Performance of Young Public Firms Managerial vs Outside Shareholder Control

in an international context

/ Daniel Heinz Klaus Heinrich Richard Walter Bogdanski

/ 900912-T352

/ Double Degree MSc International Financial Management / Business and Economics

/ Faculty of Economics and Business

/ Rijksuniversiteit Groningen and Uppsala Universitet

/ Supervised by Dr. Wim Westerman

/ Co-Assessed by Dr. Halìt Gönenç

Abstract This paper studies the relationship between firm performance, proxied by Tobin's Q, and two distinct

ownership types, managerial owned firms and outside owned firms. The sample consists of 2005 young

firms from Europe and the US that incorporated since the dot­com­bubble 2001. Very similar to the

pre­2001 period, young and highly funded firms are of popular concern. In particular their owners,

founders and CEOs are topic of interest and serve as figurehead for their company, raising the question

whether their firms perform better if they also own them or not and whether that differs with the

institutional framework that the company is situated in. Thus the research question is the following: What

is the effect of having management as majority shareholder(s) on the performance of the young firm in

different environments? To find an answer, I used quantitative data from Orbis and analyzed it using

time­series panel data, recent information using simple OLS as well as multiple analyses of variance. I

find evidence of higher valuations of firms owned by managers, especially in countries with common law

and stronger shareholder rights. I also find evidence of relatively lower valuations of firms owned by their

managers when these are situated in code law countries or countries with stronger creditor rights. A

surprising addition to the findings were extreme values of Tobin's Q that may indicate another bubble in

the making, coincidentally closing the circle of this study.

Key words: International, Financial, Management, Ownership, Firm Performance

JEL Classification: G32, L25, L26

Table of contents

1 Introduction 3

2 Literature Review 7 2.1 Triggering Agency Costs ­ CEO Turnover decisions 7 2.2 Managerial ownership and firm performance 8 2.3 Family firms and its comparability to managerial ownership 9 2.4 Implicit forces on managers, founders, family­ and widely­held­firms 9 2.5 Entrenchment as performance inhibitor for not­owners 11 2.6 Regional differences and investor protection 12

3 Data and Methods 14 3.1 Data collection process 14 3.2 Treatment of missing data 15 3.3 Treatment of outliers 16 3.4 Empirical methodology 16 3.5 Dependent Variable ­ Tobin’s Q and LN Tobin’s Q 17 3.6 Relevant Independent Variables 17 3.7 Control Variables 18 3.8 Variance analysis models 18 3.9 Log­Log regression models 18 3.10 Random Effects model 19

4 Hypotheses and Empirical Models 20 4.1 Null Hypothesis 20 4.2 Ownership Difference and Direction Model and Hypotheses 21 4.3 International Perspective 22

4.3.1 Ownership Country Difference and Direction Model and Hypotheses 23 4.3.2 Legal Origin Models and Hypothesis 24 4.3.3 Creditor and Shareholder Rights Models and Hypothesis 25

5 Empirical results 27 5.1 Overlap US and Europe for Homogeneity of Variance and Normality 27 5.2 US Firms 28 5.3 European firms 29 5.4 Implication US and European individual tests 30 5.5 Using full sample 31

5.5.1 Basic models (6) + (7) 33

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5.5.2 Legal Origin Models (8) ­ (10) 33 5.5.3 Investor Rights Models (11) ­ (13) 35

5.6 Robustness and sensitivity tests 36

6 Conclusion and results 38

7 References 41

8 Appendix­A 47

9 Appendix­B 50

10 Appendix­C 60

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1 Introduction

It is commonly accepted that managers are largely determining firm performance, but just as

much that firms are largely determining this manager's job safety. These circumstances create uncertainty

potentially leading the managers to alter their behavior. “Unfriend: What drove Zuck to fire Saverin”

(CNET, 2012), “How Elon Musk Fired Tesla CEO and co­founder” (Business Insider, 2014) or the

famous “Apple CEO John Sculley fires Steve Jobs” (Fortune, 1985) were some of the most famous

headlines in past years and history. Those headlines and their background suggest that it is not exclusively

performance that drives founders and managers out of their firms. Usually those stories start with

“[Investor] Carl Icahn wants Yahoo CEO fired” (Fortune, 2008), followed by “Icahn Ramps Up Pressure,

Vows To Get Jerry Yang Fired” (Business Insider, 2008) to end with “Jerry Yang Resigns From Yahoo,

the Company He Founded” (WSJ, 2012), suggesting the power of controlling shareholders and implying

forces other than performance. The question is so current and attracting so much attention, that US TV

network HBO dedicated a whole award winning TV Series towards this topic, calling it “Silicon Valley”

famously quoting “take the money or keep the company”. This raises the question: what is it actually that

lets founders of young companies get into a position of facing to be ousted out of their own company? Do

firms perform differently when separation of ownership and control does not exist?

Recent work in top journals took it upon them to investigate this subject: CEO turnover and their

determinants. Research of Jenter and Kanaan (2015) suggests precisely the issue implied, turnover

decisions irrespective of the pure nature of firm performance. Pointing out an issue that managers mostly

sense one way or another, brings about frictions firms not necessarily desire. Looking at older research of

Morck, Shleifer and Vishny (1989), they actually suggest that industry performance is filtered out of

dismissal decisions, which additionally triggers the question whether this is a phenomenon that only

applies to younger firms.

In order to grow their company, founders have to make crucial decisions in very early stages.

Capital structures will haunt founders until the end of the days, because they imply control issues. Some

founders therefore choose to base their financing decisions around control, whereas others choose to

neglect this issue and first grow the company.

In their famous work, Modigliani and Miller (1958) describe the irrelevance theorem of capital

structure for a firm, yet under assumptions which may not hold regularly anymore, in particular for young

firms. Most importantly today, these assumptions ignore problems triggered by an individual in charge

who wants to protect himself, as well as the issue of cash flows regularity, which represent the securities

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for the investor. Hart (1995) describes the feature of securities to be not only limited to cash flows, but

also includes other rights that these securities give access to, amongst others the right to vote for directors.

These rights become critical to acting managers, because their behavior is determined by the people who

control their job. In the end, dividends are paid because shareholders control the directors and creditors

are paid because otherwise they can demand their collateral (La Porta et al., 1998).

News coverage around young firms and startups picked up increasingly and developed into a

trend. Many founders want to be part of the sharing­economy, be the new Uber, Facebook, Google or

AirBnB. The impact of young firms and the trend around it has been growing, fueled by venture capitalist

and early series funding of high potential firms, even without revenues or tangible assets. While those

crucial decisions around financing in young firms mostly happen in the period before public offering, the

capital structure has to be carried with them during the process of going public. The longer the process

takes, the more are shares of managing owners and founders watered down. With that in mind, looking at

venture funding betting on exponential growth of cash­flows, Modigliani and Miller’s findings do not

hold anymore, triggering a new problem of being a young firm, disproportional cost of debt.

“Pre­Revenue” companies with billion dollar valuations (so­called “unicorns”) are not the exception

anymore. Even in 1976, Jensen and Meckling already argued that they believe ”the existence of agency

costs provide stronger reasons for arguing that the probability distribution of future cash flows is not

independent of the capital or ownership structure”, leading to agency costs.

The question I ask here is whether it is really a clever decision to give away shares and ownership

early, to fuel fast growth or create more sustainable and potentially flat growth, risking to take too much

time, but be part of the decision making process when the own firm is in the position that the founder

always dreamed about. And how does that differ in between regions? If there are differences, what are

those? La Porta et al. (1998) argue that the intrinsic characteristics inherent to the securities of investors is

not equal across the world. According to their findings, capital structure differs around the world because

of differences in enforcement. This raises the question: Do the findings of La Porta et al. still hold for

determinants emanating from country regulatory differences and are they applicable to young firms?

The story of the Yahoo­CEO or the co­founders of Apple, Facebook and Tesla can also go the

opposite way. If we take a look at the now five biggest firms in the world, according to market

capitalization, most people will associate some type of founder or entrepreneur with the companies; Apple

with Steve Jobs, Microsoft with Bill Gates, Berkshire Hathaway with Warren Buffett, Amazon with Jeff

Bezos or Alphabet, now the second biggest company in the world (Financial Times, 2016), who is still led

by their original founders, Larry Page and Sergey Brin, and have achieved to keep 52.5% of the voting

rights to the firm that is most commonly known as Google (Alphabet, 2016). Since these examples are

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just anecdotal evidence to what motivates founders and how they keep track of their ownership and

exercising voting rights, I want to dive into a more generalized view by including the full sample of

young companies that emerged after the dot­com bubble in the most developed regions in the world, the

US and Europe. Is it really worth passing on early money, just to bootstrap the way up to glory?

“Lots of companies don’t succeed over time. What do they fundamentally do wrong?

They usually miss the future. I try to focus on that: What is the future really going to

be? And how do we create it? And how do we power our organization to really focus on

that and really drive it at a high rate?” ­ Larry Page, CEO Google (FT, 2014) on long

term orientation, intrinsic motivation and the resulting inner power of founders who

are free to chase their dreams within their own company.

This study revolves around the key requirements set out by the master degree that it is intended to

complete. It follows the papers of Jenter and Kanaan (2015), La Porta et al. (1998 and 2001) as well as

large parts of the methodology and structure of Gönenç and Scholtens (2017). The papers employed serve

individual purposes that are merged within this paper. While Jenter and Kanaan (2015) provide insights

into the determinants of CEO turnover based on the performance of the firm the CEO manages, the papers

of La Porta et al. (1998 and 2001) provide the international fundament that seeks to explain differences

and/or similarities between the regions of the United States and Europe. Those papers introduce the

variables of investor rights and legal origin.

The main research question I thus define as follows: What is the effect of having

management as majority shareholder(s) on the performance of the young firm in

different environments?

I found support for the notion that there are differences in firm performance between firms owned

by managers and those who are not owned by managers (H1), leading to findings that managerial

ownership leads to better firm performance (H2). Regarding the regional differences, there are differences

between managerial owned firm performances in the US and Europe (H3), leading to findings that

managerial ownership effects are stronger for US firms than European firms (H4). Stronger and more

important was the finding that managerial ownership firm performance was positively affected by being

located in common law countries and negatively affected by being located in civil law countries,

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confirming H5. Surprisingly, I found results partly opposing H6, namely negative effects of creditor rights

on firm performance, even though they are rather low and weak. On the other hand, stronger shareholder

rights have a positive effect on firm performance, thus also just partly confirming H6 and providing

support to the findings of La Porta et al. (2001). This study contributes to the literature by giving insights

into effects of who owns companies and how this is in turn affected by the institutional framework, taking

the general findings of La Porta et al. (1997, 1998, 1999a, 1999b, 2001 and 2008) to a further detailed

level. I moreover confirm most of these findings and provide food for thought regarding the current

situation in market valuation for a very specific type of firms, young firms.

To the best of my knowledge there has been no study trying to find out whether firms perform

better if they are owned by their managers and whether that differs with the institutional framework that

the company is situated in. This is the gap this study is supposed to fill with a fresh dataset of companies

founded since 2001, while focusing on publicly listed firms from the US, Europe and together, to test

effects in between. In order to investigate the research question, I make use of analyses of variance as

defined by Mann and Whitney as well as multiple types of regression analyses and panel data to dig

deeper into the effects of individual variables as well as investigate the effects of other variables affecting

the findings.

This study is organized as follows: Section one of this paper starts with an introduction into the

field, its relevance and current topics. Section two provides the review of existing and relevant literature

while weighing off topics that may influence or not, which then leads to hypothesis creation and

communication of decisions what factors apply and are taken into account. Section three describes the

data used, how the sample was selected and how variables are defined, which leads to section four,

main­presentation of empirical results. In section five, multiple additional tests are performed to test

robustness and includes multiple tests of potentially explaining independent variables.

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2 Literature Review

Underlying this research is the agent, as in the manager(s), being the performance determinant of

firms. Their impression of long­term ability to strategize and build their ideas depends on job safety,

which I argue to create costs for non­owners, while owners of firms can focus on the job instead of

building safety nets, leading to higher performance in firms owned by its managers. The overall approach

takes strategies by other papers that I then apply to the topic of this paper. To avoid excessive repetition in

literature review and hypothesis argumentation, this section focuses on the broad picture of available

literature and outlines the factors that are relevant for this study per topic by weighing off findings and

giving a wider impression. Specific findings including their hypothesized direction of impact applied to

firm performance will mostly be laid out in section four “Hypotheses and Empirical Models”, which

partly requires assembling multiple topics for the argument, hence requires to be separated from this

literature review. Following literature builds the foundation of this paper:

2.1 Triggering Agency Costs ­ CEO Turnover decisions

The decision to retain or fire a CEO is often outside the control of managers themselves and that

is because of firm performance outside managerial control (Jenter and Kanaan, 2015), where managers

get blamed for exogenous shocks on performance they can not control. This blame may come from

outside pressure such as shareholders (Fisman, Khurana, and Rhodes­Kropf, 2014), or the inside, whereas

boards appear to be filtering out market shocks (Gibbons and Murphy, 1990; Kaplan and Minton, 2012).

Other studies generally find that turnover decisions are filtered out by market shocks (Barro and Barro,

1990; Morck, Shleifer, and Vishny, 1989), yet not in those with older samples (Warner, Watts, and Wruck,

1988).

Research on corporate governance clearly points to improving corporate governance mechanisms

since the 70s, which links to the aforementioned ability to make CEO turnover decisions and filtering out

market shocks (Huson, Parrino, and Starks, 2001; Holmström and Kaplan, 2001; Hermalin, 2005).

Bebchuk and Fried (2004) on the other hand found that corporate governance since the 70s has been

worsening, which led Jenter and Kanaan (2015) to argue that if that is the case, CEOs were the ones that

hide behind rather strong industry performance even though they were underperforming themselves. In a

recent study, De Cesari, Gönenç and Ozkan (2016) found no significant effect of acquisitions on CEO

turnover. Furthermore, other non­performance related issues may cause turnover, arguing that

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performance and turnover are potentially related, but one does not necessarily cause the other (Comte and

Milhal, 1990). In the light of these circumstances, uncertainty created by conditions out of managerial

power may inhibit managerial performance and lead to agency problems originating from the

circumstances and self­protection.

Interestingly, most literature on CEO turnover does not provide pay­for­performance as

determinant for turnover, namely rather selecting the “optimal” person (Jovanovic, 1979), a type of match

between the candidate and firm (Jenter and Kanaan, 2015). Most literature in the 90s does not focus or

provide good explanations for turnover in good times (e.g Aggarwal and Samwick, 1999; Murphy, 1999).

Under these circumstances CEO compensation surprisingly does not influence the argumentation of this

paper, excluding the paper of Casamatta and Guembel (2010). They argue that legacy potential

significantly determines CEO compensation, strategy and turnover Such legacies then increase

replacement costs and thus strengthen certainty of managers, long term ideas and implicitly then

strengthen their position, leading to similar effects argued to be an important performance determinant of

managerial owners. Legacies are, however, not the norm. This rather provides reason to believe that

family­firms play an important role in describing ownership effects on firm performance.

