-providers may induce different research outcomes.

Furthermore, our measurement of the unionization rate is relatively inaccurate. Since, we used the publicly accessible database of OECD, only data on the country level was obtained. The actual sign is expected to be correct as the aggregated unionization rate is determined based on all industries and these show similar patterns in different countries, so differences are likely caused by country characteristics (Schnabel, 2002).Yet, unionization may differ strongly between different industries and even between firms (Schnabel & Wagner, 2005). This causes our estimate to be potentially biased in terms of magnitude.

Lastly, because we researched only the main indices of the nine developed countries in our sample dataset, it might become harder to generalize the results to other countries/ companies. For instance, the companies in the main index are often subject to more debate/ investor & analyst attention and thus require additional management capabilities (e.g. financing/ investor relations). Moreover, these companies are often more internationally active (and sometimes have multiple listings) than locally listed companies. Again, this increases the desired management capabilities. These increases in required capabilities probably increases executive (e.g. CEO) compensation and thus the CEO pay ratio.

6.3 FUTURE RESEARCH The findings in this study are a first step into understanding the dynamics of the CEO pay ratio within the European context and provide knowledge to practitioners that can be important in competitive benchmarking, justification and determination of CEO compensation, employee compensation and firms’ internal pay differentials. Academics no doubt will continue to work on further unraveling the puzzle of the CEO pay ratio and its drivers. Recommendations for future research include the following:

1. Additional periods, geographies and companies

Currently, the main limitation of our research is the number of datapoints. Hence, future research should try to use larger and multiple periods datasets. Although, US (and a large part of UK) data is widely available, future research could focus on exploring the ratio in other countries/ continents (e.g. Europe or Asia), given the relative absence of research there and the abundance for US/ UK research (Faleye et al., 2013). Furthermore, it could be interesting to do research where one combines US and European data, this could then be a “CEO pay ratio” revision of the famous paper on “the prince and the pauper” by Conyon and Murphy (2000).

2. Endogeneity problem

As said, endogeneity is a well-known statistical problem in research on CEO compensation but is notoriously hard to overcome. In this research, we employed an instrumental variable regression analysis with instruments for unionization and performance measurement (ROA). Although, the outcomes of the analysis were meaningful and give reason to belief that our model is robust, future research could test different instrumental variables (i.e. from different/ non-public databases) that prove to be even more relevant. Moreover, the creation of a panel-dataset should also increase the effectiveness of instrumental variables.

3. Private versus public companies

Academics have already shown that executive compensation and its determinants differ when researched at private firms (e.g. Michiels, Voordeckers, Lybaert & Steijvers, 2013). So, it is also likely that the CEO pay ratio shows different patterns for private firms. Moreover, we already looked at a factor that is strongly different for private firms compared to public firms: ownership. Our results show that this factor does have influence on the CEO pay ratio, which further justifies additional research focused on private firms (compared to public firms).

4. Next-in-line: Chief Financial Officer

In European listed companies, generally the second hierarchical position observed is the Chief Financial Officer (CFO). This position is also often the second-best paid position within the company. Obviously, it is also possible to calculate a pay ratio for this position (the CFO pay ratio), which automatically triggers the question whether the same drivers are applicable and if there are other factors that play a role. So far, only limited knowledge exists on what this next-in-line person earns and especially what he/ or she earns relative to the average employee pay in the company. Hence, a future direction for pay ratio research could be to also look at other executive positions, for example the CFO.

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APPENDIX A – SAMPLE CONSTITUENTS

Table A1 – scope parameters companies In this table, the 323 companies that are part of this research are displayed. They are sorted based on the country of their listing. The scope parameters are derived from Thomson Reuters Datastream and deal with the fiscal year applicable (mostly 2017, but some companies have broken book-year accounting, thus reporting is then over 2017-2018). The financial parameters revenue, market capitalization and assets are in million € (converted based on average exchange rates over 2017), where the market capitalization is calculated as the monthly average in 2017. Total employees represent the number of (full-time) employees. The subindustry is based on the classification system GICS as developed by MSCI.

Market Number of Country Company Total revenue capitalization Total assets employees GICS Subindustry

Netherlands Aalberts Industries NV € 2,694 € 4,146 € 2,910 16,003 Industrial Machinery

Netherlands ABN AMRO Group NV € 10,680 € 8,377 € 393,171 19,954 Diversified Banks

Netherlands Aegon NV € 57,808 € 10,136 € 396,291 28,318 Life & Health Insurance

Netherlands Akzo Nobel NV € 9,612 € 18,751 € 16,178 35,700 Specialty Chemicals

Netherlands Altice Europe NV € 23,500 € 25,987 € 72,753 47,143 Cable & Satellite

Netherlands ArcelorMittal SA € 57,252 € 23,374 € 71,104 197,000 Steel

Netherlands ASML Holding NV € 9,053 € 56,345 € 19,598 16,219 Semiconductor Equipment

Netherlands ASR Nederland NV € 6,249 € 4,516 € 55,405 4,117 Multi-line Insurance

Netherlands Gemalto NV € 2,972 € 4,230 € 4,309 15,000 Application Software

Netherlands Heineken NV € 21,888 € 47,890 € 41,034 80,425 Brewers

Netherlands ING Groep NV € 18,049 € 57,616 € 846,216 54,302 Diversified Banks

Netherlands Koninklijke Ahold Delhaize NV € 62,890 € 23,000 € 33,871 224,000 Food Retail

Netherlands Koninklijke DSM NV € 8,632 € 12,201 € 12,802 21,054 Specialty Chemicals

Netherlands Koninklijke KPN NV € 6,498 € 12,256 € 13,776 13,275 Integrated Telecommunication Services

Netherlands Koninklijke Philips NV € 17,780 € 29,529 € 25,315 63,798 Health Care Equipment

Netherlands Koninklijke Vopak NV € 1,306 € 4,954 € 5,115 3,639 Oil & Gas Storage & Transportation

Netherlands NN Group NV € 18,612 € 11,333 € 227,062 14,971 Life & Health Insurance

Netherlands Randstad NV € 23,273 € 9,595 € 9,763 37,930 Human Resource & Employment Services

Netherlands Royal Dutch Shell PLC € 254,400 € 208,037 € 339,360 86,000 Integrated Oil & Gas

Netherlands Signify NV € 6,965 € 4,445 € 6,678 32,130 Electrical Components & Equipment

Netherlands Unilever NV € 53,715 € 140,676 € 60,285 161,000 Personal Products

Netherlands WFD Unibail Rodamco NV € 2,078 € 21,543 € 43,241 2,012 Retail REITs

Netherlands Wolters Kluwer NV € 4,422 € 11,677 € 8,478 18,315 Research & Consulting Services

Belgium Ackermans & Van Haaren NV € 3,951 € 4,835 € 13,469 12,352 Multi-Sector Holdings

Belgium Ageas NV € 12,144 € 8,133 € 103,341 11,260 Multi-line Insurance

Belgium Anheuser Busch Inbev NV € 47,052 € 169,855 € 205,173 200,000 Brewers

Belgium Aperam SA € 4,479 € 3,562 € 4,366 9,600 Steel

Belgium argenx SE € 36 € 633 € 371 73 Biotechnology

Belgium Bpost SA € 2,972 € 4,665 € 3,223 25,323 Air Freight & Logistics

Belgium Cofinimmo SA € 262 € 2,292 € 3,783 129 Diversified REITs

Belgium Etablissementen Franz Colruyt NV € 9,031 € 6,839 € 4,054 27,795 Food Retail

Belgium Galapagos NV € 156 € 3,719 € 1,286 600 Biotechnology

Belgium Groep Brussel Lambert NV € 5,309 € 13,930 € 24,059 50 Multi-Sector Holdings

Belgium KBC Groep NV € 10,059 € 27,925 € 292,342 37,130 Diversified Banks

Belgium Ontex Group NV € 2,355 € 2,391 € 2,720 11,000 Personal Products

Belgium Proximus NV € 5,739 € 9,783 € 8,713 12,892 Integrated Telecommunication Services

Belgium Sofina SA € 67 € 4,388 € 5,796 57 Multi-Sector Holdings

Belgium Solvay SA € 10,891 € 12,482 € 21,451 24,500 Diversified Chemicals

Belgium Telenet Group Holding NV € 2,528 € 6,612 € 5,470 3,353 Cable & Satellite

Belgium Ucb SA € 4,530 € 12,470 € 9,917 7,478 Pharmaceuticals

Belgium Umicore SA € 11,947 € 7,123 € 5,116 13,129 Specialty Chemicals

France Accor SA € 1,937 € 11,657 € 12,080 18,393 Hotels, Resorts & Cruise Lines

France Air Liquide SA € 20,349 € 42,344 € 41,027 65,200 Industrial Gases

France Airbus SE € 66,767 € 58,203 € 111,074 129,442 Aerospace & Defense

France Atos SE € 12,691 € 12,842 € 13,484 89,989 IT Consulting & Other Services

France AXA SA € 132,892 € 59,099 € 870,128 95,728 Multi-line Insurance

France BNP Paribas SA € 87,613 € 78,816 € 1,949,778 196,128 Diversified Banks

France Bouygues SA € 32,923 € 13,816 € 36,303 119,836 Construction & Engineering

France Capgemini SE € 12,525 € 15,537 € 15,944 193,077 IT Consulting & Other Services

France Carrefour SA € 80,975 € 15,392 € 47,813 378,923 Hypermarkets & Super Centers

France Compagnie de Saint Gobain SA € 40,810 € 26,569 € 42,917 179,149 Building Products Compagnie Generale des France Etablissements Michelin SCA € 21,960 € 20,796 € 25,267 107,807 Tires & Rubber

France Credit Agricole SA € 63,328 € 39,302 € 1,551,600 73,707 Diversified Banks

France Danone SA € 24,677 € 43,599 € 44,259 104,843 Packaged Foods & Meats

France Engie SA € 65,029 € 32,710 € 150,140 150,000 Multi-Utilities

France EssilorLuxottica SA € 7,402 € 24,194 € 12,312 85,150 Health Care Supplies

France Hermes International SCA € 5,549 € 45,780 € 6,768 13,483 Apparel, Accessories & Luxury Goods

France Kering SA € 10,816 € 38,690 € 25,577 44,055 Apparel, Accessories & Luxury Goods

France Legrand SA € 5,521 € 15,845 € 9,424 37,356 Electrical Components & Equipment

France L'Oreal SA € 26,024 € 101,444 € 35,339 82,606 Personal Products LVMH Moet Hennessy Louis France Vuitton SE € 42,636 € 112,671 € 69,755 128,637 Apparel, Accessories & Luxury Goods