2.2 Managerial ownership and firm performance

Relevant studies regarding managerial ownership and firm performance find a curvilinear

relationship of firm performance and Tobin’s Q (Barnhart and Rosenstein, 1998; Morck, Shleifer, and

Vishny, 1988; McConnell and Servaes, 1990). Stulz (1988) found that an increase in managerial voting

rights leads to an increase in premium offered for takeovers, increasing shareholders wealth. Furthermore,

Morck, Shleifer, and Vishny (1988) find Tobin’s Q to be increasing with larger ownership of board of

directors, most importantly over the 25% threshold. Chen and Yu (2012) go further to base on Shleifer &

Vishny’s findings (1994) for the relevance of ownership structure, that this is precisely what determines

agency problems. Fama and Jensen (1983) argued that ownership of management in firms is

counterproductive due to inability to supervise, which Morck, Shleifer, and Vishny (1988) refute. They

also specifically point out the decrease in Tobin’s Q when the firm is run by a founding family member

compared to outsider, yet only for older firms and second generation family firms. This is of vital

importance for the following section and the line that this paper draws between family­firms, managerial

owners and the relevance of findings in family­firm research.

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2.3 Family firms and its comparability to managerial ownership

Research on family firms is widely available (Anderson and Reeb, 2003; Miller et al, 2007;

Villalonga and Amit, 2006) and I argue that family firms are very similar to firms owned by the

manager(s), much like Chen and Yu (2012). While there is no commonly accepted consensus as to what

precisely constitutes a “family­firm” (e.g. Kraiczy, 2013; Westhead and Cowling, 1998), two common

uses emerged. Firstly firms with family CEO as successor in at least the second generation (Bennedsen et

al., 2007) and secondly those with family ownership (Anderson and Reeb, 2003; Cronqvist and Nilsson,

2003; Miller, 2013; Villalonga and Amit 2006), whereas Anderson and Reeb (2003) and Villalonga and

Amit (2006) go as far as defining a family firm as those with members of founders’ family or the founder

himself in top management or as major shareholder, without specifying a threshold. This description

builds the foundation for comparing managerial owners to family firms, as such a first generation family

firm. Reversing this argument, managerial ownership would be defined as a family firm. Thus, I argue

family­CEOs are similar or equal to founders and others managerial owners. In that case, characteristics

of family­firms are arguably features of managerial owned firms as well.

Benefits of family firms are lower agency cost, reducing those stemming from the separation of

control and ownership (Jensen and Meckling, 1976), and the principle of making long­term decisions

(Gómez­Mejía et al., 2011). The authors found this to be especially significant in firms with concentrated

ownership, which provides the opportunity to push the above mentioned ideas through that underlie

family­firms. Additionally, family­CEOs may behave altruistically with their family in mind (Schulze,

Lubatkin and Dino, 2002) and controlling families are not found to expropriate wealth from minority

shareholders (Croci, Gönenç and Ozkan, 2012), substantiating the similarities between family firms and

managerial owners who are thus interested in firm performance.

These benefits were found to affect firm performance in those family­CEO family­firms

positively (Anderson and Reeb, 2003; Morck, Shleifer and Vishny, 1988), namely in S&P 500 and

Fortune 500 firms, respectively. Morck, Shleifer and Vishny (1988) also find rising Tobin’s Q with rising

board ownership from a threshold of 25% (which are blocking rights).

2.4 Implicit forces on managers, founders, family­ and widely­held­firms

This line of argumentation leads to stewardship theory and the contrast to agency theory,

depending on who owns the company. Agency theory states that managerial actions may differ from the

optimum that maximizes firm performance for principals and instead agents act in their own interest,

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(Berle and Means 1932; Pratt and Zeckhauser 1985). Berle and Means (1932) were the ones who set the

hare running explaining that when the equity held by managers is low and shareholders dispersed then

they were unable to enforce maximization of value, because control is concentrated in the hands of

managers. Jensen and Meckling (1976) then further developed this theory into the principal­agent theory

that constitutes the basis of the now known problem. According to them, it is in the interest of the both

principal and agent to maximize their own interest instead of the others and thus an agent in the form of a

manager does not always work towards the interest of the principals. The work a manager does is then

influenced by his or her own perception of what is the best to save the job. Thus, according to Adams and

Ferreira (2009), shareholders prefer risky project to increase wealth, whereas managers are trying to

reduce taking risks that could put them in a bad position.

Stewardship theory differs in the structure of control. While agency theory underlies a separation

of board and CEO, who is put on track by incentive schemes set out by the board, this separation does not

exist in stewardship theory, putting the CEO in full charge, acting as “steward” of the firm. Donaldson

and Davis (1991) find significant improvements in ROE of firms that have combined chair and CEO

positions, compared to companies with separated ones. This separation exists in the same sense when

firms are owned by their managers instead of widely held.

On the other side this effect of stewardship may be weaker depending on institutional context,

moving to the downsides of family ownership, much like managerial owners could. Even though La Porta

et al. (1999 and 2000) did not explicitly mention the idea, I would like to use these papers to show in what

direction I am pointing here, taking findings of legal origin and the investor rights effects to demonstrate

performance effects.

Johnson et al (2000) argue that family shareholders who are in control, may extract private

benefits to adversely affect small shareholders. Under these circumstances agency cost come into effect,

which emanate from controlling ownership, a case that applies to this study as well. Additionally, Barth et

al (2005) find a negative relationship of family CEOs on firm productivity, the results thus stand in

contrast to the findings of Anderson and Reeb (2003). This finding needs to be emphasized, because their

main determinant of productivity is not family­firms per se, but their results mainly refer to family firms

led by family managers, who are less productive than non­family managers. The authors argue that

implementing managers from within the family simply limits the pool of potential candidates with talent

to a very small amount. Their findings point to potential agency problems within firms, more specifically

nepotism. Additionally, Bandiera et al (2014) find that family CEOs work fewer hours than professional

CEOs in family firms, whereas these findings may strongly relate to strong governance mechanisms in

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family firms, as Lin and Hu (2007) find. Within this research, it does, however, speak in favor of firms

owned by its managers, because they are the primary corporate governance mechanism as majority owner.

The above mentioned agency problems may, however, also be prevented by controlling

shareholders other than managers as controlling shareholders themselves. Research of Shleifer and Vishny

(1997) as well as La Porta et al. (1998) suggest that such controlling shareholders are able to implement

better controlling mechanisms to balance out governance issues to avoid bad decisions of the firm, much

like the governance mechanisms described in Lin and Hu (2007), emanating from families as controlling

shareholders. Different to agency in families who do not expropriate from the firm (Croci, Gönenç and

Ozkan, 2012) in this case the authors write that it may well be that controlling shareholders act against

other shareholders of a firm. These findings may emphasize again the differences in issues related to

control and (long­term) motivation of the controller.

Considering the overall picture that I use to proxy family firms for managerial ownership

performance, the relevant points made are those originating from the overall performance determinants of

ownership by the managers. Firstly, control issues leading to uncertainty avoidance. Secondly age, namely

founders and not second­generation or later stage family firm managers. Those aspects firm up the basis

of this study relating to family­firms.

2.5 Entrenchment as performance inhibitor for not­owners

In case managers are not rooted deeply enough within a company, managers have the ability to

control their actions to make themselves more valuable to shareholders or more difficult to replace at the

cost of shareholders. This theory of entrenchment of managers, developed by Shleifer and Vishny (1989)

builds up the opposing side of altruistic founders in future family firms and stewardship principles. It

emphasizes the diversion of a manager’s most rare resources, time and funding, into saving and protecting

oneself instead of working towards the ultimate goal of overall firm performance. Those managers could

for example acquire a target that only has value or has increased value under the specific manager, or

undervalue resulting from risk preference incongruity. Some executives exploit their position by

foregoing risky­projects to set their seat in concrete (Fama, 1980; Lambert, 1986) or by looking beyond

the horizon and prevent failures to save their face, reputation and career (Holmström & Costa, 1986;

Smith & Watts, 1992; Aggarwal & Samwick, 2006) Yim (2013) shows that those actions decrease the risk

of replacement. Fich et al (2014) find supporting results. Jensen (1986) also describes empire building of

CEOs for private benefits, which may apply to both groups used in this study. According to Shleifer and

Vishny (1989), these actions may not only hinder growth and performance, but actively destroy value,

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solely due to uncertainty and fear of replacement that does not exist if the manager is at the same time the

owner of the firm. Emphasizing the role of this study, Allgood and Farrell (2000) specifically find CEOs,

who have not been founder, to engage in entrenchment, especially their intermediate years. Additionally,

their study shows weakening effects over time, which in turn expressly underlines the importance of a

safe position and manager concerns of uncertainty.

2.6 Regional differences and investor protection

Different ownership structures are commonly found in between the regions of the US and Europe,

for example family control is comparatively dominant in continental Europe compared to the United

States (Faccio and Lang, 2002). Multiple papers of La Porta et al. (1998, 1999a, 1999b, 2000, 2001 and

2008) go into more detail of how individual companies finance themselves, what capital structures they

are using and what determinants are responsible for these outcomes.

La Porta et al. (1998) found strong differences between the legal background of countries and

their legal layout of investor protection. The separation is found to be especially strong between common

and civil law countries, with weakest investor protection in countries with French legal origin and

strongest in those countries whose legal system originates from traditions of the United Kingdom. More

specifically, they found differences in ownership structures depending on the ability of an investor to

enforce their rights. Two types of protection emerged, the first being creditor rights, the second

shareholder rights.

Shareholder rights are measured by the authors as the so­called “anti­director­rights”, which

provides an indicator of the ability of minority shareholders to take part in the decision making process

and how the law protects these rights to vote. The index is made up of six dummy variables providing a

range of values from 0 to 6, the latter being the strongest, representing individual rights that the investor

may use. One­share one­vote practices are not relevant in this study due to the countries in the sample.

Next to shareholder rights, the papers of La Porta et al. explain creditor rights, which is again a

number of (five) dummy variables representing a total value, in this case ranging the outcomes from 0 to

4. The difference between the two lies, as mentioned in the introduction, in the security of the investor.

For creditors the security is collateral in the firm's assets and for shareholders their voting rights, whereas

there are more different types of creditors with largely varying aims. The essence is, however, that

creditor security is based around the collateral claim. Making reclaiming of collateral in default

comparably difficult, would disincentivize an investor to invest (La Porta et al., 1998) .

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Therefore, investor protection plays a crucial role for firm valuation as well (La Porta et al.,

2001), where a weak protection of shareholders is being penalized by those with lower valuations. The

glue for the aforementioned papers is the quality of government (La Porta et al., 1999b), which enforces

these investor rights. Looking at differences in enforcement for creditor and shareholder rights then

explains differences of capital structures in­between countries. This leads to La Porta et al. (1999a), who

partly oppose the picture that Berle and Means (1932) painted of separation of control and widely held

ownership, finding relatively few firms which are widely held outside the United Kingdom and the United

States. To give another example, Edwards and Fischer (1994) find Germany to have strong banks, but a

weak stock market, which is supported by the findings of La Porta et al. (1998) rating Germany with

relatively strong creditor rights and relatively weak shareholder rights, leading to relatively more debt

financing versus equity financing for German firms.

The key takeaway of this section is the divergence of incentives in each country emanating from

the law and enforcement within its borders, which spills over to the market valuation of firms governed

by these countries. The legal origin that persists in the countries is a strong determinant of these

outcomes. The papers of La Porta et al. emphasize that countries differ and that this difference has an

impact on firms and corporate finance. I would also like to emphasize that incentivization is the red line

that leads through this study and provides the push and pull mechanism that finally leads to performance.

These mechanisms are involved throughout all parties, from binary legal origin providing the direction; to

individual countries using the direction and creating a unique framework; to firms working with the

framework and adapting their capital structure to accommodate their needs; and then the firm’s managers

who are bound by the framework set out by countries and firms, trying to extract as much as possible for

personal needs, loosely adapted from the principle maximum utility function: “if everyone thinks of

themselves, everyone is sought after”.

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3 Data and Methods

Following Croci, Doukas and Gönenç (2011) and Gönenç, Hermes and van Sinderen (2013), I

collect the data from the ORBIS database on all information I need. Especially the ability to distinguish

between ownership thresholds is convenient for dummy variable creation. I apply a three step approach in

order to increase validity and decrease potential measurement errors. After the first step, data collection in

ORBIS, I secondly, edit the data in EXCEL, which leads to thirdly, the final data analysis in STATA.

Interestingly, I find two datasets of almost equal size. A distribution of countries included in the dataset

can be found in table A3. A distribution observations per countries can be found in figure A1

Following the suggestions from Chen and Yu (2012), managerial ownership is created as a

dummy variable where managers either have full (or ultimate) control or not. To do so, I apply filters in

ORBIS for either >50% ownership of managers or <50% ownership of managers. These datasets are then

merged per country and later for the whole set.

3.1 Data collection process

For the international perspective of the research, to obtain comparable data, the same settings are

downloaded into four different sets (each region and each ownership type). The main reason for doing this

is to have four sets that can be analyzed individually and put together into one to analyze the fifth as a

whole. Additionally, Orbis has better internal measures to distinguish between ownership types in their

search strategy options than would be possible to evaluate by hand or judgement of other variables

provided by Orbis. Hence, following search strategy is pursued:

1. Firms are excluded that are in the financial service, gambling and betting, insurance, reinsurance,

(pension) funding, those that are similar and those auxiliary to these services because of their

potentially distorted Tobin’s q values. Their complete code according to SIC categorization (SIC,

2016) is excluded.

2. The year of incorporation is set to between 2001 up to and including 2015, excluding companies

for which the year of incorporation is unknown, to be precise. Data for 2016 is excluded, because

most of the data is not available yet.

3. Only publicly listed companies are included.

14

This concludes the general search strategy that applies to all of the four datasets. Following selections are

created once for each combination:

1. The two possible regions are first, the geographical region of the United States, which is at the

same time one political region. This is important to mention, because for the counter variable, the

political region of the all areas that are considered as belonging to the European Union in a wider

spectrum, namely western and eastern Europe as well as Scandinavia, the Baltic, Nordic and

Balkan states are chosen. Currency issues should not be relevant within the framework of this

research as the performance measure, Tobin’s Q, is merely a ratio. Hence, this measure is

independent of currencies.

2. The possible ownership scenarios are twofold here; first scenario are managers owning less than

50% of the business and the second scenario is managers owning more than 50% of the business.

Orbis provides this selection criteria in their selections strategy options and for this I include

companies for which the manager is also the ultimate owner only for companies >50%

managerial ownership.

3. Only absolute year values for annual data are used and all values are denominated in US dollar.

Final size of datasets is the following: Firstly, US firms with managers owning more than 50%;

before cleaning raw data size is 409 firms. Secondly, US firms with managers owning less than 50%;

before cleaning raw data size is 1117. Thirdly, European firms with managers owning more than 50%;

before cleaning raw data size is 279. Lastly, European firms with managers owning less than 50%; before

cleaning 997. The combined set with added dummy variables for the two different specifications

(ownership and region) includes a raw amount of 2802 firms. The absolute final sample size is 2005

firms. The US dataset includes 1131 and the European dataset 878 firms. The difference of 4 firms stems

from double counted firms that are split in two, but run under the same firm name.

Finally, I insert the original datasets of La Porta et al. (1998 and 2008), to obtain the

cross­country variables of legal origin, anti­director rights and creditor rights.

3.2 Treatment of missing data

For some firms it occurs that there is missing information for one or more years; the different

types are treated differently for the different tests that are performed. Three types of missing values exist

that are corrected for. Firstly, those for which some or no data is available and either way has no “last

value of Tobin’s Q” according to ORBIS. Those are deleted, as they are useless for this research and it is

15

not known for what reason no last value is listed. This provides a more realistic number of firms that are

actually in the dataset. This is done by creating a function that gives an output of either 0 (for available

data in 2015) or 1 (for no data available in 2015) and then filtering by 1, which rows are deleted.

Secondly, those that arise because of “late IPO”, namely stemming from late public offering that results in

available data from a later point as this research includes only young firms and not all are going public at

the same time. In these cases regularly data is available from that point on, but not before. This

information is only necessary for panel data. Thirdly, values missing for specific years. It is rare but does

happen that a single year is missing. In these cases the average of the year before and after is taken. This

information is also only needed for panel data and not for the main test, Mann­Whitney U test.

Additionally, two extreme outliers were dropped.

3.3 Treatment of outliers

Only applied to the non­panel data tests, to finalize the data treatment process and avoid outliers

affecting results, trimming to the 5th and 95th percentile is being conducted as the extremity of some

values does not reflect the overall picture of the dataset and it may well be that the data collected is at

times without foundation. I chose trimming in place for winsorizing, because a test with winsorizing led

the medians that replaced the extreme values still be outliers due to the extreme outliers that I want to

correct for. It is also my intention to test for realistic results that others would obtain as well with different

datasets or in different regions. Hence, it appears obvious that this dataset needs to be trimmed. For the

joint dataset the lower threshold is .09, the higher 224.95, leaving 2005 companies. For the European

dataset the lower threshold is .083, the higher 5.62, leaving 878 companies. For the US dataset, the lower

threshold is .1, the higher 651.48, leaving 1131 companies. I left the dataset I use for panel data as is and

only trimmed values for robustness and sensitivity tests.

3.4 Empirical methodology

In my empirical analysis, I use two measures of firm performance: Tobin’s Q and the natural

logarithm of Tobin’s Q. Adding the natural logarithm of Tobin’s Q gives me the opportunity to normalize

the distribution of the dataset and obtain elasticity results between the variables when conducting multiple

regression analysis.

16

3.5 Dependent Variable ­ Tobin’s Q and LN Tobin’s Q

The dependent variable is a proxy for firm performance. For this research, I use the natural

logarithm of Tobin's Q (LNQ), which is market value/total assets and used commonly as a proxy in

similar studies (e.g. Barnhart and Rosenstein, 1998; Gönenç and Scholtens, 2017; La Porta et al., 2001;

Morck, Shleifer and Vishny, 1988). Additionally to following similar studies on ownership, I argue that

using Tobin’s Q as a measure of intrinsic value of a firm reflects future potential better than other types of

accounting based measures because of the young age of firms. Especially in the current times it is

relatively common to have large pre­revenue companies that live off a potential without actual revenues

such as Facebook or Google for a long time, to name prominent examples. Therefore, using ROA or ROE

as firm performance indicators would be wrong, which will be shown and inferred upon in robustness

tests. Note that the assumption holds that Tobin's Q is a measure that implies time (as in expectancy)

within its value, therefore the sets for last years values and panel data should be similar enough to obtain

the same results, which is also found in the robustness tests. To be comprehensive, the main tests will still

be using panel data analysis.

3.6 Relevant Independent Variables

The main independent variables are managerial ownership (OWNER) and region (REGION). For

both variables I created dummy variables where the dummy takes one for non­managerial owned firms

and zero otherwise as well as the region dummy, which takes the value for zero if the firm is located in the

US and one for Europe. This separation is based on findings of different concentrations of ownership and

family owners and provides a benchmark for the following variables. To test the international perspective,

this paper follows the papers of La Porta et al. (1998, 1999 and 2001), who developed creditor rights

scores and categorized the legal origins of the countries involved in this research. Within the framework

of this research, investor rights (CR and ADR) are dealt with as a dummy for the individual creditor rights

levels. The same procedure applies to legal origin (CIVIL and COMMON). The reason behind this is that

creditor rights is not a continuous variable in itself. While there is strength indicated by having a smaller

number, the individual dummies that make up the creditor rights value may not be of equal weight (La

Porta et al., 1998).

17

3.7 Control Variables

Previous research shows that other variables may also influence the outcome of this type of

research. In the regression analyses at a later stage, I therefore control for multiple factors that may

influence the dependent variable firm performance and the independent variables, managerial ownership

and region. These control variables are categorized into size and country­level variables.

With respect to the size­level control variables, I control for firm size (FIRMSIZE), measured as

the natural logarithm of market capitalization. Another firm size proxy is controlled for, employee “size”

(EMPLOYEES), measured as the natural logarithm of number of employees. Further, to include the

managerial aspect into firm size and the focus on employees, I control for number of managers

(MANAGERS) and again use the natural logarithm for this measure.

Additionally, I will use controls for industry and test with switching firm performance proxies to

see whether the Tobin’s Q is the driving factor instead of other firm performance proxies for the results.

To do so, I use the Standard Industrial Code categorization that can be found in ORBIS. Due to size and

amount of variables within the table as well as their results, those tests are moved to robustness.

3.8 Variance analysis models

In order to test whether there are actually significant differences between the means of different

dependent and independent variables, I conduct Mann­Whitney U tests. A similar approach has been

conducted in Gönenç and Scholtens (2017). For hypothesis 1, I check for significant differences between

Tobin's Qs under managerial and under outside ownership. Further, I want to test whether there are

significant differences in means that predict higher values for managerial ownership, compared to outside

ownership, which is hypothesis 2. Additionally I test for variances between regions for managerial

ownership, followed by testing whether one region has a stronger stronger effect of managerial ownership

on Tobin's Q. The selection of this test originates from the significant tests for homogeneity of variance

(Levene’s test) and normality (Skewness/Kurtosis and Shapiro­Wilk) for all datasets and for both each

Tobin's Q as well as the natural logarithm of Tobin's Q.

3.9 Log­Log regression models

In order to test for the impact of managerial ownership and robustness, I conduct multiple

regression analyses, which is the common method for comparable studies, following the earlier mentioned

18

similar papers again (e.g. Barnhart and Rosenstein, 1998; Gönenç and Scholtens, 2017; La Porta et al.,

2001; Morck, Shleifer and Vishny, 1988). The data utilized within these equations are panel data, which

includes static information that is time­invariant (legal origin, etc.). Throughout the models, I use the

natural logarithm of Tobin's Q (LNQ) as dependent variable. Due to the still extreme values of Tobin's Q

from the United States, further trimming may not be ideal and not be equally possible, hence the

logarithmic transformation to normalize distribution. The methods are regressed using random effects.

3.10 Random Effects model

To avoid STATA dropping time­invariant variables out of the dataset due to collinearity with the

ID, I use random effects models instead of fixed effects. Most of the dependent variables used are

time­invariant and fixed effects do not allow time­invariant variables. Additionally, it is possible that

differences across the firms in the sample have some influence on firm performance. To ensure that the

results are valid, random effects are used across firms.

Table 1

Definition of relevant variables, their respective measurement and part within this study.

Variable Unit of Measurement Usage Description

LN Tobins Q Ratio Dependent Variable The natural logarithm of Tobins Q as described below

Tobins Q Natural Logarithm Alternative Dep. Variable

ORBIS' ratio of market capitalization/assets; Confirmed by the data specialist of ORBIS ­ Bureau van Dijk

Ownership Dummy Independent Variable 1 if the firm is owned by outside shareholders (managers <50% ownership) and 0 if managers own company (>50% combined)

Region Dummy Independent Variable 1 if the firm is from the Europe and 0 for companies from the United States

Creditor Rights Categorical Independent Variable Strength of creditor rights as defined by La Porta et al. (1998), ranging from 0 to 4; 0 being low, 4 being high investor protection

Shareholder Rights Categorical Independent Variable Strength of shareholder rights as defined by La Porta et al. (1998), ranging from 0 to 6; 0 being low, 6 being high investor protection

Legal Origin Categorical Independent Variable Legal origin of firm country as described in La Porta et al. (1998)

LN Market Cap Natural Logarithm Control Variable Market capitalization of firm

LN Employees Natural Logarithm Control Variable Number of employees in firm (estimation)

LN Managers Natural Logarithm Control Variable Number of managers in firm

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4 Hypotheses and Empirical Models

In this study I focus on publicly listed firms that incorporated after the dot­com­bubble 2001,

which I call “young firms”. I use data of firms from the United States and Europe. The structure applied is

a funnel approach. In the wake of having a large amount of comparable and potentially pivotal proxies for

the international perspective, empirical testing will be done stepwise, introducing variables successively

and adopt different combinations to better comprehend their influence. Using this strategy aims to

improve the validity from a reader’s view. In the following I use the findings outlined in the literature

review to point into a direction, which then creates hypotheses. Additionally, for ease of read and to

prevent repetition of theory, I include the regression equations and analyses methods following from the

hypothesis, whose methodology is explained in section three and is applied in section five. This way it

should become clear immediately how this section (four) is put into practise in the following section

(five). A definition of variables can be found in Table A1 and a description of the variables utilized in the

model can be found in the respective table descriptions. Equations using analyses of variance testing are

denoted (1) thru (5) and panel data regressions are denoted (6) thru (13).

4.1 Null Hypothesis

The hypotheses of this study are the alternative of following two null hypotheses. I am testing

whether managerial and outside ownership are equal in their means to determine whether there is any

validity present to exercise this research. If the null hypothesis were to be significant, there would not be

significant results to infer any economic significance or relevance, rendering this study redundant.

For the case of variance analyses, following null hypothesis holds:

H 0

μ 1 = μ 2 (1.1)

Alternatively for the case of multiple regressions, following null hypothesis holds:

H 0

1 β2 .. βk 0 β = = . = = (1.2)

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4.2 Ownership Difference and Direction Model and Hypotheses

As discussed before, there are multiple forces affecting behavior of the agents, which leads to the

hypothesis of difference between the two groups. The forces of ownership types mostly lean on tacit and

soft facts. Principal­Agent theory (Jensen and Meckling, 1976) provided evidence that managers as agents

not always work toward the same goals as their principles, especially if governance does not align these.

Stewardship theory (Donaldson and Davis, 1991) argues too, that there are differences in firm

performance when goals are automatically aligned by having the manager in full charge. Both agency and

stewardship theory are leaning on Berle and Means (1932), that the self­interest of managers combined

with their ability to set directions, may affect firms. Additionally, Morck, Shleifer, and Vishny, (1988),

Chen and Yu (2012) and Fama and Jensen (1983) provide plenty evidence that firms with different types

of owners are not performing equally. Due to the effects of agency cost, stewardship theory and past

research on family firms, especially insights into the comparable first generation family firms, I argue that

there are also differences when managers own a firm, much like there are differences when families own

firms, compared to non­family firms. Consequently, I hypothesize the following:

H1: There are differences in firm performance between firms owned by managers and those who are

not owned by managers

Hence, for hypothesis 1, I check for significant differences between Tobin's Q under managerial

and under outside ownership:

Q anagerialOwnership = Q utsideOwnership μ 1 M / μ 2 O (2)

Utilizing the findings that argue for differences also provides for a directional hypothesis. The

aforementioned principle­agent theory suggests that if there are consequences of separation of ownership

and control, then these are negative. Since firm performance is literally defined as the performance of the

firm, “outsourcing” control to an agent only achieves firm performance goals to the degree with which the

goals of principal and agent are aligned, not beyond (Jensen and Meckling, 1976), creating, if at all, a

negative effect. As for stewardship, the argument of Donaldson and Davis (1991) goes the opposite way,

claiming that eliminating separation of ownership and control actually lets the manager be the “steward”

21

that goes the “extra mile”, outperforming others, consequently creating a positive effect. These findings

are heavily inspired by findings on family­firms, who have lower agency costs (Jensen and Meckling,

1976), think more long­term (Gómez­Meija et al., 2011), resulting in a potentially more altruistic course

of conduct (Schulze, Lubatkin and Dino, 2002) and still are not found to be expropriating wealth from

minority shareholders (Croci, Gönenç and Ozkan, 2012). Rounding up the argumentation, agents may

entrench themselves into the company, with exclusively negative repercussions, creating another type of

friction through agency costs (see section 2.6). By implication, I argue that on one hand negative effects

of agency costs and on the other hand positive effects of stewardship benefit one side more than the other,

not only explaining that the groups are different, but also that there are significant firm performance

differences in favor of those that are owned by the managers. Thus, I hypothesize that:

H2: Managerial owned firms perform better than outside owned firms

Hence, I want to test whether there are significant differences in means that predict higher values

for managerial ownership, compared to outside ownership, which is H2:

Q anagerialOwnership Q utsideOwnership μ 1 M > μ 2 O (3)

Further, I argue that there is a significantly positive and economically relevant relationship for

ownership on firm performance in a simple model that serves as benchmark for following tests, leading to

following panel regression equation, estimated using random effects:

NQ α γ1OWNER γ2REGION (c ) L i,t = 0 + ′i + ′i + i + u i,t (6)

4.3 International Perspective

In the following, I want to build up a strong international perspective by identifying determinants

of international differences. The most famous literature in this regard originates from various papers of La

Porta, Lopez­de­Silanes, Shleifer and Vishny, who specify four areas of international differences: (1)

Country/Region­level, (2) legal origin, (3) creditor rights and (4) shareholder rights. Following this

section, I separate the set into dummy variables according to the firms regions, either the United States or

22

Europe (4.3.1), the legal origin hypotheses follow (4.3.2), finalized by investor protection, which is both

creditor and shareholder rights (4.3.3).

Even in the hypothetical case of not finding differences in firm performance by purely separating

ownership types in the whole dataset, these findings may still be swayed by the regions used. If there are

differences in ownership effects on firm performance, but with different directions, they would not be

visible in the tests before, balancing each other out. Hence, the international perspective testing adds

another significance level and robustness.