France Orange SA € 41,096 € 38,190 € 95,349 138,038 Integrated Telecommunication Services

France Pernod Ricard SA € 8,987 € 31,417 € 29,558 18,442 Distillers & Vintners

France Peugeot SA € 62,256 € 15,871 € 57,915 208,227 Automobile Manufacturers

France Publicis Groupe SA € 10,246 € 14,076 € 23,780 78,895 Advertising

France Renault SA € 58,770 € 24,262 € 109,899 181,344 Automobile Manufacturers

France Safran SA € 16,940 € 32,959 € 32,385 58,324 Aerospace & Defense

France Sanofi SA € 36,221 € 103,358 € 99,813 106,556 Pharmaceuticals

France Schneider Electric SE € 24,743 € 41,265 € 39,849 153,124 Electrical Components & Equipment

France Societe Generale SA € 55,683 € 37,561 € 1,274,200 147,125 Diversified Banks

France Sodexo SA € 20,698 € 16,498 € 14,874 427,268 Restaurants

France TechnipFMC PLC € 12,552 € 12,150 € 23,561 37,000 Oil & Gas Equipment & Services

France Total SA € 124,290 € 114,644 € 202,260 98,277 Integrated Oil & Gas

France Valeo SA € 18,550 € 14,419 € 17,468 111,600 Auto Parts & Equipment

France Veolia Environnement SA € 25,125 € 10,585 € 38,279 168,800 Multi-Utilities

France Vinci SA € 40,876 € 45,771 € 69,803 194,428 Construction & Engineering

France Vivendi SA € 12,501 € 25,291 € 34,236 41,743 Movies & Entertainment

Germany Adidas AG € 21,218 € 36,798 € 14,019 56,888 Apparel, Accessories & Luxury Goods

Germany Allianz SE € 103,039 € 80,802 € 901,300 140,553 Multi-line Insurance

Germany BASF SE € 64,475 € 80,802 € 78,768 115,490 Diversified Chemicals

Germany Bayer AG € 35,015 € 90,403 € 75,087 99,820 Pharmaceuticals

Germany Bayerische Motoren Werke AG € 98,678 € 54,500 € 195,506 129,932 Automobile Manufacturers

Germany Beiersdorf AG € 7,056 € 23,171 € 8,205 18,934 Personal Products

Germany Commerzbank AG € 9,907 € 12,728 € 452,513 49,417 Diversified Banks

Germany Continental AG € 44,010 € 40,529 € 37,441 235,473 Auto Parts & Equipment

Germany Covestro AG € 14,138 € 14,754 € 11,341 16,176 Specialty Chemicals

Germany Daimler AG € 164,330 € 71,605 € 255,345 289,321 Automobile Manufacturers

Germany Deutsche Bank AG € 29,548 € 30,544 € 1,474,732 97,535 Diversified Capital Markets

Germany Deutsche Boerse AG € 2,803 € 17,329 € 135,141 5,640 Financial Exchanges & Data

Germany Deutsche Lufthansa AG € 35,579 € 9,567 € 36,267 129,424 Airlines

Germany Deutsche Post AG € 60,444 € 42,621 € 38,672 519,544 Air Freight & Logistics

Germany Deutsche Telekom AG € 74,947 € 74,916 € 141,334 217,349 Integrated Telecommunication Services

Germany E.ON SE € 37,965 € 18,361 € 55,950 42,699 Multi-Utilities Fresenius Medical Care AG & Co Germany KGaA € 17,784 € 25,104 € 24,025 114,000 Health Care Services

Germany Fresenius SE & Co KGaA € 33,886 € 39,576 € 53,133 273,249 Health Care Services

Germany HeidelbergCement AG € 17,266 € 17,133 € 34,558 59,054 Construction Materials

Germany Henkel AG & Co KgaA € 20,029 € 48,017 € 28,344 53,700 Household Products

Germany Infineon Technologies AG € 7,063 € 22,587 € 9,945 37,479 Semiconductors

Germany Linde AG € 17,113 € 31,127 € 33,412 57,605 Industrial Gases

Germany Merck KGaA € 15,327 € 12,740 € 35,621 52,880 Pharmaceuticals Muenchener Rueckversicherungs Germany Gesellschaft AG in Muenchen € 61,595 € 28,166 € 265,722 42,410 Reinsurance

Germany RWE AG € 42,434 € 10,474 € 69,059 19,106 Multi-Utilities

Germany SAP SE € 23,461 € 112,587 € 42,506 88,543 Application Software

Germany Siemens AG € 83,049 € 102,216 € 136,111 372,000 Industrial Conglomerates

Germany Thyssenkrupp AG € 41,447 € 13,923 € 35,048 158,739 Steel

Germany Volkswagen AG € 230,682 € 72,980 € 422,193 642,292 Automobile Manufacturers

Germany Vonovia SE € 5,977 € 16,864 € 37,516 8,448 Real Estate Operating Companies

Italy A2A SpA € 5,796 € 4,475 € 9,949 11,280 Multi-Utilities

Italy Assicurazioni Generali SpA € 87,947 € 23,225 € 537,080 71,327 Multi-line Insurance

Italy Atlantia SpA € 6,365 € 20,760 € 40,057 16,745 Highways & Railtracks

Italy Azimut Holding SpA € 790 € 2,461 € 8,107 830 Asset Management & Custody Banks

Italy Banca Generali SpA € 840 € 3,126 € 8,991 873 Asset Management & Custody Banks

Italy Banca Mediolanum SpA € 3,974 € 5,249 € 43,267 2,698 Other Diversified Financial Services

Italy Banco Bpm SpA € 7,586 € 4,342 € 161,207 22,289 Diversified Banks

Italy Bper Banca SpA € 2,248 € 2,222 € 71,339 11,653 Diversified Banks

Italy Buzzi Unicem SpA € 2,806 € 4,273 € 5,783 10,157 Construction Materials

Italy CNH Industrial NV € 23,297 € 13,436 € 42,346 63,356 Agricultural & Farm Machinery

Italy Davide Campari Milano SpA € 1,816 € 6,871 € 4,419 4,020 Distillers & Vintners

Italy Enel SpA € 72,664 € 48,343 € 155,641 62,900 Electric Utilities

Italy Eni SpA € 66,919 € 50,815 € 114,928 32,934 Integrated Oil & Gas

Italy Exor NV € 143,430 € 12,033 € 163,775 21 Multi-Sector Holdings

Italy Ferrari NV € 3,417 € 15,278 € 4,141 3,380 Automobile Manufacturers

Italy Fiat Chrysler Automobiles NV € 110,934 € 17,527 € 96,299 237,150 Automobile Manufacturers

Italy FinecoBank Banca Fineco SpA € 587 € 4,277 € 22,340 1,119 Diversified Banks

Italy Freni Brembo SpA € 2,464 € 4,453 € 2,299 9,837 Auto Parts & Equipment

Italy Intesa Sanpaolo SpA € 29,224 € 44,818 € 796,861 96,892 Diversified Banks

Italy Italgas SpA € 1,571 € 3,652 € 5,844 3,580 Gas Utilities

Italy Leonardo SpA € 11,527 € 7,823 € 24,676 45,134 Aerospace & Defense

Italy Luxottica Group SpA € 9,157 € 24,344 € 10,064 85,150 Apparel, Accessories & Luxury Goods

Italy Mediaset SpA € 3,631 € 4,066 € 5,781 5,470 Broadcasting Mediobanca Banca di Credito Italy Finanziario SpA € 2,518 € 7,688 € 72,301 4,717 Diversified Banks

Italy Moncler SpA € 1,194 € 5,602 € 1,380 3,498 Apparel, Accessories & Luxury Goods

Italy Pirelli & C SpA € 5,352 € 7,002 € 12,734 30,189 Tires & Rubber

Italy Poste Italiane SpA € 32,856 € 8,071 € 202,670 127,435 Life & Health Insurance

Italy Prysmian SpA € 7,901 € 5,767 € 6,728 21,050 Electrical Components & Equipment Recordati Industria Chimica e Italy Farmaceutica SpA € 1,288 € 7,464 € 2,056 4,176 Pharmaceuticals

Italy Saipem SpA € 9,017 € 3,711 € 12,590 32,058 Oil & Gas Equipment & Services

Italy Salvatore Ferragamo SpA € 1,393 € 4,185 € 1,183 4,183 Apparel, Accessories & Luxury Goods

Italy Snam SpA € 2,533 € 14,204 € 21,816 2,919 Oil & Gas Storage & Transportation

Italy STMicroelectronics NV € 6,958 € 14,382 € 9,063 45,468 Semiconductors

Italy Telecom Italia SpA € 19,828 € 15,892 € 68,783 59,429 Integrated Telecommunication Services

Italy Terna Rete Elettrica Nazionale SpA € 2,184 € 9,749 € 16,917 3,897 Electric Utilities

Italy UniCredit SpA € 23,039 € 35,347 € 836,790 91,952 Diversified Banks

Italy Unione di Banche Italiane SpA € 4,675 € 4,119 € 127,376 21,414 Diversified Banks

Italy Unipol Gruppo SpA € 12,988 € 2,753 € 89,973 14,188 Multi-line Insurance

Italy UnipolSai Assicurazioni SpA € 12,996 € 5,613 € 67,750 10,907 Multi-line Insurance

Spain Acciona SA € 7,254 € 4,176 € 17,147 37,403 Electric Utilities

Spain Acerinox SA € 4,627 € 3,374 € 4,404 6,952 Steel ACS Actividades de Construccion y Spain Servicios SA € 34,898 € 10,172 € 31,881 181,527 Construction & Engineering

Spain Aena SME SA € 3,961 € 23,824 € 15,307 8,174 Airport Services

Spain Amadeus IT Group SA € 4,853 € 22,881 € 7,883 14,963 Data Processing & Outsourced Services Banco Bilbao Vizcaya Argentaria Spain SA € 32,261 € 47,637 € 690,059 131,856 Diversified Banks

Spain Banco de Sabadell SA € 7,026 € 9,574 € 221,348 25,845 Diversified Banks

Spain Banco Santander SA € 56,108 € 86,976 € 1,444,305 202,251 Diversified Banks

Spain Bankia SA € 3,421 € 11,818 € 213,932 17,757 Diversified Banks

Spain Bankinter SA € 2,503 € 7,138 € 71,333 7,772 Diversified Banks

Spain Caixabank SA € 10,492 € 24,003 € 383,186 36,972 Diversified Banks

Spain Cellnex Telecom SA € 758 € 4,175 € 4,056 1,403 Integrated Telecommunication Services