4.3.1 Ownership Country Difference and Direction Model and Hypotheses

Additionally to ownership, I argue that there are differences between the region in which the firm

is located. I derive this argument from the same studies that focused on family firms and managerial

ownership, who also limited or compared their data sets to regions. However the main focus for this part,

using the plain regional separation, serves as methodological and theoretical benchmark for the following

sections and the separations according to La Porta et al. (1997, 2001 and 2008). Here, I hypothesize that:

H3: There are differences between managerial owned firm performances in US and European firms

Hence, I want to test whether there are significant differences in means of managerial ownership

in the United States, compared to managerial ownership in the Europe, which is H3:

Q anagerialOwnershipUS = Q anagerialOwnershipEurope μ 3 M / μ 4 M (4)

To test the effects of region and ownership, I introduce an interaction term of european firms

owned by managers, following similar papers (Gönenç, Hermes and van Sinderen, 2013; Gönenç and

Scholtens, 2017 and La Porta et al., 2001). To do so, I reverse the dummy variable of ownership so that

the values of one only apply for European firms and those owned by managers. The twist this brings is

emanating from the hypotheses, which only hypothesize a negative relationship in Europe with

managerial owners performing comparatively less well than their counterparts in the United States. The

calculation brings about an interaction effect, which is in principle a dummy again, but triggers only when

these are managerial owners from Europe. This is in principle hypothesized to be the only outlier in terms

of direction. Hence, this interaction is supposed to further substantiate region effect hypotheses and

23

provide a ground to argue for international differences. The basic panel regression is estimated using

random effects:

NQ α γ1OWNER γ2REGION γ3OWNER EGION (c ) L i,t = 0 + ′i + ′i + ′i R ′i + i + u i,t (7)

Leading further, I argue that the effects of managerial ownership are stronger for firms owned by

managers in the United States, again serving as benchmark. Thus, I hypothesize that:

H4: Managerial ownership effects are stronger for US firms than European firms

Hence, I want to test whether there are significant differences in means that predict higher values

for managerial ownership in the US, compared to managerial ownership in Europe:

Q anagerialOwnershipUS Q anagerialOwnershipEurope μ 3 M > μ 4 M (5)

4.3.2 Legal Origin Models and Hypothesis

Additionally to ownership direction and the regional hypothesis, I want to go into more detail and

argue that the legal origin of a company is a performance determinant of a company. In the legal origins

theory of La Porta et al. (1997 and 2008), the authors argue that two different types of legal tradition,

common and code law, affect economic outcomes based on their legal specific determinants; better

protection of investors, less government regulation, less government ownership and better enforcement

through judicial systems that are more independent and less formalized. In the outlined scenario, common

law countries outperform code law countries. The stronger protection of investors that La Porta et al.

found in common law countries determines a better breeding ground for investors with better developed

capital markets (La Porta et al., 1997), because enforcement of their rights incentivizes these investors to

finance firms. On the other hand, weaker rights in for example french law somehow pose a bottleneck,

which is supported by the findings of higher concentration of ownership in french civil law countries.

Following these findings, the fifth hypothesis is formulated as follows:

H5: The effect of managerial ownership firm performance is positive for firms located in countries with

common law and negative for firms located in countries with code law

24

Hence, I am creating dummy variables for each legal origin to obtain individual results, which

can then be assessed upon economic relevance and significance. To prevent overloading this model with

too similar variables, I replace REGION with the two variables, COMMON and CIVIL to be region

indicators:

NQ α γ1OWNER γ2COMMON 3CIV IL (c ) L i,t = 0 + ′i + ′i + γ ′ i + i + u i,t (8)

Further, to assess the specific effect to managerial ownership, I again create an interaction

variable of ownership with the other relevant independent variable, common law and civil law,

respectively. To further substantiate potential findings, I include the control variables FIRMSIZE,

MANAGERS and EMPLOYEES to determine the impact of legal origin on firm performance:

NQ α γ1OWNER γ2COMMON β3FIRMSIZE β4MANAGERS L i,t = 0 + ′i + ′i + ′i,t + ′i,t

β5EMPLOY EES γ6OWNER OMMON (c ) + ′i,t + ′i C ′i + i + u i,t

(9)

NQ α γ1OWNER γ2CIV IL β3FIRMSIZE β4MANAGERS L i,t = 0 + ′i + ′i + ′i,t + ′i,t

β5EMPLOY EES γ6OWNER IV IL (c ) + ′i,t + ′i C ′i + i + u i,t

(10)

4.3.3 Creditor and Shareholder Rights Models and Hypothesis

Additionally to investor protection and legal origins, the missing puzzle piece following from the

famous papers of La Porta et al. (1997, 1998, 1999a, 1999b, 2001 and 2008) are investor rights. While the

regional separation model differs from code vs common law mainly in switching the United Kingdom

from one side to the other (compared to dummy REGION), investor rights are specific to their respective

countries. Creditor rights are ranging from 0 to 4 and shareholder rights are ranging from 0 to 6, each

indicating stronger rights of investors with increasing values (La Porta et al., 1998). Further, the authors

explain weaker investor rights to penalize firms with lower valuations, which is crucial to this study, using

Tobin's Q as firm performance proxy. Consequently, I hypothesize:

H6: The effect on managerial ownership firm performance is positive with stronger investor rights.

25

In the following models, CR are used as continuous variable as the methodology of La Porta et al.

(1998) may be laid out. A high value for creditor rights being strong, a low value of creditor rights being

weak. Hence, the higher the CR coefficient, the stronger the effect of creditor rights, while the sign

indicates the direction, e.g. negative signage indicating weak creditor rights to be bad for firm

performance and vice versa. Further, to assess the specific effect to managerial ownership, I again create

an interaction variable of ownership with the other relevant independent variable, creditor rights (CR) and

shareholder rights (ADR), respectively. To further substantiate potential findings, I include the control

variables FIRMSIZE, MANAGERS and EMPLOYEES to determine the impact of investor rights on firm

performance:

NQ α γ1OWNER γ2CR β3FIRMSIZE β4MANAGERS L i,t = 0 + ′i + ′i + ′i,t + ′i,t

β5EMPLOY EES γ6OWNER R (c ) + ′i,t + ′i C ′i + i + u i,t

(11)

To test the effects of shareholder rights and obtain directly comparable results, I switch creditor

rights with shareholder rights

NQ α γ1OWNER γ2ADR β3FIRMSIZE β4MANAGERS L i,t = 0 + ′i + ′i + ′i,t + ′i,t

β5EMPLOY EES γ6OWNER DR (c ) + ′i,t + ′i A ′i + i + u i,t

(12)

Lastly, all variables employed in this study combined into one regression equation to substantiate

potential economic significance of the variables:

NQ α γ1OWNER γ2OWNER EGION 3COMMON 4CIV IL L i,t = 0 + ′i + ′i R ′i + γ ′i + γ ′ i5FIRMSIZE 6EMPLOY EES 7MANAGERS 8CR 9REGION (c ) + β ′i,t + β ′i,t + β ′i,t + γ ′ i + γ ′ i + i + u i,t

(13)

I will start with the small tests and descriptions for the individual datasets to get an in­depth view

of the underlying data. In the following I will conduct tests that are based on the information found,

including the added variables of La Porta et al. (1998, 1999 and 2001), creditor rights and legal origin of

firm to perform inference based on findings.

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5 Empirical results

This part of the study presents the results of the aforementioned analyses carried out. Descriptive

statistics begin with an impression of the data at hand by showing the summary statistics as well as

correlation matrix of the selected relevant variables of the panel data in table 2. A full description can be

found in table A1. In the following, I start with testing the distributions and variances combined, because

they deliver the same results for the US and Europe (5.1). Afterwards, individual samples for the US

firms are tested (5.2), then the European firms are tackled (5.3), to get a first individual impression of

their results, following the research of La Porta et al. (1998) suggesting that different countries are

heterogeneous. These two sections paint the dry picture of the results received, which are interpreted

subsequently. After doing so, I dig deeper and explain further, based on the whole sample. The same

strategy applies to the explanation of empirical results that was laid out in section four; I am building up

the argumentation stepwise to empirically explain the independent variables used and understand their

impact, finally leading to a conclusion. The intention is to let the reader observe how the variables and

setups affect ownership impacts on firm performance, which is the main aim of this study. The dummy

“ownership” is thus the only variable that stays constantly in the regressions. The empirical results are

concluded by various robustness tests in different setups.

5.1 Overlap US and Europe for Homogeneity of Variance and Normality

For both data sets I begin by testing for normality. Both the regular as well as natural logarithm of

Tobin's Q show significant results for their normality tests, therefore I reject the null hypothesis of normal

distribution. Additionally, I conduct a Levene’s test for equality of variances, for which I also obtain

significant results and thus again fail to reject the null hypothesis, affirming homoscedasticity (see tables

Appendix­C). Based on the findings, I conduct nonparametric tests instead, to compare the two

independent groups of ownership types, namely the Mann­Whitney U, which does not assume normality.

To test robustness, I also include t­tests. To get a clear picture of the values in this study, both panel and

single year values of both the logarithmized and raw values of Tobin's Q are used to visualize their

comparability. The assumption holds that Tobin's Q implies time within its value. To be precise and

comprehensive, the main regressions are using exclusively panel data.

27

Table 2

Descriptive statistics in panel A report and compare number of observations (N), mean, standard deviation, minimum values and maximum values for the whole sample, with manager as an owner and for outside owners. This sample comprises 2005 observations for both US and European firms with a sample period between 2001 and 2015. A full description of variables is presented in table A1. Panel B of this table reports the correlations of the relevant variables, defined in table 1.

Panel A: Summary Statistics of the variables used in the joint sample after reshape for panel analysis

All Sample Manager as Owner Outside Owner Variables N Mean Std.

Dev. Min Max

N Mean Std.

Dev. Min Max

N Mean Std.

Dev. Min Max

LNTobinsQ 26,065 0.534 1.544 ­2.386 5.412 4,511 0.927 1.953 ­2.333 5.412 21,554 0.452 1.430 ­2.386 5.403 LNMarketCap 26,065 16.806 3.110 ­1.833 26.387 4,511 15.444 3.196 ­1.833 26.387 21,554 17.091 3.015 2.259 25.204 LNEmployees 21,333 4.121 2.608 0 13.323 3,289 3.116 2.516 0 11.043 18,044 4.304 2.582 0 13.323 LNManagers 25,766 2.139 0.802 0 4.812 4,407 1.671 0.829 0 4.812 21,359 2.236 0.761 0 4.804 Ownership 26,065 0.173 0.378 0 1 4,511 1 0 1 1 21,554 0 0 0 0 Region 26,065 0.459 0.498 0 1 4,511 0.450 0.498 0 1 21,554 0.461 0.499 0 1 Owner x Region 26,065 0.078 0.268 0 1 4,511 0.450 0.498 0 1 21,554 0.000 0.000 0 0 Shareholder Rights 17,108 4.521 1.049 0 5 3,016 4.375 1.236 0 5 14,092 4.553 1.002 0 5 Creditor Rights 25,428 1.708 1.282 0 4 4,316 1.283 0.860 0 4 21,112 1.794 1.335 0 4 Common Law 25,558 0.762 0.426 0 1 4,342 0.608 0.488 0 1 21,216 0.794 0.405 0 1 Civil Law 26,078 0.203 0.403 0 1 4,511 0.360 0.480 0 1 21,554 0.171 0.376 0 1

Panel B: Correlations matrix

1 2 3 4 5 6 7 8 9 10 11

1 LNTobinsQ 1.0000 2 LNMarketCap ­0.1606 1.0000 3 LNEmployees ­0.3513 0.6771 1.0000 4 LNManagers ­0.3643 0.6414 0.6068 1.0000 5 Ownership 0.1165 ­0.2004 ­0.1645 ­0.2653 1.0000 6 Region ­0.3355 0.1922 0.1783 0.4396 ­0.0090 1.0000 7 Owner x Region ­0.1338 ­0.0554 ­0.0189 0.0037 0.6349 0.3151 1.0000 8 Shareholder Rights 0.2201 ­0.1600 ­0.1145 ­0.3290 ­0.0645 ­0.9109 ­0.4407 1.0000 9 Creditor Rights ­0.2156 0.2371 0.2298 0.3643 ­0.1497 0.5956 ­0.0212 ­0.4064 1.0000 10 Common Law 0.2164 ­0.0007 0.0145 ­0.1657 ­0.1638 ­0.5997 ­0.4170 0.9173 0.2052 1.0000 11 Civil Law ­0.2145 ­0.0206 ­0.0307 0.1318 0.1781 0.5484 0.4313 ­0.8342 ­0.1905 ­0.9155 1.000

5.2 US Firms

Table 4 presents the results of the Mann­Whitney U as well as t­tests performed on Tobin’s Q for

the panel data as well as “last available year's” Tobin's Q values in brackets. I am testing a sample of 4920

(1131) observations, of which 752 (214) are management owned and 4168 (917) outside owned. The

28

results indicate that the medians are different with a significance level of 1%, I thus reject the null

hypothesis that the means of managerial ownership and outside ownership on Tobin's Q are equal,

indicating differences between the two types of ownership, showing z = 8.404 (7.649), p = 0.000 (0.000).

Additionally, the output shows a p­order value of 0.596 (0.668), indicating that in 59.6% (66.8%) of

random draw cases, Tobin's Q performance would be higher if there is managerial ownership present,

providing support for hypothesis 2 in the US sample. The same results are received for the logarithmized

as well as raw forms of Tobin's Q and also show the comparability of Tobin's Q values at one point in

time to time­series panel data.

Panel B of table 3 adds some robustness to the rank sum tests performed and further substantiates

the findings. The results of the two­tailed t­test on the natural logarithm of Tobin's Q show that in the

United States, firms with owners who are also managers have statistically significant higher Tobin's Q

(1.9815 +/­ 2.26682) than those owned by outsiders (1.1562 +/­ 2.0651), t(4889) = ­9.569, p=0.000.

Those results are also supported by testing the original value of Tobin's Q on last year’s values, obtaining

statistically significant higher Tobin's Q (70.1332 +/­ 131.1039) than those owned by outsiders (20.3622

+/­ 67.0274), t(876) = ­7.889, p=0.000.

5.3 European firms

I am testing a sample of 4766 (878) observations, of which 601 (145) are management owned and

4165 (733) outside owned. The results indicate that the medians are different with a significance level of

1%, thus I reject the null hypothesis that the means of managerial ownership and outside ownership on

Tobin's Q are equal, indicating differences between the two types of ownership, showing z = ­2.884

(­2.985), p = 0.004 (0.003). Additionally, the output shows a porder value of 0.464 (0.422), indicating that

in only 46.4% (42.2%) of random draw cases, Tobin's Q performance would be higher if there is

managerial ownership present, thus not providing support for hypothesis 2 in the European sample and

indicating differences for the meaning of managerial ownership in between those regions.

Panel B of table 3 adds some robustness to the rank sum tests performed and further substantiates

the findings. The results of the two­tailed t­test show that using last years Tobin's Q values, in Europe,

firms with owners who are also managers have statistically significant lower Tobin's Q (­0.4009 +/­

0.9591) than those owned by outsiders (­0.1266 +/­ 0.9383), t(876) = 3.2047, p=0.001. Those results are

also supported by testing the original value of Tobin's Q, obtaining statistically significant lower Tobin's

Q (1.028 +/­ 0.9923) than those owned by outsiders (1.3233 +/­ 1.2147), t(876) = 2.7440, p=0.006.