Spain Cie Automotive SA € 3,724 € 2,729 € 4,480 28,728 Auto Parts & Equipment Distribuidora Internacional de Spain Alimentacion SA € 8,621 € 3,131 € 3,626 42,613 Hypermarkets & Super Centers

Spain Enagas SA € 1,360 € 5,782 € 9,573 1,426 Oil & Gas Storage & Transportation

Spain Endesa SA € 20,057 € 21,229 € 31,037 9,706 Electric Utilities

Spain Ferrovial SA € 12,208 € 13,813 € 22,990 95,978 Construction & Engineering

Spain Grifols SA € 4,318 € 14,900 € 10,920 18,309 Biotechnology

Spain Iberdrola SA € 31,263 € 42,254 € 110,689 28,750 Electric Utilities

Spain Indra Sistemas SA € 3,031 € 2,121 € 3,867 40,020 IT Consulting & Other Services

Spain Industria de Diseno Textil SA € 25,336 € 100,611 € 20,231 171,839 Apparel Retail

Spain Inmobiliaria Colonial SOCIMI SA € 286 € 2,942 € 10,508 166 Office REITs International Consolidated Airlines Spain Group SA € 22,972 € 13,890 € 27,232 63,422 Airlines

Spain Mapfre SA € 22,184 € 9,121 € 67,569 36,271 Multi-line Insurance

Spain Mediaset Espana Comunicacion SA € 986 € 3,603 € 1,235 1,273 Broadcasting

Spain Melia Hotels International SA € 1,885 € 2,862 € 3,333 21,326 Hotels, Resorts & Cruise Lines

Spain Merlin Properties SOCIMI SA € 463 € 5,211 € 12,005 162 Diversified REITs

Spain Naturgy Energy Group SA € 23,306 € 19,659 € 47,322 15,374 Gas Utilities

Spain Red Electrica Corporacion SA € 1,941 € 9,881 € 10,918 1,815 Electric Utilities

Spain Repsol SA € 41,668 € 22,178 € 59,857 24,226 Integrated Oil & Gas Siemens Gamesa Renewable Spain Energy SA € 6,538 € 8,866 € 16,467 25,489 Heavy Electrical Equipment

Spain Tecnicas Reunidas SA € 5,068 € 1,772 € 3,886 8,644 Oil & Gas Equipment & Services

Spain Telefonica SA € 52,008 € 47,348 € 115,066 122,718 Integrated Telecommunication Services

Spain Viscofan SA € 778 € 2,407 € 961 4,748 Packaged Foods & Meats

Sweden AB SKF € 7,922 € 8,384 € 8,253 45,678 Industrial Machinery

Sweden Alfa Laval AB € 3,589 € 7,924 € 5,341 16,367 Industrial Machinery

Sweden Assa Abloy AB € 7,738 € 19,557 € 10,107 47,426 Building Products

Sweden Atlas Copco AB € 11,833 € 40,061 € 12,810 47,599 Industrial Machinery

Sweden Autoliv Inc € 8,655 € 8,738 € 7,127 63,000 Auto Parts & Equipment

Sweden Boliden AB € 5,034 € 7,478 € 5,680 5,684 Diversified Metals & Mining

Sweden Electrolux AB € 12,275 € 8,601 € 9,101 56,708 Household Appliances

Sweden Essity AB (publ) € 11,106 € 16,909 € 14,943 47,700 Household Products

Sweden Getinge AB € 2,286 € 3,745 € 4,279 10,684 Health Care Equipment

Sweden H & M Hennes & Mauritz AB € 20,068 € 32,373 € 10,692 123,178 Apparel Retail

Sweden Hexagon AB € 3,448 € 13,930 € 8,613 18,315 Electronic Equipment & Instruments

Sweden Investor AB € 4,585 € 30,484 € 41,526 20,054 Multi-Sector Holdings

Sweden Kinnevik AB € 230 € 7,286 € 9,516 36 Multi-Sector Holdings

Sweden Nordea Bank Abp € 10,332 € 37,787 € 581,612 30,399 Diversified Banks

Sweden Sandvik AB € 9,232 € 17,592 € 10,852 43,024 Industrial Machinery

Sweden Securitas AB € 9,371 € 5,053 € 5,036 287,732 Security & Alarm Services

Sweden Skandinaviska Enskilda Banken AB € 4,983 € 23,168 € 259,881 15,804 Diversified Banks

Sweden Skanska AB € 16,046 € 8,120 € 11,123 40,759 Construction & Engineering

Sweden SSAB AB € 6,714 € 3,614 € 8,891 14,561 Steel

Sweden Svenska Cellulosa SCA AB € 1,694 € 11,873 € 5,764 4,031 Forest Products

Sweden Svenska Handelsbanken AB € 4,494 € 24,475 € 281,232 11,832 Diversified Banks

Sweden Swedbank AB € 5,169 € 24,785 € 224,889 14,588 Diversified Banks

Sweden Swedish Match AB € 1,194 € 5,639 € 1,360 5,413 Tobacco

Sweden Tele2 AB € 2,519 € 4,830 € 4,037 6,924 Wireless Telecommunication Services

Sweden Telefonaktiebolaget LM Ericsson € 20,874 € 18,871 € 26,414 100,735 Communications Equipment

Sweden Telia Company AB € 8,110 € 16,845 € 24,939 25,472 Integrated Telecommunication Services

Sweden Volvo AB € 34,023 € 31,235 € 42,588 87,104 Construction Machinery & Heavy Trucks

Switzerland ABB Ltd € 28,603 € 47,155 € 36,227 134,800 Electrical Components & Equipment

Switzerland Adecco Group AG € 23,660 € 11,237 € 9,890 34,000 Human Resource & Employment Services

Compagnie Financiere Richemont Switzerland SA € 10,979 € 38,723 € 25,558 28,740 Apparel, Accessories & Luxury Goods

Switzerland Credit Suisse Group AG € 17,882 € 32,433 € 681,304 46,840 Diversified Capital Markets

Switzerland Geberit AG € 2,488 € 14,698 € 3,202 11,146 Building Products

Switzerland Givaudan SA € 4,322 € 16,480 € 6,254 11,170 Specialty Chemicals

Switzerland Julius Baer Gruppe AG € 3,126 € 10,676 € 83,778 6,292 Asset Management & Custody Banks

Switzerland LafargeHolcim Ltd € 22,356 € 30,603 € 54,484 81,960 Construction Materials

Switzerland Lonza Group AG € 4,368 € 13,786 € 11,834 14,618 Life Sciences Tools & Services

Switzerland Nestle SA € 76,825 € 223,544 € 113,974 323,000 Packaged Foods & Meats

Switzerland Novartis AG € 41,793 € 186,896 € 110,936 121,597 Pharmaceuticals

Switzerland Roche Holding AG € 45,603 € 191,430 € 65,604 93,734 Pharmaceuticals

Switzerland SGS SA € 5,432 € 15,760 € 5,085 95,745 Research & Consulting Services

Switzerland Sika AG € 5,346 € 12,653 € 4,956 18,484 Specialty Chemicals

Switzerland Swiss Life Holding AG € 15,887 € 9,618 € 182,071 7,979 Life & Health Insurance

Switzerland Swiss Re AG € 35,272 € 28,628 € 185,500 14,485 Reinsurance

Switzerland Swisscom AG € 9,978 € 21,992 € 18,873 20,506 Integrated Telecommunication Services

Switzerland The Swatch Group AG € 6,811 € 18,393 € 11,533 35,360 Apparel, Accessories & Luxury Goods

Switzerland UBS Group AG € 26,523 € 56,715 € 783,422 61,253 Diversified Capital Markets

Switzerland Zurich Insurance Group AG € 50,975 € 38,856 € 351,838 51,633 Multi-line Insurance

United Kingdom 3i Group PLC € 1,492 € 9,553 € 8,985 241 Asset Management & Custody Banks

United Kingdom Admiral Group PLC € 1,272 € 6,340 € 6,600 9,336 Property & Casualty Insurance

United Kingdom Anglo American PLC € 21,876 € 20,501 € 45,483 69,000 Diversified Metals & Mining

United Kingdom Antofagasta PLC € 3,959 € 10,058 € 11,845 5,410 Copper

United Kingdom Ashtead Group PLC € 4,225 € 9,809 € 7,688 15,996 Trading Companies & Distributors

United Kingdom Associated British Foods PLC € 17,479 € 26,323 € 14,580 132,590 Packaged Foods & Meats

United Kingdom AstraZeneca PLC € 18,727 € 69,681 € 52,813 61,100 Pharmaceuticals

United Kingdom Aviva PLC € 55,776 € 23,816 € 498,631 30,021 Multi-line Insurance

United Kingdom BAE Systems PLC € 20,638 € 22,279 € 25,930 83,200 Aerospace & Defense

United Kingdom Barclays PLC € 26,211 € 40,058 € 1,276,466 79,900 Diversified Banks

United Kingdom Barratt Developments PLC € 5,511 € 6,779 € 7,833 6,193 Homebuilding

United Kingdom Berkeley Group Holdings PLC € 3,082 € 5,388 € 5,446 2,617 Homebuilding

United Kingdom BHP Billiton PLC € 37,352 € 87,799 € 95,859 65,000 Diversified Metals & Mining

United Kingdom BP PLC € 200,240 € 105,719 € 230,506 74,000 Integrated Oil & Gas

United Kingdom British American Tobacco PLC € 22,036 € 118,372 € 158,880 55,761 Tobacco

United Kingdom British Land Company PLC € 727 € 7,261 € 14,992 559 Retail REITs

United Kingdom BT Group PLC € 27,011 € 33,568 € 48,732 105,800 Integrated Telecommunication Services

United Kingdom Bunzl plc € 9,665 € 8,705 € 5,845 17,595 Trading Companies & Distributors

United Kingdom Burberry Group PLC € 3,109 € 8,612 € 2,529 9,752 Apparel, Accessories & Luxury Goods

United Kingdom Carnival PLC € 14,712 € 40,110 € 34,261 97,000 Hotels, Resorts & Cruise Lines

United Kingdom Centrica PLC € 31,564 € 12,210 € 23,292 34,901 Multi-Utilities

United Kingdom Coca Cola HBC AG € 6,522 € 9,468 € 6,630 29,427 Soft Drinks

United Kingdom Compass Group PLC € 25,595 € 28,901 € 12,451 588,112 Restaurants

United Kingdom CRH PLC € 25,220 € 26,214 € 31,633 85,363 Construction Materials

United Kingdom Croda International PLC € 1,547 € 5,834 € 1,857 4,309 Specialty Chemicals

United Kingdom DCC PLC € 16,226 € 7,170 € 6,856 10,244 Industrial Conglomerates

United Kingdom Diageo PLC € 13,750 € 69,241 € 33,591 29,362 Distillers & Vintners

United Kingdom Direct Line Insurance Group PLC € 3,937 € 5,680 € 11,205 10,808 Property & Casualty Insurance

United Kingdom DS Smith PLC € 6,573 € 5,480 € 7,216 27,097 Paper Packaging

United Kingdom Easyjet PLC € 5,724 € 5,567 € 6,772 11,655 Airlines

United Kingdom EVRAZ plc € 9,025 € 4,187 € 8,653 70,186 Steel

United Kingdom Experian PLC € 3,784 € 16,868 € 6,676 16,239 Research & Consulting Services

United Kingdom Ferguson Plc € 17,750 € 14,321 € 8,681 33,511 Trading Companies & Distributors

United Kingdom Fresnillo PLC € 1,745 € 12,353 € 3,950 4,784 Precious Metals & Minerals

United Kingdom GlaxoSmithKline PLC € 34,001 € 85,517 € 63,506 98,462 Pharmaceuticals

United Kingdom Glencore PLC € 171,287 € 54,361 € 113,032 145,977 Diversified Metals & Mining

United Kingdom GVC Holdings PLC € 896 € 2,704 € 1,760 2,657 Casinos & Gaming

United Kingdom Halma PLC € 1,224 € 4,789 € 1,646 6,341 Electronic Equipment & Instruments

United Kingdom Hargreaves Lansdown PLC € 506 € 7,822 € 895 1,398 Asset Management & Custody Banks

United Kingdom HSBC Holdings PLC € 57,680 € 161,453 € 2,102,174 228,687 Diversified Banks

United Kingdom Imperial Brands PLC € 34,303 € 37,484 € 35,146 33,800 Tobacco

United Kingdom Informa PLC € 1,980 € 6,426 € 5,510 7,539 Advertising

United Kingdom InterContinental Hotels Group PLC € 1,487 € 9,078 € 2,509 6,658 Hotels, Resorts & Cruise Lines

United Kingdom Intertek Group PLC € 3,119 € 8,239 € 2,312 43,906 Research & Consulting Services

United Kingdom ITV PLC € 3,528 € 8,492 € 3,777 6,055 Broadcasting

United Kingdom J Sainsbury PLC € 32,022 € 6,336 € 24,758 52,800 Food Retail

United Kingdom Johnson Matthey PLC € 16,064 € 6,800 € 5,480 12,319 Specialty Chemicals

United Kingdom Just Eat PLC € 615 € 5,058 € 1,143 2,116 Internet & Direct Marketing Retail

United Kingdom Kingfisher PLC € 13,316 € 8,058 € 11,822 78,000 Home Improvement Retail

United Kingdom Land Securities Group PLC € 969 € 9,036 € 16,458 570 Diversified REITs

United Kingdom Legal & General Group PLC € 45,608 € 17,493 € 569,809 7,570 Life & Health Insurance

United Kingdom Lloyds Banking Group PLC € 40,332 € 55,104 € 914,742 67,905 Diversified Banks

United Kingdom London Stock Exchange Group PLC € 2,202 € 14,153 € 835,750 4,908 Financial Exchanges & Data

United Kingdom Marks and Spencer Group PLC € 12,169 € 6,288 € 8,588 80,787 Department Stores

United Kingdom Melrose Industries PLC € 2,357 € 4,806 € 3,543 11,960 Heavy Electrical Equipment

United Kingdom Micro Focus International PLC € 1,267 € 8,172 € 4,264 4,826 Application Software

United Kingdom Mondi PLC € 7,096 € 10,731 € 7,376 26,300 Paper Products

United Kingdom National Grid PLC € 17,347 € 38,601 € 66,869 23,023 Multi-Utilities

United Kingdom Next PLC € 4,627 € 7,242 € 2,922 28,318 Department Stores

United Kingdom NMC Health PLC € 1,337 € 5,425 € 2,450 13,739 Health Care Facilities

United Kingdom Ocado Group PLC € 1,658 € 2,119 € 1,013 12,799 Internet & Direct Marketing Retail

United Kingdom Paddy Power Betfair PLC € 1,966 € 7,818 € 5,551 7,503 Casinos & Gaming

United Kingdom Pearson PLC € 5,083 € 6,276 € 8,885 30,339 Publishing

United Kingdom Persimmon PLC € 4,052 € 8,498 € 5,358 4,535 Homebuilding

United Kingdom Prudential PLC € 97,308 € 51,967 € 556,364 27,151 Life & Health Insurance

United Kingdom Randgold Resources Ltd € 1,067 € 7,709 € 3,587 2,975 Gold

United Kingdom Reckitt Benckiser Group PLC € 12,967 € 57,510 € 41,691 40,400 Household Products

United Kingdom Relx PLC € 8,269 € 38,174 € 14,228 31,000 Research & Consulting Services

United Kingdom Rentokil Initial PLC € 2,717 € 5,887 € 3,500 36,036 Environmental & Facilities Services

United Kingdom Rightmove PLC € 274 € 4,375 € 81 479 Interactive Media & Services

United Kingdom Rio Tinto PLC € 33,369 € 72,197 € 79,798 46,807 Diversified Metals & Mining

United Kingdom Rolls-Royce Holdings PLC € 16,686 € 17,773 € 31,396 50,000 Aerospace & Defense

United Kingdom Royal Bank of Scotland Group PLC € 15,850 € 34,916 € 831,330 71,200 Diversified Banks

United Kingdom Royal Mail PLC € 11,639 € 4,736 € 8,303 159,117 Air Freight & Logistics

United Kingdom RSA Insurance Group PLC € 8,003 € 7,210 € 23,203 13,363 Property & Casualty Insurance

United Kingdom Sage Group PLC € 1,945 € 8,598 € 3,655 13,795 Application Software

United Kingdom Schroders PLC € 2,829 € 9,944 € 25,330 4,013 Asset Management & Custody Banks

United Kingdom SEGRO PLC € 335 € 5,716 € 8,938 293 Industrial REITs

United Kingdom Severn Trent PLC € 1,927 € 6,089 € 10,717 6,265 Water Utilities

United Kingdom Shire PLC € 12,638 € 43,584 € 56,483 23,044 Biotechnology

United Kingdom Sky PLC € 15,357 € 19,145 € 20,350 30,000 Cable & Satellite

United Kingdom Smith & Nephew PLC € 3,972 € 13,037 € 6,557 15,933 Health Care Equipment

United Kingdom Smiths Group PLC € 3,607 € 7,037 € 5,807 21,900 Industrial Conglomerates

United Kingdom Smurfit Kappa Group PLC € 8,562 € 6,098 € 9,005 46,000 Paper Packaging

United Kingdom SSE PLC € 35,520 € 16,489 € 26,407 20,786 Electric Utilities

United Kingdom St. James's Place PLC € 10,138 € 6,961 € 101,381 2,014 Asset Management & Custody Banks

United Kingdom Standard Chartered PLC € 12,764 € 28,590 € 553,101 86,021 Diversified Banks

United Kingdom Standard Life Aberdeen PLC € 2,122 € 11,084 € 223,148 7,768 Other Diversified Financial Services

United Kingdom Taylor Wimpey PLC € 4,466 € 7,194 € 5,594 5,183 Homebuilding

United Kingdom Tesco PLC € 65,310 € 17,412 € 50,988 448,988 Food Retail

United Kingdom Tui AG € 18,535 € 8,293 € 14,186 66,577 Hotels, Resorts & Cruise Lines

United Kingdom United Utilities Group PLC € 1,974 € 7,095 € 14,791 5,223 Water Utilities

United Kingdom Vodafone Group PLC € 46,571 € 65,751 € 145,611 106,135 Wireless Telecommunication Services

United Kingdom Whitbread PLC € 3,700 € 8,155 € 5,493 52,705 Restaurants

United Kingdom WM Morrison Supermarkets PLC € 19,562 € 6,315 € 10,955 105,487 Food Retail

United Kingdom WPP PLC € 17,802 € 22,927 € 37,917 134,413 Advertising

APPENDIX B – VARIABLE LIST

Table B2 – Variable definitions and sources This table provides an overview of all variables employed in this research. Specifically, we report the relevant hypothesis, a description and their respective source(s). First, we describe the three dependent variables, where the CEO pay ratio variable is our main unit of analysis, but for robustness test we also perform regressions with CEO compensation and employee compensation independently. Second, all independent variables are displayed by following the order of the hypothesis (legal, cultural, social and firm). Third, we outline the control variables. Variable Hypothesis Type Description Source CEOpayratio Dependent Total CEO compensation BoardEx relative to average employee Hand-collected compensation CEOcompensation Dependent Total CEO compensation (i.e. BoardEx (robustness) base salary plus short- and Hand-collected long-term incentives plus other benefits) Employeecompensation Dependent Average employee Thomson Reuters (robustness) compensation. Computed by Hand-collected dividing total employee benefits by the number of (full- time) employees AntiDirectorIndex 1A Independent Country score on shareholder LaPorta et al. (1998) protection developed by LaPorta et al. (1998). Score between 1 and 6 ShareholderRightsIndex 1A Independent Country score on shareholder Martynova and developed by Martynova and Renneboog (2010) Renneboog (2010). Score between 0 and 32 WGIscore 1B Independent Country score on the OECD effectiveness of governments to create and implement good corporate governance. Score between -2.5 and 2.5 CorporateRate 1B Independent The marginal corporate tax OECD rate in a country PowerDistance 2A Independent Score (0-100) on power Hofstede distance according to Hofstede Individualism 2B Independent Score (0-100) on individualism Hofstede according to Hofstede UncertaintyAvoidance 2C Independent Score (0-100) on uncertainty Hofstede avoidance according to Hofstede LongTermOrientation 2D Independent Score (0-100) on long-term Hofstede orientation according to Hofstede Masculinity 2E Independent Score (0-100) on masculinity Hofstede according to Hofstede