29

5.4 Implication US and European individual tests

Clearly evident are the directions of the individual country. While in the United States a positive

and strong significant relationship in favor of managerial ownership exists, the opposite is the case for the

European firms, confirming the findings of La Porta et al. (1998). Interestingly, a weakening factor for the

European results are the stronger deviations in mean pointing to varying stronger than the managerial

ownership counterpart (1.03 +/­ 0.99 vs. 1.32 +/­ 1.21). This may have to do with the presence of family

firms in europe, which make up a large part of european firms (Faccio and Lang, 2002) and tend to work

more long­term oriented (Casamatta and Guembel, 2010), which should decrease variance and induce

stability. Overall, the risk averseness is even more apparent, looking at the mean values for Tobin's Q in

between the regions of Europe and the United States. Since Tobin's Q is a ratio of variables that are

usually and most likely equal, especially in the long run. Wang, Wang and Yang (2013) expect values to

revolve around 1.2. This is indeed the case for most european firms, however not for firms from the

United States, whose combined ownership mean value is 29.78. The managerial Ownership mean on the

other hand even goes up to 70.13, with a standard deviation of 131.10, which shows that there are many

extreme values included in the sample. This gets even more apparent if we look at the values of the

untrimmed panel data variables, showing a mean of 724 with a standard deviation of 9326 for

management owners. High deviations and high values are also the case for outside ownership, whereas

not as extreme as their managerial counterpart. The results of the tests therefore not only shed a light on

the differences on ownership impact, but also on the differences in the sample. This is clear indication that

results may depend on the institutional framework that the firms are in, clearly the outline of La Porta et

al. (1998, 1999a, 1999b, 2001 and 2008). Table 3

Comparison of region and firm performance measured in Tobin's Q. Using Tobin's Q and the natural logarithm of Tobin's Q obtained the same results for the ranksum tests. For the t­tests, both Tobin's Q as well as the natural logarithm of Tobin's Q are included and compared. In this case 0 indicates managerial ownership and 1 indicates outside ownership. In the framework of this table, as explained in the hypotheses is the mean of applicable variables.μ

Panel A: Mann Whitney U Test

Combined Managerial Ownership Outside Ownership Results

Region N Ranksum Expected N Ranksum Expected N Ranksum Expected Z P 0 > 1

Panel

Europe 4766 11359761 11359761 601 1341542 1432483.5 4165 10018219 9927277.5 ­2.884 0.0039 0.464

US 4920 12105660 12105660 752 2151587 1850296 4168 9954073 10255364 8.404 0.0000 0.596

Last Value

Europe 878 385881 385881 145 55399 63728 733 330483 322154 ­2.9850 0.0028 0.422

US 1131 640146 640146 214 154033 121124 917 486114 519022 7.6490 0.0000 0.668

30

Panel B: T­Test

Combined Managerial Ownership Outside Ownership Results

Region N Mean Std. Dev N Mean Std. Dev N Mean Std. Dev t P (T < t ) P (|T| > |t|)

Panel LNQ

Europe 4,759 ­0.0961 1.1152 600 ­0.1871 1.1435 4,159 ­0.0830 1.1106 2.1388 0.9837 0.0325

US 4,891 1.2819 2.1877 745 1.9815 2.6682 4,146 1.1562 2.0651 ­9.5686 0.0000 0.0000

Panel Q

Europe 4,766 1.9602 7.3051 601 1.8670 4.6305 4,165 1.9736 7.6142 0.3347 0.6311 0.7379

US 4,920 393 8338 752 724 9326 4168 334 8146 1.1828 0.1185 0.8815

LNTobinsQ

Europe 878 ­0.1719 0.9467 145 ­0.4009 0.9591 733 ­0.1266 0.9383 3.2047 0.9993 0.0014

US 1131 1.2292 1.9472 214 2.2739 2.2919 917 0.9854 1.7724 ­9.0210 0.0000 0.0000

Tobin' Q

Europe 878 1.2746 1.1854 145 1.0287 0.9923 733 1.3233 1.2147 2.7440 0.9969 0.0062

US 1131 29.7795 85.2180 214 70.1332 131.1039 917 20.3622 67.0274 ­7.8890 0.0000 0.0000

5.5 Using full sample

In order to investigate whether managerial or outside ownership has a definitely more positive

influence on firm performance, I am conducting multiple types of panel data analyses. The model

assumes random variation across entities, because there is reason to assume that difference across those

entities have influence on the dependent variable, Tobin's Q and because the model includes time

invariant variables. These include, amongst others, law origins and creditor rights values. Additionally,

random effects allow for more generalized inferences. Either way, fixed effects do not yield results owing

to the nature of variables used. A log­log­model is employed in table 4, which is a helpful approach in

interpreting the results as the output shows better interpretable results to draw economic inferences, which

is the best case aspiration for this study.

From the earlier results of the analyses of variances, clear directions were visible, yet this analysis

aims to provide a deeper insight into what constitutes these differences and which factors influence those.

Hence, I laid out a step­by­step approach that adds variables throughout different stages, which makes the

data and the forces within more understandable, ultimately leading to results. Following La Porta et al.

(1998, 1999a and 2001), legal origin and creditor rights are major determinants for performance and

capital accessibility. Within their approach, they showed regional, legal and security mechanisms to be

influencing performance, which appear to overlap in countries’ institutional frameworks. Therefore I am

31

using the exact dataset from the La Porta et al. (1998) study to test for legal origin and investor rights. In

the following, I am laying out the results and construction of the panel data analyses:

Table 4 Presented in this table are the results of random effects panel regressions for the full sample of 2005 firms. The dependent variable is Tobin's Q, using the natural logarithm, the model shows . The dependent variables are: 1) Ownership as a dummy variable indicating the presence of managers owning >50% (=1) or managers owning <50% (=0), ;2) NQ L i,t WNER O ′i Region as a variable indicating the region of firm to be the US (=0) or Europe (=1), ; 3) the interaction between Ownership and Region, taking 1 for the interaction of EGION R ′ i European firms and owned by managerial shareholders, Owner*Region; 4) a dummy variable indicating the legal origin of a firm’s country to be common law (=1) or civil law (=0),

;5) a dummy variable indicating the legal origin of a firm’s country to be civil law (=1) or common law (=0), ;6) LNMarketcap, the natural logarithm of marketOMMON C ′i IV IL C ′i capitalization, ; 7) LNEmployees the natural logarithm of number of employees, ; 8) LNManagers, the natural logarithm of number of managers, IRMSIZE F ′i,t MPLOY EES E ′i,t

; 9) the value of creditor rights as constant variable indicating the creditor rights level of a firm’s country from 0­4 (La Porta et al., 1998), ; 10) Anti­Director rightsANAGERS M ′i,t R C i indicating the shareholder rights level of a firm’s country from 0­6 (La Porta et al., 1998), ; variables use following vectors: , column vector of (time­variant) parameters; DR A i β K­dimensional using as time­variant row vector of explanatory variables and , column vector of (time­invariant) parameters; M­dimensional, using as time­invariant row vector x′ i,t γ z′ i of explanatory variable, where is the constant term. Firm performance is constant when all variables seem insignificant, Table 1 provides further definitions, standard errors α 0 c ) ( i + u i,t are to be found in the brackets. This sample comprises 2005 observations for both US and European firms with a sample period between 2001 and 2015. Statistical significance is denoted with ***, ** and * at the 0.01%, 5% and 10% level, respectively. All regressions use random effects.

Variable (6) (7) (8) (9) (10) (11) (12) (13)

Ownership 0.511*** 1.030*** 0.762*** ­0.343* 0.510*** 0.265 ­0.884* 0.697***

[0.09] [0.12] [0.1] [0.15] [0.11] [0.16] [0.43] [0.13]

Region ­1.466*** ­1.276*** 0.056

[0.07] [0.07] [0.2]

Ownership*Region ­1.147*** ­0.910***

[0.18] [0.18]

Common Law 0.967*** 0.709*** 0.739**

[0.21] [0.09] [0.24]

Civil Law ­0.316 ­0.733*** ­0.273

[0.22] [0.09] [0.18]

LNMarketCap 0.088*** 0.092*** 0.104*** 0.058** 0.076***

[0.02] [0.02] [0.02] [0.02] [0.02]

LNManagers ­0.564*** ­0.600*** ­0.646*** ­0.408*** ­0.394***

[0.06] [0.06] [0.06] [0.09] [0.06]

LNEmployees ­0.214*** ­0.214*** ­0.209*** ­0.262*** ­0.212***

[0.02] [0.02] [0.02] [0.02] [0.02]

Ownership*Common 0.905***

[0.19]

Ownership*Civil ­0.835***

[0.19]

Creditor Rights ­0.068* ­0.193**

[0.03] [0.06]

Ownership*Creditor ­0.203*

[0.09]

Shareholder Rights 0.264***

[0.05]

Ownership*Shareholder 0.286**

[0.1]

Constant 1.384*** 1.295*** ­0.021 0.752*** 1.460*** 1.347*** 0.662 0.910**

[0.05] [0.05] [0.2] [0.21] [0.2] [0.21] [0.34] [0.29]

Sigma_E 1.1 1.1 1.1 1.1 1.1 1.1 1.2 1.1

rho 0.6 0.6 0.6 0.5 0.5 0.6 0.5 0.5

32

5.5.1 Basic models (6) + (7)

The basic models include the overall idea of the paper, to search for the impact of managerial

ownership on firm performance. Table 4, equation (6) clearly shows an increasing function of

performance when ownership is managerial, since the significant positive value for ownership indicates

improved firm performance when the manager is also the owner. As expected, the regression results also

show significant negative effect on Tobin's Q when the measured firm is located in Europe. From these

findings we can conclude that if the managerial ownership dummy of one triggers, we expect the Tobin's

Q to be 66.7% higher and when the firm is located in Europe, we can expect Tobin's Q to be 76.9% lower

than otherwise. Since the dummy variables used turn the term specific coefficient into a log­linear

function, the elasticity is calculated as follows: . To reinforce this finding, table C4 100 )% ( e γ 1

shows the delogarithmized findings of the simple OLS, using the raw values of Tobin's Q. In that

scenario, change of region amounts to a change of Tobin's q of ­11.9, which is just as extreme, looking at

a mean tobin's Q value of 8.31. Note that in this regression, these coefficients are actual changes and not

percentage, thus to put this into perspective, the change amounts to ten times the expected regular Tobin's

Q value of 1.2 (Wang, Wang and Yang, 2013). Nevertheless, the bottom line should be that firms from

different regions by all means have a strong, significant and economically relevant impact, supporting the

findings of Gómez­Meija et al. (2011) and La Porta et al. (1998, 2001).

Adding the interaction variable in equation (7) substantiates this finding even more. Most notably,

the effect of managerial ownership gets twice as strong, because the worst performing factors, Europe and

Outside ownership, are now comprised within that interaction variable, pushing values of Tobin's Q down

by 68.2%. Since all findings are significant to the 1% level, those are fundamentally clear indicators for

differences between ownership types and regions, demanding to dig deeper into the matter. Notice that

using a plain geographical measure mainly served to provide a benchmark for changes in legal origin

measures, which we are looking into now.

5.5.2 Legal Origin Models (8) ­ (10)

To add the legal origin models, I take out the variable “Region”. In the end, legal origin may just

as well be another proxy for region, as it was laid out by La Porta et al. (1998), just not geographically

fixed, which demands “Region” to be omitted. A similar idea applies at a later stage, when investor rights

are introduced. Howbeit, the variable region is now taken over by legal origin of the countries.

Interestingly, despite taking out the region interaction variable as well, the strong ownership indication

33

stays stable, significant and even higher than with the variable region instead of legal origin. Keep in

mind, this equation (8) is in the end the same as equation (6), just using another proxy for region. By

taking the split variables of legal origin instead of plain geographical region, I therefore obtain an

improved coefficient of ownership (0.762), which leads back to the only thing that actually changes,

moving the UK from one side (European region) to the other (Common law, together with the United

States). While I obtained extremely strong, positive, economically relevant and significant results for the

common law countries, this benchmarking emphasizes the positive impact emanating from common law

countries powerfully, not just the US, and further substantiates the findings plus the generalizability of La

Porta et al. (1997, 2001 and 2008).

Therefore, in line with all previous results obtained, common law origin positively predicts Q and

thus better firm performance, with 0.1% significance. These findings were already hinted in the analyses

of variance and visible via the extreme values of Tobin's Q in the descriptive statistics of table 2. While I

do predict civil law to negatively influence Tobin's Q and the output provides a negative value, I obtained

an insignificant p­value in equation (8). Only in the next stage, when adding more control variables, civil

Law becomes significant and negative as I would expect.

In equations (9) and (10), I added the control variables to test the robustness of the findings, of

which only the significant ones are included in the presented table, additional controls are used in the

robustness tests in Appendix­B, yielding the same results. In the same breath I test for their effects on the

relevant dependent variables as well as an interaction term to capture the effect attributable to managerial

ownership. The results show that control variables and the interaction variables do have an effect on the

results, reducing the strength of both ownership and common law region on Tobin's Q, still keeping them

significant, strong and turning the civil law region strongly significant and negative, as expected. This

finding strongly supports H5, showing that managerial owners perform 147.19% better in common law

countries, compared to other firms performing “only” 103.19% better. Managerial owned firms perform

with ­56.61% below par in civil law countries, compared to other firms only showing ­51.95%.

Additionally, both variables, civil and common law, are significant and show the same direction as their

interaction variable counterpart, which is absolutely in line with the findings of La Porta et al. (1997,

2001 and 2008) again. Also ownership stays significant, which is thus also under these circumstances

robust and provides support for the intention and hypotheses of this study. Also interesting in the results is

the fact that when testing for the common law interaction, ownership is turning negative, proving the

relevance for benchmarking and that the managerial ownership effect is especially captured in common

law countries. This same effect applies to equation (12) as well, when testing for shareholder rights,

which now follows.

34

5.5.3 Investor Rights Models (11) ­ (13)

Equation (11) shows a weakly significant negative impact of increasing creditor rights on the

value of Tobin's Q. In other words, in countries with stronger creditor rights, firms perform 6.57% worse

per increasing value, of which there are four. Yet especially managerial owned ones perform relatively

bad (­18.37%) in countries with stronger creditor rights, as captured by the interaction effect. This finding

may be argued to be attributable to the extreme values in the United States, which is a low creditor

country and are one homogenous region compared to european countries for which creditor rights values

vary. Under these circumstances I’d also expect shareholder rights to be strongly significant (for which

the US is rated the highest), which is the case. As the research of La Porta et al. (1998) proposed, stronger

shareholder rights are positively and strongly significant, increasing 30.21% per level of shareholder

rights of which five is the highest. Managerial owned firms perform even slightly better, improving

33.11%. Since there is extreme multicollinearity with the two legal origins, shareholder rights replaced

those and visibly filled the gap of ”common law” as expected. Considering that legal origins are a dummy

variable compared to the investor rights variables, the comparability becomes strong. On the other hand,

using creditor rights lets ownership become insignificant in equation (11), which needs to be interpreted

carefully in the light of the other results, due to the heavy weight of the United States and the small

coefficient. In the end I can conclude that for this sample stronger creditor rights predict lower Tobin's Q’s

and stronger shareholder rights have a positive significant impact on firm performance. Regarding

creditor rights, a possible explanation is the nature of capital structure. As mentioned earlier, stronger

creditor rights incline firms to be debt financed instead of equity financed, potentially disincentivizing

public listing, simply because it is not required. Preferring debt finance over equity potentially hints at

risk averseness too, which is penalized by the nature of Tobin's Q as performance measure in this case, as

it includes growth opportunities by default. Leading further, selecting exclusively publicly listed firms,

the sample may be biased towards those countries incentivizing equity financing.