Indulgence 2F Independent Score (0-100) on indulgence Hofstede according to Hofstede GINI 2G Independent Gini coefficient of a country. OECD Coefficient between 0 and 1 Unemployment 2G Independent Unemployed people (% of total OECD labor force) Poverty 2G Independent Number of people with income OECD below threshold (% of total inhabitants) Unionization 2H Independent Union members (% of total OECD labor force) Innovation 3A Independent Intangible assets (% of total Thomson Reuters assets) B2C 3B Independent Equals 1 if serves B2C market Own measure based on and 0 if not GICS (via Thomson Reuters) WorkforceScore 3C Independent Workforce score developed by ESG (via Thomson ESG that measures several Reuters) workplace measures SustainabilityScore 3C Independent Overall sustainability score ESG (via Thomson developed by ESG that Reuters) measures companies’ initiatives on social, environmental and governance issues Ownership 3D Independent 100% - Free-float shares (% of Thomson Reuters outstanding share capital) Sales Control 푙푛(sales) Thomson Reuters Employees Control Number of employees Thomson Reuters Leverage Control Debt-to-Equity ratio (D/E Thomson Reuters ratio) Growth Control Price-to-Earnings ratio (P/E Thomson Reuters ratio) ROA Control Return-on-Assets (ROA) Thomson Reuters TSR Control Total Shareholder Return Thomson Reuters (TSR) SD Control Standard Deviation of monthly Thomson Reuters return Beta Control Levered beta Thomson Reuters GDP Control Gross Domestic Product (GDP) OECD per capita BoardSize Control Number of directors on a BoardEx board Gender Control Gender of CEO BoardEx Age Control Age of CEO BoardEx Tenure Control Tenure of CEO BoardEx Industry fixed effects Control 11 dummies based on GICS. Thomson Reuters Takes value 1 if in industry and 0 if not

APPENDIX C – DESCRIPTIVE STATISTICS

Table C1 – Sample distribution

This table reports the final sample distribution per country and per industry. Countries are based on the primary listing of the company (e.g. certain companies have a legal form in another country, but these are not considered to be located there). For the classification of industries, we used the eleven GICS codes developed by MSCI. Panel A

Number of Percentage of Firm Percentage of companies companies Index Country observations total sample excluded excluded BEL20 Belgium 18 5.57% 2 10.00% SSMI Switzerland 20 6.19% 0 0.00% AEX the Netherlands 23 7.12% 2 8.00% OMX Sweden 27 8.36% 3 10.00% DAX Germany 30 9.29% 0 0.00% IBEX35 Spain 33 10.22% 2 5.71% CAC40 France 36 11.15% 4 10.00% FTSE MIB Italy 39 12.07% 1 2.50% FTSE100 United Kingdom 97 30.03% 3 3.00% Total 323 100.00% 17 5%

Panel B

Firm Percentage of Industry observations total sample Real Estate 8 2.48% Energy 11 3.41% Information Technology 14 4.33% Utilities 18 5.57% Health Care 21 6.50% Communication Services 24 7.43% Consumer Staples 27 8.36% Materials 34 10.53% Consumer Discretionary 46 14.24% Industrials 50 15.48% Financials 70 21.67% Total 323 100.00%

Table C2 – Summary statistics

This table reports the summary statistics of all variables in our research. The firm’s total sales are reported in € millions. To mitigate the effect of outliers, all continuous variables are Winsor sized at the 1st and 99th percentiles. In Appendix B, a definition for all variables is given.

N Mean St. Dev P25 Median P75 Dependent variables: CEOpayratio 323 82.494 70.662 32.729 63.707 108.53 CEOcompensation 323 4,780,000 3,000,000 2,347,000 4,359,000 6,553,156 Employeecompensation 323 78,567.04 80,101.09 48,746.97 65,011.18 86,456 Legal variables: AntiDirectorIndex 323 2.929 1.002 2 3 4 ShareholderRightsIndex 323 20.155 4.2 17 19 24 WGIscore 323 1.46 .417 1.33 1.61 1.74 CorporateRate 323 23.972 4.907 19 24 29 Cultural variables: PowerDistance 323 44.226 12.868 35 35 57 Individualism 323 75.272 11.491 68 75 89 UncertaintyAvoidance 323 59.003 22.598 35 58 86 LongTermOrientation 323 60.669 11.66 51 61 67 Masculinity 323 52.245 21.083 42 66 66 Indulgence 323 56.529 15.343 44 66 69 Social variables: GINI 323 .318 .03 .293 .333 .351 Unemployment 323 7.409 4.136 4.335 4.84 9.399 Poverty 323 10.937 2.332 9.1 11.1 11.1 Unionization 323 25.959 16.29 15.7 23.7 34.4 Firm-level variables: Ownership 323 18.83 21.444 0.721 9.688 33.336 Innovation 323 10.003 14.351 1.055 4.271 13.123 B2C 323 .533 .5 0 1 1 WorkforceScore 323 76.716 19.331 67.857 80.769 91.477 CSRScore 323 64.381 24.278 47.917 68.367 81.53 Control variables: GDP 323 39,595.05 5,827.104 36,684.55 37,925.46 43,188.03 Sales 323 22,054.56 33,178.91 3,631 10,137.74 25,336 Employees 323 60,744.37 85,010.05 10,244 30,021 79,900 Leverage 323 4.659 6.449 1.037 1.969 4.318 Growth 323 18.682 16.882 11.932 17.054 24.927 ROA 323 .052 .054 .014 .044 .073 TSR 323 14.234 17.839 3.264 14.013 25.552 SD 323 19.801 6.145 15.434 18.424 22.616 Beta 323 .979 .354 .746 .945 1.173 BoardSize 323 12.771 4.545 10 12 14 Gender 323 .954 .211 1 1 1 Age 323 56.724 6.486 53 56 61 Tenure 323 7.183 6.226 2.7 5.9 9.9

Table C3 – Compensation landscape

This table reports the different components of CEO compensation, average employee compensation and the CEO pay ratio on a country/ index level (Panel A) and an industry level (Panel B). CEO compensation and average employee salary are reported as the actual euro value. These values are used in the calculation of the CEO pay ratio. The percentages for the CEO compensation components are measured relative to the total CEO compensation Panel A:

CEO compensation Country Obs. Mean Min P25 Median P75 Max Belgium 18 2,759,301 647,000 1,222,000 2,060,000 2,576,000 13,300,000 France 36 5,218,533 1,389,000 2,971,070 4,850,691 7,115,715 11,300,000 Germany 30 6,930,718 2,814,451 5,366,350 6,934,709 7,621,991 14,000,000 Italy 39 3,402,671 411,000 1,515,000 2,347,000 4,902,000 13,300,000 Netherlands 23 4,278,293 876,000 2,707,000 3,440,000 5,796,000 12,400,000 Spain 33 3,684,027 163,000 1,437,000 2,709,000 5,066,000 10,300,000 Sweden 27 2,710,863 1,033,221 2,005,194 2,437,978 3,215,375 4,954,782 Switzerland 20 5,881,684 1,682,321 3,543,411 4,963,207 8,063,072 12,800,000 United-Kingdom 97 5,725,250 950,864 3,456,998 4,922,850 7,733,753 13,800,000 Total 323 4,781,146 163,000 2,347,000 4,359,000 6,553,156 14,000,000

Average employee compensation Country Obs. Mean Min P25 Median P75 Max Belgium 18 119,441 30,394 52,914 74,347 131,286 548,776 France 36 58,939 20,335 41,810 60,849 75,152 101,447 Germany 30 81,280 42,685 60,465 72,241 91,808 245,961 Italy 39 93,286 34,362 53,138 67,144 74,978 1,128,457 Netherlands 23 79,691 40,221 50,381 82,710 97,212 146,031 Spain 33 72,956 18,951 48,433 61,215 80,585 404,348 Sweden 27 75,879 26,381 47,217 63,976 74,487 411,880 Switzerland 20 105,336 30,014 60,032 91,329 133,030 225,559 United-Kingdom 97 68,381 16,966 41,823 60,999 81,442 369,749 Total 323 78,567 16,966 48,747 65,011 86,456 1,128,457

CEO pay ratio Country Obs. Mean Min P25 Median P75 Max Belgium 18 46 4 10 26 42 392 France 36 103 15 53 85 138 319 Germany 30 98 20 65 101 121 201 Italy 39 55 5 21 39 71 239 Netherlands 23 67 8 33 46 79 306 Spain 33 71 3 23 50 93 376 Sweden 27 49 3 31 40 62 132 Switzerland 20 64 13 40 56 88 153 United-Kingdom 97 108 14 52 92 127 436 Total 323 82 3 33 64 109 436

Index Country Base STI LTI Other BEL20 Belgium 27% 26% 38% 9% CAC40 France 23% 25% 49% 3% DAX Germany 22% 30% 35% 13% FTSE MIBItaly 36% 21% 38% 5% AEX Netherlands 24% 25% 39% 11% IBEX35 Spain 29% 25% 29% 18% OMX Sweden 46% 19% 17% 18% SSMI Switzerland 25% 25% 42% 7% FTSE100 United Kingdom 18% 20% 55% 7% Panel B:

Total CEO compensation Industry Obs. Mean Min P25 Median P75 Max Communication Services 24 4,257,509 936,000 1,863,500 3,203,161 5,564,766 12,100,000 Consumer Discretionary 46 5,599,589 1,096,332 3,013,139 5,326,000 7,354,117 13,300,000 Consumer Staples 27 6,440,117 902,000 2,869,101 5,569,473 9,060,000 13,800,000 Energy 11 5,085,869 1,145,000 1,437,000 4,462,000 8,909,000 11,300,000 Financials 70 4,048,077 750,000 1,936,679 3,329,391 5,354,000 12,800,000 Health Care 21 6,555,364 910,000 4,303,967 6,553,156 8,995,727 12,600,000 Industrials 50 4,363,534 163,000 2,628,469 4,195,436 5,448,841 9,717,972 Information Technology 14 4,582,495 2,709,000 3,215,375 3,686,751 4,767,000 14,000,000 Materials 34 4,377,468 411,000 2,359,082 4,532,570 5,769,048 10,100,000 Real Estate 8 4,257,919 647,000 3,498,382 4,659,855 5,220,439 6,659,000 Utilities 18 3,803,566 908,000 2,347,000 3,349,173 4,452,000 9,262,000 Total 323 4,781,146 163,000 2,347,000 4,359,000 6,553,156 14,000,000

Average employee compensation Industry Obs. Mean Min P25 Median P75 Max Communication Services 24 71,458 49,261 58,929 69,137 78,611 131,769 Consumer Discretionary 46 49,481 19,852 30,518 44,616 59,911 117,512 Consumer Staples 27 42,751 18,951 30,394 40,604 54,603 70,737 Energy 11 86,630 50,411 67,113 81,180 92,593 146,031 Financials 70 122,936 39,416 69,626 83,495 109,263 1,128,457 Health Care 21 93,257 31,613 62,522 89,538 101,075 272,389 Industrials 50 60,547 26,381 47,894 60,279 71,707 101,447 Information Technology 14 73,408 37,065 54,052 64,271 89,061 131,339 Materials 34 59,908 16,966 46,053 57,517 69,967 118,038 Real Estate 8 160,837 48,613 103,989 141,167 171,710 404,348 Utilities 18 74,241 31,599 52,015 66,036 80,902 245,961 Total 323 78,567 16,966 48,747 65,011 86,456 1,128,457