En passant, if this interpretation reflects reality, I must stress that capital structure does matter,

especially across borders, refuting the arguments of Modigliani and Miller (1958) and Hamberg (2016),

who assume capital structure irrelevance and that value stems from operating activities, respectively. If

companies future cash flows do indeed reflect the current valuation, all is fine and value is created, yet if

that is not the case, we are looking at a serious bubble indicator.

Lastly, I observe the same picture when all variables are included in the sample. Due to strong

multicollinearity, this time I excluded shareholder rights. With the full impact of control and independent

35

variables, I can conclude that managerial ownership also overall affects firm performance positively. In

fact, the management owned firms perform 100.77% better than firms that are not owned by managers,

twice as good. Common law serves as auxiliary breeding ground for high values of Tobin's Q, even

performing 109.38% better than non­common law countries. Equation (13) also shows the change in

coefficients of “Region” from equation (6) where the impact amounted to a decline in 76.9% (­1.466)

when the region dummy triggers, to an economically insignificant (now positive) change of 5.8% (0.056),

showing that the other international variables are responsible for the effects, corroborating the findings of

La Porta et al. (1998, 1999a, 1999b, 2001 and 2008) that legal factors are the driving force in varieties of

finance, not necessarily regions per se.

5.6 Robustness and sensitivity tests

To finalize this study, I conducted multiple sensitivity tests similar to the preceding regressions,

additionally including industry as a control variable. I moved the industry controls to robustness due to

the amount of extra variables and their small impact. Adding industry controls does not change the

economic significance of ownership as a predictor for firm performance. In fact, few SIC codes were

actually significantly affecting Tobin's Q, yet as mentioned, without affecting the vital independent

variable, ownership.

Table B1 shows the regression results for the exact same equations as explained in section four,

with Tobin's Q raw values and using simple OLS in the trimmed version. On top, these results also show

that Tobin's Q values from “last available year”, as specified by ORBIS, represents this dataset just like

time­variant analysis using panel data. As mentioned earlier, the type of study does not influence the

results. In the discussion of the results, I also mentioned that some values may be driven by extreme

values stemming from the US and that mostly shareholder rights and creditor rights are affected by this.

Utilizing a trimmed set in this case supports this assumption and turns the ownership variable positive

again for equation (11). Table B2 replicates table 4 again, with the only change being the inclusion of

industry as an added control variable, yet as mentioned earlier, the impact is rather negligible. To see

whether the results are robust if also the extreme values are dropped, I trimmed the panel data to 5% as

well and, again, received the same results, visible in table B3 and B4. While I used the natural logarithm

of Tobin's Q in table B3, the raw version is used in B4. In these scenarios, only equation (11) becomes

insignificant for its main test, creditor rights. Table B5 and B6 then, again, replicate the panel data studies

and use “last available year” values of Tobin's Q in a OLS setup. These tables report the same results as

well. As mentioned, I believed Tobin's Q to be the driver of the values and valuations that underly no

36

actual numbers such as return or physical assets. To test this assumption, I switched the firm performance

proxy in two additional models to the natural logarithm of return on assets (table B7) and return on equity

(table B8). In both models, only one out of sixteen regressions is slightly significant, proving the point

that Tobin's Q is largely responsible for the findings. Additional region specific tests are included in

Appendix­C. Key take­away is that Tobin's Q is important to monitor and understand

37

6 Conclusion and results

I studied young firms that incorporated after the dot­com bubble 2001, from the United States and

Europe, to test whether firms perform better if they are owned by their managers and whether that differs

with the institutional framework that the company is situated in. Very similar to the pre­2001 period,

young and highly funded firms are of popular interest. More than ever, young firms are published in a

diverse set of media with their innovative solutions, intended positive impact and high funding, even

without revenues and largely based on intangible assets. In particular their owners, founders and CEOs

are topic of interest and serve as figurehead for their company. Quite literally, some managing founders

have been watered down to only be a figurehead without real long term power, hence this study tried to

answer the question: What is the effect of having management as majority shareholder(s) on the

performance of the young firm in different environments? To find an answer, I used quantitative data from

Orbis and analyzed it using time­series panel data as well as last year’s values using OLS. To proxy firm

performance, I use information that uses accounting as well as stock market information, namely Tobin's

Q. The sample is comprised of all listed European and US firms that were founded after the

dot­com­bubble (2001) and have available data.

I find that whether firms with managerial or outside owners perform better, depends on the

institutional context. While generally using all firms, there is strong evidence that firms owned by

managers perform better than those who are not, yet there is a good reason to believe that extreme values

from the US are at least partly responsible for the positive effect. Furthermore, there is strong evidence

that common law and strong shareholder protection provides fertile soil for high Tobin's Qs, thus firm

performance in this framework, as already suggested by La Porta et al. (1998, 2001).

I tested several hypotheses. I found support for the notion that there are differences in firm

performance between firms owned by managers and those who are not owned by managers (H1), leading

to findings that managerial ownership leads to better firm performance (H2). Regarding the regional

differences, there are differences between managerial owned firm performances in the US and Europe

(H3), leading to findings that managerial ownership effects are stronger for US firms than European firms

(H4). Stronger and more important was the finding that managerial ownership firm performance was

positively affected by being located in common law countries and negatively affected by being located in

civil law countries, confirming H5. Surprisingly, I found results partly opposing H6, namely negative

effects of creditor rights on firm performance, even though they are rather low and weak. On the other

hand, stronger shareholder rights have a positive effect on firm performance, thus also just partly

38

confirming H6 and providing support to the findings of La Porta et al. (2001). This study contributes to

the literature by giving insights into effects of who owns companies and how this is in turn affected by the

institutional framework, taking the general findings of La Porta et al. (1997, 1998, 1999a, 1999b, 2001

and 2008) to a further detailed level. I moreover confirm most of these findings and provide food for

thought regarding the current situation in market valuation for a very specific type of firms, young firms.

First of all, generally interpreting these findings without looking at a potentially overheating

market makes clearly visible why most firms of the sample are from the United States. As the papers of

La Porta et al. proposed, some regulatory frameworks are better than others for growing a company.

Strong shareholder rights and strong enforcement puts investors in a comfortable position, leading to

more liquid financial markets. As said, the sample is comprised of all listed firms that incorporated after

2001 from the two geographical areas and some countries, such as Germany, are represented much less

compared to their size. Providing fertile soil is probably the magic word and this study shows clearly that

the soil of some countries seems more fertile than others. Looking at the findings closely, it becomes

visible that especially managerial owners are affected stronger than others. In good soil, managerial

owners outperform others, whereas in poor soil, they perform worse. Thus, founders and generally young

companies must choose wisely where to incorporate their firm. In the context of these factors, it then

seems reasonable to assume that the soft factors around stewardship theory and agency theory do play a

major role in motivating managers to outperform their counterpart. Afterall, having ownership and control

aligned is not investor friendly and riskier than being in control as an investor. And still, managerial

owned firms largely outperform non­managerial owned firms.

As a final remark, I would like to note that the findings of Tobin's Q values are rather disturbing.

The extremity of values lets me doubt their sustainability. A large issue of young companies is the amount

of intangible assets included in their valuations, either through acquisitions (goodwill) or direct valuations

with largely venture capital as driver, thus even without true collateral or an illiquid one. These types of

assets have become ever more common and ever more arbitrary in their underlying values. Not without

ulterior motive young firms were defined as post dot­com­bubble incorporated firms and I would not be

surprised if the current hysteria about “finding the new facebook” at all costs ends in the next bubble

burst. Even though this is a time­series study, including all available values for the past 14 years from all

available firms from all countries remotely being considered European and belonging to the United States

and already trimming for extreme outliers in some tests, I am still very sceptical about the height of

Tobin's Qs based in the United States and am not sure if the same results will be found at a later point in

time, after this potential bubble bursts. Generally speaking, investors, young companies and even

unrelated firms need to be careful for the upcoming months and prepare for a meltdown of these

39

valuations. From an investor's point of view, further engaging in venture funding may come costly, if the

current times actually constitute a peak. In that scenario, the same firms that ask for funding may be much

cheaper in the near future, when financing cash flows become more careful and fewer. Under these

circumstances, the very same firms with the very same ideas and products are potentially moving into a

squeeze that forces them to accept down­rounds, namely accept lower valuations. Once this happens, the

firms that were based on the future prospective of reaching scale and market share before profits, fueled

by artificial growth, may be forced to get any sort of funding or die. A famous example is the german

startup conglomerate Rocket Internet, who have been based on the idea of scaling their firms for five to

ten years first, letting their startups grow artificially until they reach a target market share, with a cash

burn rate of almost a billion Euro yearly in the process. If this hypothetical bubble bursts and funding

becomes more risk averse, the pools of money they funded themselves from will run dry and they have to

default by being insolvent. Such firms destroying value so quickly will then lead to fuelling a new

dot­com­bubble. As said, in the end, their only collateral are intangible assets that are valued by belief

only. Maybe there is a reason why billion dollar startups are called “unicorns”. In principle they only exist

when you believe they do. Much like in the crisis time of 1999 to 2001, this crisis could spill over to the

whole market by distrusting the participants of this madness and leading to troubles not only of investors

losing their money and young firms dying, but also other firms being affected by a potential financing

squeeze. On the other hand, these findings close the circle of this study. Choosing the sample frame from

post­dot­com­bubble 2001 till now intended to shed the peak and crisis values, yet may only show that

people haven’t learned anything from superheated markets.

Limitations of this research include the quality of data obtained. This relates to the amount of

outliers, extreme values as well as missing data on unlisted firms. Further, the availability of data may be

a drawback, since Tobin's Q is a market valuation measure of firm performance and may penalize debt

funded firms. Lastly mentionable is that the amounts of firms per country were disproportional to country

size. Further research directions regard on the one hand valuation and on the other how the values of

Tobin's Q keep developing. Being able to appropriately value intangible assets used to be rather

negligible, but with the emergence of increasingly non­physical assets responsible for firm values, it is

time to research valuation of intangible assets, otherwise company valuation moves back in time, if it isn’t

able to account for one of its major new building blocks.

40

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46

8 Appendix­A

Word count: 15792 Table A1

Definition of variables

Variable Unit of Measurement Usage Description

LN Tobins Q Ratio Dependent Variable The natural logarithm of Tobins Q as described below

Tobins Q Natural Logarithm Alternative Dep. Variable

ORBIS' ratio of market capitalization/assets from the last available year

AVG Tobins Q all Ratio average Control Variable Average of all available Tobins Q values per firm

LN AVG Tobins Q all Natural Logarithm Control Variable Natural logarithm of the average of all available Tobins Q values per firm

Ownership Dummy Variable Independent Variable 1 if the firm is owned by outside shareholders (managers <50% ownership) and 0 if managers own company (>50% combined)

Region Dummy Variable Independent Variable 1 if the firm is from the Europe and 0 for companies from the United States

Creditor Rights Categorical Variable Independent Variable Strength of creditor rights as defined by La Porta et al. (1998), ranging from 0 to 4; 0 being high, 4 being low investor protection

Legal Origin Categorical Variable Independent Variable Legal origin of firm country as described in La Porta et al. (1998)

LN Market Cap Natural Logarithm Control Variable Market capitalization of firm

LN Total Assets Natural Logarithm Control Variable Total assets of firm

LN Operating Turnover Natural Logarithm Control Variable Operating turnover

LN Employees Natural Logarithm Control Variable Number of employees in firm (estimation)

LN Managers Natural Logarithm Control Variable Number of managers in firm

LN ROA P/L last Natural Logarithm Control Variable Return on assets using Profit/Loss before tax in the last available year

LN ROE P/L last Natural Logarithm Control Variable Return on equity using Prof/Loss before tax in the last available year

LN ROE NI last Natural Logarithm Control Variable Return on equity using Net Income in the last available year

LN ROA NI last Natural Logarithm Control Variable Return on assets using Net Income in the last available year

SIC Dummy Variable Control Variable Standard Industrial Classification (see Table A1)

Market Cap Continuous Variable Control Variable Raw data of market capitalization of firm

Total Assets Continuous Variable Control Variable Raw data of total assets of firm

Operating Turnover Continuous Variable Control Variable Raw data of operating turnover

Employees Discrete numerical variable Control Variable Raw data of number of employees in firm (estimation)

Managers Discrete numerical variable Control Variable Raw data of number of managers in firm

ROA P/L last Continuous Variable Control Variable Raw data of return on assets using Profit/Loss before tax in the last available year

ROE P/L last Continuous Variable Control Variable Raw data of return on equity using Prof/Loss before tax in the last available year

ROE NI last Continuous Variable Control Variable Raw data of return on equity using Net Income in the last available year

ROA NI last Continuous Variable Control Variable Raw data of return on assets using Net Income in the last available year

47

Table A2

Standard Industrial Classification as used for the industrial control variable

SIC Code Division

1 Agriculture, Forestry, Mining and Construction

2 Manufacturing 1 ­ (Paper, Food, Chemicals and Apparel)

3 Manufacturing 2 ­ (Leather, Metal, Electronics, Rubber and Stone)

4 Communication/Transport

5 Wholesale/Retail

6 Financial Services

7 Travel and Entertainment

8 Services

9 Public Administration Figure A1 The figure depicts the frequency of observations in the panel data sample

48

Table A3

Presented in this table are the countries and the mean, median, maximum and minimum value of Tobin’s Q within as well as the amount of firms. Variables are defined in Table 1.