CEO pay ratio Industry Obs. Mean Min P25 Median P75 Max Communication Services 24 66 10 26 44 90 215 Consumer Discretionary 46 131 18 71 112 146 436 Consumer Staples 27 162 23 66 154 236 392 Energy 11 57 12 21 57 86 140 Financials 70 46 3 21 32 65 139 Health Care 21 89 9 48 76 118 285 Industrials 50 75 3 45 69 93 232 Information Technology 14 65 31 41 64 73 115 Materials 34 83 9 45 62 100 288 Real Estate 8 36 5 20 29 38 110 Utilities 18 63 17 28 54 73 233 Total 323 82 3 33 64 109 436

Industry Base STI LTI Other Communication Services 26% 23% 44% 7% Consumer Discretionary 23% 28% 44% 6% Consumer Staples 18% 24% 53% 6% Energy 22% 24% 49% 4% Financials 31% 16% 41% 12% Health Care 19% 25% 49% 7% Industrials 25% 26% 39% 10% Information Technology 26% 20% 44% 10% Materials 25% 28% 38% 9% Real Estate 22% 24% 46% 7% Utilities 26% 22% 38% 14%

Table C4 – Mean statistics

This table shows the results of a mean-difference test, in which we split the sample dataset into an above and below median CEO pay ratio (63.707). The test of difference shows whether variables have significantly different means when either in the high or the low CEO pay ratio sub sample. The statistical significance is reported for 0.1, 0.05 and 0.01 confidence levels, indicated by *, **, *** respectively.

Mean Test of difference Low CEO pay High CEO pay Variable Low-High ratio ratio CEOpayratio 34.269 131.019 -96.75*** CEOcompensation 2820000 6750000 -3930000*** Employeecompensation 99260.76 57744.79 41515.96*** AntiDirectorIndex 2.821 3.038 -0.217* ShareholderRightsIndex 19.79 20.522 -0.732 WGIscore 1.398 1.523 -0.126*** CorporateRate 23.982 23.962 0.018 PowerDistance 45.407 43.038 2.37* Individualism 73.802 76.752 -2.949** UncertaintyAvoidance 61.562 56.429 5.133** LongTermOrientation 60.994 60.342 0.652 Masculinity 48.389 56.124 -7.736*** Indulgence 56.05 57.013 -0.963 GINI 0.314 0.323 -0.009*** Unemployment 8.123 6.691 1.433*** Poverty 11.14 10.733 0.408 Unionization 30.343 21.547 8.796*** Ownership 22.455 15.182 7.273*** Innovation 7.945 12.073 -4.129*** B2C 0.481 0.584 -0.102* WorkforceScore 74.699 78.745 -4.046* CSRScore 61.138 67.645 -6.507** Employees 27176.93 94520.3 -67300*** Ln(Sales) 8.599 9.684 -1.085*** Leverage 5.767 3.545 2.222*** Growth 17.662 19.709 -2.047 ROA 0.051 0.053 -0.003 TSR 14.788 13.678 1.109 SD 20.206 19.394 0.813 Beta 0.969 0.99 -0.021 GDP 39699.45 39490.01 209.447 BoardSize 12 13.547 -1.547*** Gender 0.945 0.963 -0.018 Age 56.642 56.808 -0.166 Tenure 6.18 8.193 -2.014***

Table C5 – Pearson correlations

This table reports the Pearson correlations for the dependent and independent variables only. We have chosen to exclude the control variables from this table, as this would create unnecessary ambiguity. For an overview of all variables, we refer to Appendix B.

Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (1) CEOpayratio 1 (2) AntiDirectorIndex 0.101 1 (3) ShareholderRightsIndex 0.105 0.353 1 (4) WGIscore 0.111 -0.718 -0.478 1 (5) CorporateRate -0.027 0.325 -0.458 -0.211 1 (6) PowerDistance -0.055 0.525 -0.153 -0.548 0.718 1 (7) Individualism 0.157 -0.198 0.558 0.197 -0.504 -0.451 1 (8) UncertaintyAvoidance -0.113 0.409 -0.173 -0.544 0.759 0.884 -0.67 1 (9) LongTermOrientation -0.083 -0.352 -0.316 0.167 0.539 0.13 -0.211 0.427 1 (10) Masculinity 0.139 0.34 0.712 -0.269 -0.235 -0.057 0.304 0.05 0.122 1 (11) Indulgence 0.067 -0.656 -0.203 0.762 -0.585 -0.57 0.505 -0.778 -0.324 -0.342 1 (12) GINI 0.165 0.507 0.784 -0.318 -0.584 -0.194 0.328 -0.309 -0.737 0.53 -0.01 1 (13) Unemployment -0.138 0.593 -0.057 -0.696 0.316 0.671 -0.723 0.675 -0.29 -0.169 -0.591 0.157 1 (14) Poverty -0.066 0.587 0.57 -0.745 -0.23 0.13 -0.271 0.232 -0.418 0.361 -0.523 0.666 0.663 1 (15) Unionization -0.206 -0.289 -0.199 -0.066 -0.21 -0.22 0.126 -0.281 -0.03 -0.344 0.288 -0.288 -0.1 -0.022 1 (16) Ownership -0.033 0.276 0.1 -0.454 0.233 0.371 0.237 0.409 0.053 0.044 -0.406 0.002 0.397 0.317 0.028 1 (17) Innovation 0.136 0.018 0.055 -0.001 -0.053 -0.05 0.112 -0.098 -0.09 -0.019 0.057 0.056 -0.05 -0.024 0.014 -0.023 1 (18) B2C 0.165 0.132 0.067 -0.102 0.04 0.058 -0.026 0.047 -0.068 0.019 -0.093 0.085 0.092 0.095 -0.068 -0.088 -0.096 1 (19) WorkforceScore 0.059 0.103 -0.1 0.076 0.087 -0.019 -0.154 0.025 0.009 0.018 -0.076 0.019 0.038 -0.002 -0.174 0.143 -0.044 0.089 1 (20) CSRScore 0.112 -0.142 0.061 0.183 -0.152 -0.219 0.144 -0.193 0.01 0.068 0.147 0.054 -0.21 -0.082 -0.055 0.162 -0.01 0.083 0.348 1

APPENDIX D – REGRESSION RESULTS

Table D1 – Country variables only

This table reports the regression outcomes for country variables only. The dependent variable is the CEO pay ratio. Specification 1 to 3 independently regress the legal, cultural and social variables. Regression 4 and 5 include those variables that show signs of multicollinearity. Finally, regression 6 and 7 include all non-multicollinear/ relevant country-level variables (without and with industry fixed effects). The reported standard errors are Hubert-White robust standard errors.

Dependent variable: CEO pay ratio (1) (2) (3) (4) (5) (6) (7) AntiDirectorIndex 19.84*** (4.969) ShareholderRightsIndex 3.961*** -1.959 23.40*** -9.154*** -6.081** (1.001) (3.567) (8.681) (2.975) (2.528) WGIscore 67.26*** 93.43** (11.74) (39.68) CorporateRate 0.796 (0.809) PowerDistance 1.524 0.882 15.90*** -0.112 -0.233 (1.179) (0.640) (4.809) (0.392) (0.329) Individualism -0.503 1.700** -11.58*** 2.653*** 1.767** (0.582) (0.769) (4.068) (0.804) (0.763) UncertaintyAvoidance -1.367 -13.49*** (1.002) (3.913) LongTermOrientation 0.0548 8.771*** (0.571) (2.928) Masculinity 0.603*** -0.342 -0.525* 0.272 0.132 (0.183) (0.229) (0.311) (0.261) (0.204) Indulgence -0.184 -2.321*** 0.264 -0.861 -0.655 (0.429) (0.594) (0.627) (0.546) (0.487) GINI 532.1*** 1058.3*** 812.4*** 671.5*** (187.8) (236.2) (284.5) (228.6) Unemployment -0.978 (1.268) Poverty -3.666 (2.831) Unionization -0.568*** 0.279 -1.202*** -0.832*** -0.736*** (0.168) (0.520) (0.222) (0.243) (0.201) Constant -192.1*** 89.91 -48.18 -397.8*** 65.38 -130.1* -105.1* (42.48) (57.28) (45.54) (130.3) (72.41) (66.61) (53.72) Industry fixed-effects Yes Yes Yes Yes Yes No Yes Adjusted R-Square 0.309 0.259 0.299 0.301 0.301 0.087 0.296 N 323 323 323 323 323 323 323 Standard errors in parentheses * p<0.1 ** p<0.05 *** p<0.01

Table D2 – Country and firm variables

This table reports the regressions with our desired model specification (i.e. including all hypothesized variables) but excludes the multicollinear variables. The dependent variable is the CEO pay ratio. Regressions 1 to 4 include only firm level variables. Whereas specification 5 to 7 include all independent, control and fixed effect variables. The reported standard errors are Hubert-White robust standard errors.

Dependent variable: CEO pay ratio (1) (2) (3) (4) (5) (6) (7) Innovation 0.766*** 0.230 0.418 0.435* 0.640** 0.128 0.343 (0.283) (0.274) (0.263) (0.244) (0.280) (0.271) (0.238) B2C 24.64*** 6.989 -2.074 -1.662 22.69*** 5.318 -4.657 (7.610) (8.501) (7.987) (7.971) (7.553) (8.260) (7.586) WorkforceScore 0.0550 0.179 -0.0771 0.0289 -0.0391 0.0953 0.0374 (0.247) (0.233) (0.230) (0.231) (0.229) (0.220) (0.217) CSRScore 0.257 0.180 0.0218 0.00875 0.204 0.158 -0.0818 (0.161) (0.148) (0.123) (0.124) (0.160) (0.144) (0.128) Ownership -0.118 -0.183 -0.0891 -0.0512 0.196 0.0222 0.126 (0.229) (0.212) (0.195) (0.192) (0.259) (0.246) (0.214) ShareholderRightsIndex -9.346*** -5.909** -0.503 (3.194) (2.852) (2.885) PowerDistance -0.118 -0.138 0.0430 (0.383) (0.345) (0.328) Individualism 2.480*** 1.718** 0.482 (0.831) (0.798) (0.764) Masculinity 0.402 0.147 0.0534 (0.259) (0.224) (0.199) Indulgence -0.659 -0.609 0.430 (0.582) (0.519) (0.576) GINI 751.7** 656.4*** 483.7** (295.9) (249.2) (235.2) Unionization -0.816*** -0.689*** -0.255 (0.237) (0.205) (0.190) Employees 0.000360*** 0.000409*** 0.000413*** (0.0000528) (0.0000517) (0.0000556) Leverage 0.617 0.692 0.0991 (0.600) (0.623) (0.644) Growth 0.240* 0.322** 0.276* (0.131) (0.140) (0.151) ROA 15.04 -7.416 -43.25 (60.10) (59.03) (58.45) Beta 12.14 13.76 8.929 (9.620) (9.646) (9.520) BoardSize -2.354*** -0.801 (0.731) (0.792) Gender 8.111 9.859 (11.06) (11.08) Age -0.411 -0.0446 (0.492) (0.512) Tenure 0.909 0.512 (0.562) (0.568) Constant 43.18** 31.23 33.66* 56.91* -144.6** -130.1** -170.2** (17.93) (22.59) (19.17) (31.67) (67.57) (57.92) (67.22) Industry fixed-effects No Yes Yes Yes No Yes Yes Adjusted R-Square 0.047 0.242 0.411 0.428 0.121 0.291 0.472 N 323 323 323 323 323 323 323 Standard errors in parentheses * p<0.1 ** p<0.05 *** p<0.01

Table D3 – Integration of variables

This table reports the regression outcomes from the inclusion of integrated variables. In all regressions the dependent variable is the CEO pay ratio. All regressions have included the control variables and the industry fixed effects. The reported standard errors are Hubert-White robust standard errors.

Dependent variable: CEO pay ratio (1) (2) (3) (4) (5) ShareholderRightsIndex -0.177 -2.718 -0.493 -0.773 -0.470 (2.857) (2.838) (2.888) (2.968) (2.860) PowerDistance 20.93** 0.382 0.0587 0.0272 0.0593 (9.221) (0.364) (0.337) (0.336) (0.323) Individualism -1.046 0.631 0.482 0.582 0.463 (1.139) (0.728) (0.764) (0.782) (0.749) Masculinity -0.713** -0.255 0.0490 0.0445 0.0637 (0.345) (0.209) (0.199) (0.203) (0.197) Indulgence -0.726 -1.138 0.444 0.382 0.449 (0.648) (0.765) (0.583) (0.593) (0.565) GINI 3870.4*** 695.7*** 483.2** 343.6 478.2** (1469.9) (236.6) (235.0) (217.3) (230.2) Unionization -0.388** 0.460 -0.258 -0.275 -0.453 (0.188) (0.393) (0.190) (0.197) (0.652) Ownership 0.127 0.127 0.124 0.147 0.128 (0.212) (0.212) (0.213) (0.214) (0.215) Innovation 0.360 0.360 0.342 0.356 0.353 (0.237) (0.237) (0.239) (0.238) (0.240) B2C -4.677 -4.677 3.171 -103.2* -4.781 (7.639) (7.639) (16.62) (61.31) (7.567) WorkforceScore 0.0372 0.0372 0.0360 0.0350 -0.0438 (0.216) (0.216) (0.217) (0.215) (0.386) CSRScore -0.0761 -0.0761 -0.0109 -0.0755 -0.0835 (0.125) (0.125) (0.167) (0.127) (0.128) GINI*Powerdistance -73.25** (32.21) WGIscore*ShareholderRightsIndex 2.736** (1.203) B2C*CSR -0.129 (0.229) B2C*GINI 310.9 (198.0) Union*WorkforceScore 0.00267 (0.00761) Constant -925.1*** -207.3*** -174.0** -122.6* -164.3** (331.9) (66.11) (68.28) (72.76) (67.51) Firm- and Board controls Yes Yes Yes Yes Yes Industry fixed-effects Yes Yes Yes Yes Yes Adjusted R-Square 0.477 0.477 0.471 0.475 0.471 N 323 323 323 323 323 Standard errors in parentheses * p<0.1 ** p<0.05 *** p<0.01

Table D4 – Different measure of the CEO pay ratio (dependent variable)

In this table, the estimates are given from the regressions with different dependent variables. Column 1 and 2 show the results when the dependent variable, the natural logarithm of the CEO pay ratio is. Next, column 3 and 4 give the results with the CEO pay ratio (whilst excluding LTI compensation from CEO compensation) as dependent. Lastly, specification 5 and 6 give the estimate for the CEO pay ratio (without incentives) as dependent variable. All the three sets of regressions have with and without fixed industry effects regression results. The reported standard errors are Hubert-White robust standard errors.

CEO payratio (no CEO payratio (no CEO pay ratio (no CEO pay ratio (no Dependent variable: ln( CEOpayratio) ln( CEOpayratio) LTI) LTI) incentives) incentives) (1) (2) (3) (4) (5) (6) ShareholderRightsIndex -0.169*** -0.0761** -4.814*** 0.445 -2.364*** 0.293 (0.0402) (0.0346) (1.696) (1.666) (0.816) (0.826) PowerDistance -0.00887* -0.00749 -0.411* -0.318 -0.311*** -0.229** (0.00474) (0.00454) (0.213) (0.195) (0.0981) (0.0995) Individualism 0.0429*** 0.0224** 0.810 -0.435 0.234 -0.386 (0.0100) (0.00910) (0.509) (0.480) (0.271) (0.256) Masculinity 0.00508 0.00164 0.131 -0.0170 -0.0142 -0.0869* (0.00342) (0.00258) (0.133) (0.102) (0.0579) (0.0492) Indulgence -0.0197*** -0.0101 -0.477 0.185 -0.303* 0.0604 (0.00740) (0.00750) (0.330) (0.318) (0.156) (0.166) GINI 12.69*** 9.472*** 366.5** 159.6 251.5*** 142.8** (3.718) (2.893) (154.4) (127.3) (67.57) (56.57) Unionization -0.0136*** -0.00745*** -0.325** -0.00575 -0.0399 0.113** (0.00332) (0.00273) (0.132) (0.109) (0.0610) (0.0514) Ownership -0.00139 -0.00271 0.269 0.225 0.107 0.0893 (0.00291) (0.00239) (0.203) (0.180) (0.0965) (0.0851) Innovation 0.00907*** 0.00350 0.246 0.139 0.0560 0.0174 (0.00305) (0.00257) (0.158) (0.159) (0.0656) (0.0593) B2C 0.239*** 0.0563 8.835* -3.437 6.038*** 1.466 (0.0869) (0.113) (4.569) (4.467) (2.184) (2.216) WorkforceScore 0.00166 0.00277 0.0211 0.0715 0.0305 0.0470 (0.00336) (0.00280) (0.127) (0.124) (0.0647) (0.0598) CSRScore 0.00369* 0.000782 0.135 -0.0528 0.0926* 0.00386 (0.00202) (0.00194) (0.0968) (0.0808) (0.0509) (0.0465) Employees 0.00000363*** 0.000250*** 0.000117*** (0.000000656) (0.0000400) (0.0000206) Leverage 0.00194 0.233 0.104 (0.00970) (0.376) (0.189) Growth 0.00618*** 0.0584 0.111** (0.00220) (0.0955) (0.0461) ROA -1.348 -33.99 -18.42 (0.830) (35.14) (18.41) Beta 0.132 9.084 7.102** (0.129) (6.355) (3.237) BoardSize -0.0125 -0.703 -0.254 (0.0108) (0.594) (0.367) Gender -0.0366 -1.132 1.060 (0.177) (5.120) (3.146) Age -0.00109 -0.306 -0.182 (0.00707) (0.374) (0.189) Tenure 0.00643 0.363 0.243 (0.00678) (0.411) (0.226) Constant 1.248 1.322 -9.028 8.527 -4.113 -6.210 (0.808) (0.958) (38.14) (44.63) (18.33) (23.91) Industry fixed-effects No Yes No Yes No Yes Adjusted R-Square 0.225 0.488 0.049 0.386 0.073 0.354 N 323 323 323 323 323 323 Standard errors in parentheses * p<0.1 ** p<0.05 *** p<0.01

Table D5 – Less regulated industries versus heavily regulated industries

This table shows the results of the regressions when we split the dataset into heavily regulated (Financials and Utilities) and less regulated (Other) industries. Again, we used both the CEO pay ratio and its natural log as dependent variables. Column 1 to 4 do not include control variables, whereas column 5 to 6 do. The reported standard errors are Hubert-White robust standard errors.