Country Mean Median Maximum Minium Frequency

Austria 0.86 1.00 1.05 0.37 13

Belgium 1.49 1.04 5.43 0.11 65

Bulgaria 8.92 0.84 78.63 0.07 104

Croatia 0.27 0.27 0.42 0.12 13

Cyprus 0.43 0.30 1.62 0.10 65

Denmark 1.20 1.00 4.36 0.10 78

Finland 1.25 0.70 6.55 0.15 143

France 3.10 1.40 37.44 0.06 1066

France (French Guiana) 0.51 0.29 1.00 0.24 13

Germany 0.99 0.61 9.95 0.00 299

Gibraltar 5.59 4.78 14.82 2.18 26

Hungary 0.62 0.56 1.19 0.27 26

Ireland 1.42 1.04 6.42 0.08 208

Italy 1.34 0.70 11.13 0.10 208

Lithuania 0.68 0.39 3.02 0.14 39

Luxembourg 0.64 0.56 2.25 0.09 65

Macedonia (Fyrom) 0.24 0.25 0.27 0.19 13

Netherlands 1.24 0.67 5.09 0.10 182

Norway 0.96 0.41 6.76 0.32 65

Poland 1.85 0.83 42.75 0.07 2080

Portugal 0.27 0.21 0.82 0.05 39

Romania 0.57 0.36 5.54 0.07 143

Russian Federation 1.19 0.41 20.30 0.00 377

Spain 0.53 0.37 2.56 0.00 221

Sweden 1.68 0.82 19.97 0.07 494

Switzerland 1.29 0.60 18.43 0.09 117

Turkey 0.83 0.49 3.73 0.00 221

United Kingdom 2.12 0.98 348.52 0.00 5525

United States of America 405.73 2.56 468050.00 0.00 13663

USA (Puerto Rico) 1.26 1.14 2.07 0.69 13

49

50

9 Appendix-B

Table B1 Presented in this table are the results of the multiple regression analysis for the full sample of 2005 firms usnign OLS. The dependent variable is Tobin's Q, using the natural raw value, the model shows 𝑄 𝑖,𝑡 . The independent variables are: 1) Ownership as a dummy variable indicating the presence of managers owning >50% (=1) or managers owning <50% (=0), 𝑂𝑊𝑁𝐸𝑅′𝑖 ;2) Region as a variable indicating the region of firm to be the US (=0) or Europe (=1), 𝑅𝐸𝐺𝐼𝑂𝑁′ 𝑖 ; 3) the interaction between Ownership and Region, taking 1 for the interaction of European firms and owned by managerial shareholders, Owner*Region; 4) a dummy variable indicating the legal origin of a firm’s country to be common law (=1) or civil law (=0), 𝐶𝑂𝑀𝑀𝑂𝑁′𝑖;5) a dummy variable indicating the legal origin of a firm’s country to be civil law (=1) or common law (=0), 𝐶𝐼𝑉𝐼𝐿′𝑖;6) LNMarketcap, the natural logarithm of market capitalization, 𝐹𝐼𝑅𝑀𝑆𝐼𝑍𝐸′𝑖,𝑡; 7) LNEmployees the natural logarithm of number of employees, 𝐸𝑀𝑃𝐿𝑂𝑌𝐸𝐸𝑆′𝑖,𝑡; 8) LNManagers, the natural logarithm of number of managers, 𝑀𝐴𝑁𝐴𝐺𝐸𝑅𝑆′𝑖,𝑡; 9) the value of creditor rights as constant variable indicating the creditor rights level of a firm’s country from 0-4 (La Porta et al., 1998), 𝐶𝑅 𝑖; 10) Anti-Director rights indicating the shareholder rights level of a firm’s country from 0-6 (La Porta et al., 1998), 𝐴𝐷𝑅 𝑖; variables use following vectors: 𝛽, column vector of (time-variant) parameters; K-dimensional using 𝑥′ 𝑖,𝑡 as time-variant row vector of explanatory variables and 𝛾, column vector of (time-invariant) parameters; M-dimensional, using 𝑧′ 𝑖 as time-invariant row vector of explanatory variable, where 𝛼 0 is the constant term. Firm performance is constant when all variables seem insignificant, Table 1 provides further definitions, standard errors (𝑐 𝑖 + 𝑢 𝑖,𝑡) are to be found in the brackets. This sample comprises 2005 observations for both US and European firms with a sample period between 2001 and 2015. Statistical significance is denoted with ***, ** and * at the 0.1%, 5% and 10% level, respectively. All regressions use random effects.

Variable (6) (7) (8) (9) (10) (11) (12) (13)

Ownership 10.479*** 19.097*** 12.326*** -2.799 11.299*** 8.984*** -13.474 14.854***

[1.44] [1.92] [1.52] [2.46] [1.87] [2.6] [7.89] [2.14]

Region -11.904*** -8.613*** -1.884

[1.09] [1.19] [3.4]

Owner*Region -19.083*** -16.841***

[2.86] [3.04]

Common Law 7.294* 3.356* 0.892

[3.27] [1.42] [4.06]

Civil Law -3.057 -3.575* -2.084

[3.44] [1.52] [3.01]

LNMarketCap 0.235 0.235 0.39 0.138 0.153

[0.25] [0.25] [0.25] [0.35] [0.25]

LNManagers -5.221*** -5.421*** -6.271*** -5.014** -4.173***

[0.97] [0.95] [1.01] [1.59] [1.05]

LNEmployees -1.235*** -1.217*** -1.225*** -1.672*** -1.209***

[0.29] [0.28] [0.29] [0.43] [0.29]

Ownership*Common 15.019***

[3.1]

Ownership*Civil -14.041***

[3.14]

Creditor Rights -0.075 -0.168

[0.45] [1.06]

Ownership*Creditor -3.063*

[1.54]

Sharholder Rights 0.827

[0.89]

Ownership*Shareholder 5.284**

[1.75]

Constant 11.960*** 10.442*** 1.21 16.048*** 19.682*** 18.539*** 18.768** 18.441***

[0.78] [0.81] [3.21] [3.5] [3.34] [3.43] [6.27] [4.82]

R-sqr 0.08 0.1 0.051 0.129 0.128 0.105 0.128 0.138

dfres 2002 2001 1962 1594 1617 1588 1053 1585

BIC 18510.2 18473.6 18231.3 14340.5 14530.9 14335.6 9915.5 14298.3

51

Table B2 Presented in this table are the results of random effects panel regressions for the full sample of 2005 firms. The dependent variable is Tobin's Q, using the natural logarithm, the model shows 𝐿𝑁𝑄 𝑖,𝑡 . The independent variables are: 1) Ownership as a dummy variable indicating the presence of managers owning >50% (=1) or managers owning <50% (=0), 𝑂𝑊𝑁𝐸𝑅′𝑖 ;2) Region as a variable indicating the region of firm to be the US (=0) or Europe (=1), 𝑅𝐸𝐺𝐼𝑂𝑁′ 𝑖 ; 3) the interaction between Ownership and Region, taking 1 for the interaction of European firms and owned by managerial shareholders, Owner*Region; 4) a dummy variable indicating the legal origin of a firm’s country to be common law (=1) or civil law (=0), 𝐶𝑂𝑀𝑀𝑂𝑁′𝑖;5) a dummy variable indicating the legal origin of a firm’s country to be civil law (=1) or common law (=0), 𝐶𝐼𝑉𝐼𝐿′𝑖;6) LNMarketcap, the natural logarithm of market capitalization, 𝐹𝐼𝑅𝑀𝑆𝐼𝑍𝐸′𝑖,𝑡; 7) LNEmployees the natural logarithm of number of employees, 𝐸𝑀𝑃𝐿𝑂𝑌𝐸𝐸𝑆′𝑖,𝑡; 8) LNManagers, the natural logarithm of number of managers, 𝑀𝐴𝑁𝐴𝐺𝐸𝑅𝑆′𝑖,𝑡; 9) the value of creditor rights as constant variable indicating the creditor rights level of a firm’s country from 0-4 (La Porta et al., 1998), 𝐶𝑅 𝑖; 10) Anti-Director rights indicating the shareholder rights level of a firm’s country from 0-6 (La Porta et al., 1998), 𝐴𝐷𝑅 𝑖11) SIC codes to control for industry, as seen in Table A2; variables use following vectors: 𝛽, column vector of (time-variant) parameters; K-dimensional using 𝑥′ 𝑖,𝑡 as time-variant row vector of explanatory variables and 𝛾, column vector of (time-invariant) parameters; M-dimensional, using 𝑧′ 𝑖 as time-invariant row vector of explanatory variable, where 𝛼 0 is the constant term. Firm performance is constant when all variables seem insignificant, Table 1 provides further definitions, standard errors (𝑐 𝑖 + 𝑢 𝑖,𝑡) are to be found in the brackets. This sample comprises 2005 observations for both US and European firms with a sample period between 2001 and 2015. Statistical significance is denoted with ***, ** and * at the 0.1%, 5% and 10% level, respectively. All regressions use random effects.

Variable (6) (7) (8) (9) (10) (11) (12) (13)

Ownership 0.523*** 1.002*** 0.752*** -0.277 0.496*** 0.217 -0.556 0.650***

[0.09] [0.12] [0.1] [0.15] [0.11] [0.16] [0.41] [0.13]

SIC 1 0.597 0.518 0.513 0.315 0.385 0.474 0.68 0.193

[0.31] [0.3] [0.32] [0.29] [0.29] [0.31] [0.39] [0.29]

SIC 2 0.534 0.48 0.719* 0.779** 0.825** 0.772* 0.925* 0.539

[0.3] [0.3] [0.32] [0.28] [0.28] [0.31] [0.38] [0.29]

SIC 3 0.297 0.244 0.49 0.674* 0.701* 0.597 0.841* 0.451

[0.3] [0.3] [0.32] [0.28] [0.28] [0.31] [0.38] [0.29]

SIC 4 -0.042 -0.103 -0.051 0.214 0.272 0.117 0.342 0.05

[0.32] [0.31] [0.33] [0.29] [0.29] [0.32] [0.4] [0.3]

SIC 5 0.338 0.304 0.427 0.664* 0.707* 0.589 0.820* 0.495

[0.31] [0.31] [0.33] [0.29] [0.29] [0.31] [0.39] [0.3]

SIC 6 -0.441 -0.491 -0.56 -0.617* -0.58 -0.722* -0.688 -0.744*

[0.32] [0.32] [0.34] [0.3] [0.3] [0.33] [0.41] [0.31]

SIC 7 0.663* 0.56 0.759* 0.879** 0.930*** 0.876** 1.162** 0.694*

[0.3] [0.3] [0.32] [0.28] [0.28] [0.3] [0.38] [0.29]

SIC 8 0.708* 0.645* 0.778* 0.704* 0.765** 0.632* 1.052** 0.521

[0.32] [0.31] [0.33] [0.29] [0.29] [0.32] [0.4] [0.3]

SIC 9 0.484 0.442 0.471 0.412 0.492 -0.026 1.308 0.304

[0.72] [0.71] [0.76] [0.67] [0.67] [0.7] [1.46] [0.67]

Region -1.383*** -1.209*** -0.102

[0.07] [0.07] [0.2]

Ownership*Region -1.056*** -0.798***

[0.17] [0.18]

Common Law 0.933*** 0.690*** 0.561*

[0.2] [0.08] [0.24]

Civil Law -0.263 -0.707*** -0.258

[0.21] [0.09] [0.17]

LNMarketCap 0.111*** 0.114*** 0.128*** 0.086*** 0.100***

[0.02] [0.02] [0.02] [0.02] [0.02]

LNManagers -0.527*** -0.565*** -0.631*** -0.401*** -0.383***

[0.06] [0.06] [0.06] [0.08] [0.06]

LNEmployees -0.251*** -0.250*** -0.240*** -0.297*** -0.247***

[0.02] [0.02] [0.02] [0.02] [0.02]

Ownership*Common 0.819***

[0.18]

Ownership*Civil -0.752***

[0.19]

Creditor Rights -0.05 -0.118

[0.03] [0.06]

Ownership*Creditor -0.132

[0.09]

Sharholder Rights 0.235***

[0.05]

Ownership*Shareholder 0.210*

[0.09]

Constant 0.933** 0.917** -0.488 -0.113 0.525 0.451 -0.354 0.313

[0.3] [0.29] [0.36] [0.33] [0.33] [0.36] [0.47] [0.38]

Sigma_e 1.1 1.1 1.1 1.1 1.1 1.1 1.2 1.1

rho 0.6 0.6 0.6 0.5 0.5 0.5 0.4 0.5

52

Table B3 Presented in this table are the results of random effects panel regressions for the full sample of 2005 firms. The dependent variable is Tobin's Q, the model shows 𝑄 𝑖,𝑡 . The independent variables are: 1) Ownership as a dummy variable indicating the presence of managers owning >50% (=1) or managers owning <50% (=0), 𝑂𝑊𝑁𝐸𝑅′𝑖 ;2) Region as a variable indicating the region of firm to be the US (=0) or Europe (=1), 𝑅𝐸𝐺𝐼𝑂𝑁′ 𝑖 ; 3) the interaction between Ownership and Region, taking 1 for the interaction of European firms and owned by managerial shareholders, Owner*Region; 4) a dummy variable indicating the legal origin of a firm’s country to be common law (=1) or civ il law (=0), 𝐶𝑂𝑀𝑀𝑂𝑁′𝑖;5) a dummy variable indicating the legal origin of a firm’s country to be civil law (=1) or common law (=0), 𝐶𝐼𝑉𝐼𝐿′𝑖;6) LNMarketcap, the natural logarithm of market capitalization, 𝐹𝐼𝑅𝑀𝑆𝐼𝑍𝐸′𝑖,𝑡 ; 7) LNEmployees the natural logarithm of number of employees, 𝐸𝑀𝑃𝐿𝑂𝑌𝐸𝐸𝑆′𝑖,𝑡; 8) LNManagers, the natural logarithm of number of managers, 𝑀𝐴𝑁𝐴𝐺𝐸𝑅𝑆′𝑖,𝑡; 9) the value of creditor rights as constant variable indicating the creditor rights level of a firm’s country from 0-4 (La Porta et al., 1998), 𝐶𝑅 𝑖; 10) Anti-Director rights indicating the shareholder rights level of a firm’s country from 0-6 (La Porta et al., 1998), 𝐴𝐷𝑅 𝑖11) SIC codes to control for industry, as seen in Table A2; variables use following vectors: 𝛽, column vector of (time-variant) parameters; K-dimensional using 𝑥′ 𝑖,𝑡 as time-variant row vector of explanatory variables and 𝛾, column vector of (time-invariant) parameters; M-dimensional, using 𝑧′ 𝑖 as time-invariant row vector of explanatory variable, where 𝛼 0 is the constant term. Firm performance is constant when all variables seem insignificant, Table 1 provides further definitions, standard errors (𝑐 𝑖 + 𝑢 𝑖,𝑡) are to be found in the brackets. This sample comprises 2005 observations for both US and European firms with a sample period between 2001 and 2015. Statistical significance is denoted with ***, ** and * at the 0.1%, 5% and 10% level, respectively. All regressions use random effects.