Other industries Financials/Utilities Other industries Financials/Utilities Other industries Financials/Utilities Other industries Financials/Utilities Dependent variable: CEO pay ratio CEO pay ratio ln (CEO pay ratio) ln (CEO pay ratio) CEO pay ratio CEO pay ratio ln (CEO pay ratio) ln (CEO pay ratio) (1) (2) (3) (4) (5) (6) (7) (8) ShareholderRightsIndex -7.899** -2.364 -0.148*** -0.0816 -1.942 5.077 -0.101*** 0.0393 (3.756) (2.430) (0.0377) (0.0724) (3.539) (4.082) (0.0371) (0.0893) PowerDistance -0.449 0.442 -0.0154*** 0.00554 -0.0308 0.164 -0.0127** 0.000222 (0.437) (0.331) (0.00467) (0.00823) (0.436) (0.446) (0.00506) (0.00935) Individualism 2.632** -0.416 0.0445*** 0.00282 1.297 -1.917* 0.0338*** -0.0205 (1.032) (0.856) (0.0112) (0.0165) (0.948) (1.085) (0.0106) (0.0191) Masculinity 0.103 0.629*** 0.000281 0.0196*** 0.0478 0.568** 0.00000779 0.0170** (0.297) (0.228) (0.00314) (0.00704) (0.258) (0.224) (0.00268) (0.00647) Indulgence -0.991 0.453 -0.0252*** 0.00323 0.190 1.359** -0.0167* 0.0136 (0.692) (0.502) (0.00761) (0.0119) (0.771) (0.673) (0.00887) (0.0144) GINI 720.0** 540.7*** 11.06*** 10.06 529.1* 37.22 9.170*** 2.381 (330.5) (202.4) (3.366) (6.715) (294.1) (309.8) (3.104) (6.996) Unionization -0.850*** -0.0364 -0.0135*** -0.00211 -0.348 0.531* -0.00948*** 0.00809 (0.268) (0.202) (0.00305) (0.00629) (0.246) (0.282) (0.00288) (0.00634) Ownership 0.233 -0.388* -0.00124 -0.00876** 0.343 -0.356* -0.000427 -0.00844** (0.343) (0.210) (0.00331) (0.00393) (0.290) (0.205) (0.00290) (0.00361) Innovation 0.0900 -0.106 0.00290 -0.00734 0.334 0.0251 0.00418 -0.00587 (0.321) (0.175) (0.00281) (0.00665) (0.284) (0.162) (0.00258) (0.00681) B2C -6.466 25.61*** -0.138 0.725*** -16.58* 14.81* -0.225* 0.574*** (10.93) (7.323) (0.136) (0.184) (9.341) (8.231) (0.127) (0.198) WorkforceScore 0.149 -0.0454 0.00151 0.00430 0.0611 0.0554 0.00124 0.00695 (0.303) (0.167) (0.00309) (0.00406) (0.284) (0.181) (0.00286) (0.00425) CSRScore 0.116 0.207 0.00209 0.00220 -0.180 0.432** -0.000361 0.00399 (0.186) (0.152) (0.00211) (0.00359) (0.155) (0.216) (0.00206) (0.00430) Employees 0.000431*** 0.000290*** 0.00000340*** 0.00000590*** (0.0000628) (0.0000817) (0.000000632) (0.00000182) Leverage 1.751 -0.458 0.0250 -0.00901 (1.880) (0.612) (0.0201) (0.0115) Growth 0.344* -0.0582 0.00638** 0.00577 (0.184) (0.262) (0.00247) (0.00526) ROA -5.995 -40.50 -0.417 -0.543 (69.28) (93.57) (0.802) (2.389) Beta 12.34 -11.60 0.233 -0.289 (13.87) (10.39) (0.148) (0.230) BoardSize -1.028 0.466 -0.0123 -0.0123 (1.151) (1.069) (0.0130) (0.0206) Gender 12.63 -4.165 0.0113 -0.237 (14.22) (15.50) (0.202) (0.365) Age -0.192 -0.680 -0.00646 -0.00693 (0.640) (0.661) (0.00754) (0.0125) Tenure 0.284 3.036* 0.00512 0.0400* (0.605) (1.644) (0.00651) (0.0212) Constant -134.7* -142.8** 2.276*** -0.418 -189.7** -58.57 2.228** 1.275 (80.41) (60.99) (0.843) (1.590) (87.51) (86.44) (1.071) (1.812) Industry fixed-effects Yes Yes Yes Yes Yes Yes Yes Yes Adjusted R-Square 0.232 0.248 0.345 0.400 0.450 0.387 0.482 0.487 N 235 88 235 88 235 88 235 88 Standard errors in parentheses * p<0.1 ** p<0.05 *** p<0.01

APPENDIX E – ROBUSTNESS TESTS

Table E1 – Separate regressions on CEO compensation and average employee compensation

In this table, the results from the robustness test with respect to the expected signs based on CEO compensation and average employee compensation are shown. We performed two times two regressions (normal and log value of the dependent). After the regressions we included a column that shows the expected sign based on the outcomes from the two regressions. The reported standard errors are Hubert- White robust standard errors.

Average Total CEO employee ln( CEOcompens ln (Employeeco Dependent variable: compensation compensation Expected sign ation) mpensation) Expected sign (1) (2) (3) (4) ShareholderRightsIndex -39348.7 3780.9 Negative -0.0444 0.0317 Negative (139707.4) (2935.2) (0.0319) (0.0205) PowerDistance -23361.6 127.7 Negative -0.00835** -0.000859 Negative (14410.0) (504.3) (0.00392) (0.00302) Individualism 9000.6 -1351.9** Positive 0.0123 -0.0101** Positive (36044.7) (651.9) (0.00864) (0.00513) Masculinity 27960.0*** 493.1** Positive 0.00598** 0.00434*** Positive (10634.3) (215.1) (0.00241) (0.00150) Indulgence 20795.2 1065.2 Positive -0.00207 0.00799* Negative (27913.0) (798.7) (0.00698) (0.00465) GINI 3014011.8 -575864.3** Positive 2.338 -7.134*** Positive (11326693.9) (255328.3) (2.657) (1.728) Unionization -29231.6*** 105.2 Negative -0.00877*** -0.00132 Negative (9573.0) (241.9) (0.00251) (0.00156) Ownership -11999.8 39.44 Negative -0.00439** -0.00169 Negative (8438.1) (175.7) (0.00220) (0.00151) Innovation 28569.4** 324.4 Positive 0.00552* 0.00203 Positive (12453.7) (274.6) (0.00306) (0.00220) B2C -398351.7 -41586.5** Negative -0.120 -0.176** Negative (355549.2) (17402.7) (0.0957) (0.0752) WorkforceScore 938.0 -730.1* Positive 0.000613 -0.00215 Positive (9252.0) (405.7) (0.00235) (0.00178) CSRScore 10316.4 36.06 Positive 0.00238 0.00159 Positive (6587.5) (206.4) (0.00173) (0.00113) Constant 1302402.7 341183.2** 14.79*** 13.47*** (3996577.5) (133222.6) (0.878) (0.626) Firm- and board controls Yes Yes Yes Yes Industry fixed-effects Yes Yes Yes Yes Adjusted R-Square 0.335 0.221 0.327 0.446 N 323 323 323 323 Standard errors in parentheses * p<0.1 ** p<0.05 *** p<0.01

Table E2 – Other control variables

In this table, we report the outcomes of including additional controls into our regression. Each column has an additional control variable, where we started with the most reasonable variable (ln(Sales) as proxy for size) and ended with GDP, which is known to be likely multicollinear related. However, for the sake of completeness we do report the outcome here as well. The reported standard errors are Hubert-White robust standard errors.

Dependent variable: CEO pay ratio (1) (2) (3) (4) ShareholderRightsIndex 0.372 0.375 0.361 3.932 (2.870) (2.877) (2.883) (3.306) PowerDistance 0.157 0.171 0.185 -1.238** (0.322) (0.320) (0.319) (0.542) Individualism 0.184 0.160 0.146 -1.612 (0.765) (0.765) (0.763) (1.097) Masculinity 0.0795 0.0666 0.0797 0.965** (0.193) (0.193) (0.194) (0.401) Indulgence 0.589 0.611 0.640 2.209** (0.566) (0.568) (0.571) (0.881) GINI 485.7** 508.2** 502.1** -905.3 (229.4) (236.0) (234.2) (578.5) Unionization -0.0618 -0.0327 -0.0353 -0.602** (0.202) (0.197) (0.199) (0.239) Ownership 0.141 0.151 0.148 0.154 (0.211) (0.210) (0.208) (0.205) Innovation 0.385 0.375 0.386 0.405* (0.240) (0.245) (0.246) (0.243) B2C -8.546 -9.689 -9.694 -10.29 (7.805) (7.669) (7.679) (7.708) WorkforceScore 0.0125 0.0150 0.0217 0.0162 (0.218) (0.213) (0.212) (0.210) CSRScore -0.158 -0.161 -0.162 -0.162 (0.125) (0.125) (0.125) (0.120) Employees 0.000347*** 0.000348*** 0.000349*** 0.000343*** (0.0000511) (0.0000522) (0.0000525) (0.0000530) Leverage -0.188 -0.269 -0.282 -0.311 (0.640) (0.633) (0.627) (0.635) Growth 0.241* 0.237* 0.241* 0.258* (0.142) (0.143) (0.143) (0.150) ROA -8.556 -16.95 -11.30 -22.05 (55.70) (57.30) (61.13) (61.24) Beta 4.360 5.158 3.931 8.583 (9.501) (9.749) (9.918) (10.56) BoardSize -1.253 -1.254 -1.238 -2.583** (0.805) (0.809) (0.804) (1.015) Gender 14.64 14.49 14.64 15.96 (9.989) (10.26) (10.29) (10.11) Age -0.114 -0.0715 -0.0654 0.0322 (0.497) (0.499) (0.501) (0.498) Tenure 0.519 0.497 0.495 0.419 (0.575) (0.586) (0.588) (0.574) logSales 10.83*** 11.46*** 11.59*** 12.62*** (3.219) (3.229) (3.249) (3.322) TSR 0.201 0.200 0.238 (0.207) (0.207) (0.205) SD 0.188 0.0902 (0.498) (0.496) GDP -0.00597*** (0.00209) Constant -273.6*** -287.9*** -292.6*** 393.2 (67.97) (72.52) (73.64) (244.7) Industry fixed-effects Yes Yes Yes Yes Adjusted R-Square 0.494 0.494 0.492 0.502 N 323 323 323 323 Standard errors in parentheses * p<0.1 ** p<0.05 *** p<0.01

Table E3 – The endogeneity problem

In this table, we report the outcomes of the 2SLS instrumental variables regressions. Both regressions have the CEO pay ratio as the dependent variable. Column 1 has Unionization as predicted by the Inflation. Column 2 includes ROA as predicted by Ln(Cash), which is the natural logarithm of the total of all cash and cash equivalents (e.g. short-term investments) a company has. The reported standard errors are Hubert- White robust standard errors.

Dependent variable: CEO pay ratio (1) (2) ShareholderRightsIndex -0.310 -4.622 (2.841) (3.420) PowerDistance 0.365 -0.551 (0.355) (0.431) Individualism 0.239 1.242 (0.761) (0.808) Masculinity 0.216 0.115 (0.226) (0.239) Indulgence 0.541 -0.164 (0.560) (0.628) GINI 609.2*** 860.7*** (229.3) (294.6) Unionization (predicted) 0.569 -0.335 (0.432) (0.238) Ownership 0.0399 0.179 (0.209) (0.241) Innovation 0.362 0.608* (0.226) (0.332) B2C -4.525 -8.172 (7.471) (9.382) WorkforceScore 0.0722 0.253 (0.215) (0.311) CSRScore -0.0681 -0.146 (0.125) (0.175) Employees 0.000438*** 0.000347*** (0.0000577) (0.0000608) Leverage 0.271 -2.477* (0.632) (1.430) Growth 0.326** 0.452 (0.148) (0.337) ROA (predicted) -36.79 -869.4** (57.24) (411.3) Beta 8.010 -2.082 (9.119) (13.56) BoardSize -0.332 -1.747* (0.760) (1.045) Gender 5.166 19.70 (11.15) (13.37) Age -0.0303 -0.162 (0.497) (0.576) Tenure 0.751 0.823 (0.571) (0.663) Constant -249.4*** -174.7** (71.02) (75.29) Industry fixed-effects Yes Yes Adjusted R-Square 0.447 0.158 N 323 323 Standard errors in parentheses * p<0.1 ** p<0.05 *** p<0.01