Variable (6) (7) (8) (9) (10) (11) (12) (13)

Ownership 2.724*** 5.170*** 3.294*** -1.325 2.699*** 1.047 -3.068 3.397***

[0.49] [0.67] [0.52] [0.78] [0.62] [0.84] [2.4] [0.72]

SIC 1 1.866 1.502 1.542 0.139 0.381 0.784 0.384 -0.129

[1.66] [1.65] [1.71] [1.55] [1.56] [1.63] [2.29] [1.58]

SIC 2 1.404 1.176 2.032 1.752 2.031 1.733 1.394 0.961

[1.65] [1.64] [1.69] [1.52] [1.53] [1.6] [2.22] [1.55]

SIC 3 0.592 0.352 1.292 1.731 1.79 1.482 1.746 1.011

[1.66] [1.64] [1.7] [1.52] [1.54] [1.61] [2.23] [1.56]

SIC 4 -0.393 -0.679 -0.608 0.189 0.363 -0.071 -0.084 -0.292

[1.71] [1.7] [1.76] [1.58] [1.59] [1.67] [2.36] [1.61]

SIC 5 0.885 0.733 1.176 1.743 1.914 1.526 1.964 1.316

[1.69] [1.68] [1.74] [1.56] [1.57] [1.64] [2.29] [1.59]

SIC 6 -1.383 -1.59 -1.943 -2.364 -2.295 -2.66 -3.288 -2.735

[1.73] [1.72] [1.77] [1.62] [1.64] [1.71] [2.38] [1.65]

SIC 7 1.354 0.89 1.766 1.803 1.93 1.85 2.034 1.255

[1.64] [1.63] [1.68] [1.51] [1.52] [1.6] [2.22] [1.54]

SIC 8 2.243 1.949 2.338 1.745 1.88 1.557 2.618 1.204

[1.71] [1.7] [1.76] [1.58] [1.59] [1.67] [2.33] [1.61]

SIC 9 0.322 0.132 -0.118 -0.692 -0.441 -2.237 0.03 -0.803

[3.88] [3.85] [3.98] [3.57] [3.61] [3.68] [8.36] [3.57]

Region -5.662*** -4.850*** -1.82

[0.36] [0.39] [1.07]

Ownership*Region -5.071*** -4.249***

[0.97] [0.98]

Common Law 3.713*** 2.159*** 0.786

[1.07] [0.45] [1.27]

Civil Law -0.471 -2.119*** -0.571

[1.13] [0.49] [0.93]

LNMarketCap 0.299*** 0.331*** 0.358*** 0.250* 0.251**

[0.08] [0.08] [0.08] [0.11] [0.08]

LNManagers -2.092*** -2.298*** -2.415*** -1.508** -1.397***

[0.31] [0.31] [0.33] [0.49] [0.34]

LNEmployees -0.931*** -0.936*** -0.893*** -1.226*** -0.926***

[0.1] [0.1] [0.1] [0.14] [0.09]

Ownership*Common 4.048***

[1]

Ownership*Civil -4.041***

[1.02]

Creditor Rights -0.259 -0.068

[0.14] [0.33]

Ownership*Creditor -0.507

[0.48]

Sharholder Rights 0.831**

[0.27]

Ownership*Shareholder 1.134*

[0.54]

Constant 6.255*** 6.161*** 0.445 4.947** 6.792*** 6.732*** 4.233 6.888***

[1.61] [1.59] [1.9] [1.8] [1.8] [1.87] [2.77] [2.04]

Sigma_e 5.9 5.9 5.8 5.2 5.3 5.3 6.5 5.3

rho 0.6 0.6 0.6 0.6 0.6 0.6 0.5 0.6

53

Table B4 Presented in this table are the results of random effects panel regressions for the full sample of 2005 firms. The dependent variable is Tobin's Q, using the natural logarithm, the model shows 𝐿𝑁𝑄 𝑖,𝑡 . The independent variables are: 1) Ownership as a dummy variable indicating the presence of managers owning >50% (=1) or managers owning <50% (=0), 𝑂𝑊𝑁𝐸𝑅′𝑖 ;2) Region as a variable indicating the region of firm to be the US (=0) or Europe (=1), 𝑅𝐸𝐺𝐼𝑂𝑁′ 𝑖 ; 3) the interaction between Ownership and Region, taking 1 for the interaction of European firms and owned by managerial shareholders, Owner*Region; 4) a dummy variable indicating the legal origin of a firm’s country to be common law (=1) or civil law (=0), 𝐶𝑂𝑀𝑀𝑂𝑁′𝑖;5) a dummy variable indicating the legal origin of a firm’s country to be civil law (=1) or common law (=0), 𝐶𝐼𝑉𝐼𝐿′𝑖;6) LNMarketcap, the natural logarithm of market capitalization, 𝐹𝐼𝑅𝑀𝑆𝐼𝑍𝐸′𝑖,𝑡; 7) LNEmployees the natural logarithm of number of employees, 𝐸𝑀𝑃𝐿𝑂𝑌𝐸𝐸𝑆′𝑖,𝑡; 8) LNManagers, the natural logarithm of number of managers, 𝑀𝐴𝑁𝐴𝐺𝐸𝑅𝑆′𝑖,𝑡; 9) the value of creditor rights as constant variable indicating the creditor rights level of a firm’s country from 0-4 (La Porta et al., 1998), 𝐶𝑅 𝑖; 10) Anti-Director rights indicating the shareholder rights level of a firm’s country from 0-6 (La Porta et al., 1998), 𝐴𝐷𝑅 𝑖11) SIC codes to control for industry, as seen in Table A2; variables use following vectors: 𝛽, column vector of (time-variant) parameters; K-dimensional using 𝑥′ 𝑖,𝑡 as time-variant row vector of explanatory variables and 𝛾, column vector of (time-invariant) parameters; M-dimensional, using 𝑧′ 𝑖 as time-invariant row vector of explanatory variable, where 𝛼 0 is the constant term. Firm performance is constant when all variables seem insignificant, Table 1 provides further definitions, standard errors (𝑐 𝑖 + 𝑢 𝑖,𝑡) are to be found in the brackets. This sample comprises 2005 observations for both US and European firms with a sample period between 2001 and 2015. Statistical significance is denoted with ***, ** and * at the 0.1%, 5% and 10% level, respectively. All regressions use random effects.

Variable (6) (7) (8) (9) (10) (11) (12) (13)

Ownership 0.180* 0.422*** 0.324*** -0.18 0.195* -0.025 -0.18 0.252*

[0.07] [0.1] [0.07] [0.12] [0.09] [0.13] [0.31] [0.11]

SIC 1 0.338 0.303 0.258 0.022 0.086 0.148 0.256 -0.074

[0.24] [0.24] [0.25] [0.23] [0.23] [0.24] [0.3] [0.23]

SIC 2 0.563* 0.540* 0.678** 0.615** 0.655** 0.613* 0.774** 0.434

[0.24] [0.24] [0.24] [0.22] [0.22] [0.24] [0.29] [0.23]

SIC 3 0.323 0.299 0.445 0.531* 0.556* 0.480* 0.724* 0.363

[0.24] [0.24] [0.25] [0.22] [0.22] [0.24] [0.29] [0.23]

SIC 4 -0.071 -0.099 -0.091 0.032 0.081 -0.038 0.152 -0.092

[0.25] [0.25] [0.25] [0.23] [0.23] [0.25] [0.31] [0.24]

SIC 5 0.363 0.348 0.42 0.584* 0.619** 0.535* 0.780** 0.448

[0.24] [0.24] [0.25] [0.23] [0.23] [0.25] [0.3] [0.23]

SIC 6 -0.418 -0.438 -0.495 -0.685** -0.653** -0.756** -0.746* -0.779**

[0.25] [0.25] [0.26] [0.24] [0.24] [0.26] [0.31] [0.24]

SIC 7 0.546* 0.501* 0.604* 0.646** 0.692** 0.638** 0.884** 0.506*

[0.24] [0.24] [0.24] [0.22] [0.22] [0.24] [0.29] [0.23]

SIC 8 0.655** 0.626* 0.690** 0.540* 0.589* 0.489* 0.862** 0.399

[0.25] [0.25] [0.26] [0.23] [0.23] [0.25] [0.3] [0.24]

SIC 9 0.45 0.431 0.455 0.365 0.43 0.064 1.213 0.261

[0.56] [0.56] [0.58] [0.53] [0.53] [0.55] [1.09] [0.53]

Region -0.910*** -0.830*** 0.069

[0.05] [0.06] [0.16]

Ownership*Region -0.500*** -0.337*

[0.14] [0.15]

Common Law 0.634*** 0.553*** 0.595**

[0.15] [0.07] [0.19]

Civil Law -0.175 -0.564*** -0.182

[0.16] [0.07] [0.14]

LNMarketCap 0.116*** 0.120*** 0.128*** 0.092*** 0.109***

[0.01] [0.01] [0.01] [0.01] [0.01]

LNManagers -0.294*** -0.325*** -0.366*** -0.168** -0.195***

[0.05] [0.05] [0.05] [0.06] [0.05]

LNEmployees -0.229*** -0.228*** -0.218*** -0.265*** -0.226***

[0.01] [0.01] [0.01] [0.02] [0.01]

Ownership*Common 0.390**

[0.15]

Ownership*Civil -0.349*

[0.15]

Creditor Rights -0.039 -0.133**

[0.02] [0.05]

Ownership*Creditor -0.056

[0.07]

Sharholder Rights 0.203***

[0.03]

Ownership*Shareholder 0.068

[0.07]

Constant 0.566* 0.557* -0.385 -0.714** -0.215 -0.271 -1.002** -0.479

[0.23] [0.23] [0.28] [0.27] [0.26] [0.28] [0.36] [0.3]

Sigma_e 0.8 0.8 0.8 0.8 0.8 0.8 0.9 0.8

rho 0.6 0.6 0.6 0.5 0.5 0.5 0.5 0.5

54

Table B5 Presented in this table are the results of random effects panel regressions for the full sample of 2005 firm, trimmed to 5% on both tails. The dependent variable is Tobin's Q, using the natural logarithm, the model shows 𝐿𝑁𝑄 𝑖,𝑡 . The independent variables are: 1) Ownership as a dummy variable indicating the presence of managers owning >50% (=1) or managers owning <50% (=0), 𝑂𝑊𝑁𝐸𝑅′𝑖 ;2) Region as a variable indicating the region of firm to be the US (=0) or Europe (=1), 𝑅𝐸𝐺𝐼𝑂𝑁′ 𝑖 ; 3) the interaction between Ownership and Region, taking 1 for the interaction of European firms and owned by managerial shareholders, Owner*Region; 4) a dummy variable indicating the legal origin of a firm’s country to be common law (=1) or civil law (=0), 𝐶𝑂𝑀𝑀𝑂𝑁′𝑖;5) a dummy variable indicating the legal origin of a firm’s country to be civil law (=1) or common law (=0), 𝐶𝐼𝑉𝐼𝐿′𝑖;6) LNMarketcap, the natural logarithm of market capitalization, 𝐹𝐼𝑅𝑀𝑆𝐼𝑍𝐸′𝑖,𝑡; 7) LNEmployees the natural logarithm of number of employees, 𝐸𝑀𝑃𝐿𝑂𝑌𝐸𝐸𝑆′𝑖,𝑡; 8) LNManagers, the natural logarithm of number of managers, 𝑀𝐴𝑁𝐴𝐺𝐸𝑅𝑆′𝑖,𝑡; 9) the value of creditor rights as constant variable indicating the creditor rights level of a firm’s country from 0 -4 (La Porta et al., 1998), 𝐶𝑅 𝑖; 10) Anti-Director rights indicating the shareholder rights level of a firm’s country from 0-6 (La Porta et al., 1998), 𝐴𝐷𝑅 𝑖11) SIC codes to control for industry, as seen in Table A2; variables use following vectors: 𝛽, column vector of (time-variant) parameters; K-dimensional using 𝑥′ 𝑖,𝑡 as time-variant row vector of explanatory variables and 𝛾, column vector of (time-invariant) parameters; M-dimensional, using 𝑧′ 𝑖 as time-invariant row vector of explanatory variable, where 𝛼 0 is the constant term. Firm performance is constant when all variables seem insignificant, Table 1 provides further definitions, standard errors (𝑐 𝑖 + 𝑢 𝑖,𝑡) are to be found in the brackets. This sample comprises 2005 observations for both US and European firms with a sample period between 2001 and 2015. Statistical significance is denoted with ***, ** and * at the 0.1%, 5% and 10% level, respectively. All regressions use random effects.

Variable (6) (7) (8) (9) (10) (11) (12) (13)

Ownership 0.464*** 0.970*** 0.614*** -0.26 0.461*** 0.411** -0.832* 0.612***

[0.08] [0.11] [0.09] [0.14] [0.11] [0.15] [0.41] [0.12]

SIC 1 0.497 0.417 0.44 0.127 0.166 0.167 0.525 -0.018

[0.3] [0.3] [0.31] [0.29] [0.29] [0.31] [0.4] [0.3]

SIC 2 0.811** 0.757* 0.933** 0.749** 0.775** 0.698* 0.915* 0.546

[0.3] [0.3] [0.31] [0.29] [0.29] [0.3] [0.39] [0.3]

SIC 3 0.56 0.506 0.689* 0.701* 0.714* 0.595 0.867* 0.506

[0.3] [0.3] [0.31] [0.29] [0.29] [0.31] [0.39] [0.3]

SIC 4 0.252 0.191 0.23 0.254 0.3 0.125 0.367 0.097

[0.31] [0.31] [0.32] [0.3] [0.3] [0.32] [0.41] [0.31]

SIC 5 0.543 0.513 0.618* 0.678* 0.685* 0.564 0.773 0.507

[0.31] [0.3] [0.31] [0.3] [0.3] [0.31] [0.4] [0.3]

SIC 6 -0.13 -0.176 -0.207 -0.572 -0.544 -0.695* -0.642 -0.681*

[0.32] [0.31] [0.32] [0.31] [0.31] [0.32] [0.42] [0.31]

SIC 7 0.782** 0.675* 0.849** 0.808** 0.840** 0.750* 1.041** 0.627*

[0.3] [0.3] [0.31] [0.29] [0.29] [0.3] [0.39] [0.29]

SIC 8 0.929** 0.864** 0.972** 0.799** 0.843** 0.693* 1.144** 0.617*

[0.31] [0.31] [0.32] [0.3] [0.3] [0.31] [0.4] [0.31]

SIC 9 0.966 0.92 1.022 0.985 1.055 0.599 1.414 0.827

[0.7] [0.69] [0.72] [0.67] [0.67] [0.69] [1.36] [0.67]

Region -0.958*** -0.767*** 0.267

[0.07] [0.07] [0.2]

Ownership*Region -1.119*** -0.810***

[0.17] [0.18]

Common Law 0.445* 0.432*** 0.436

[0.19] [0.08] [0.24]

Civil Law -0.452* -0.491*** -0.369*

[0.2] [0.09] [0.17]

LNMarketCap 0.152*** 0.152*** 0.166*** 0.128*** 0.147***

[0.02] [0.01] [0.02] [0.02] [0.02]

LNManagers -0.413*** -0.429*** -0.504*** -0.375*** -0.356***

[0.06] [0.06] [0.06] [0.08] [0.06]

LNEmployees -0.252*** -0.249*** -0.244*** -0.273*** -0.247***

[0.02] [0.02] [0.02] [0.02] [0.02]

Ownership*Common 0.735***

[0.18]

Ownership*Civil -0.670***

[0.18]

Creditor Rights -0.002 -0.146*

[0.03] [0.06]

Ownership*Creditor -0.254**

[0.09]

Sharholder Rights 0.102*

[0.05]

Ownership*Shareholder 0.270**

[0.09]

Constant 0.3 0.278 -0.471 -1.079** -0.656 -0.730* -0.906 -0.775*

[0.29] [0.29] [0.34] [0.34] [0.34] [0.35] [0.48] [0.38]

R-sqr 0.156 0.175 0.115 0.29 0.292 0.257 0.308 0.297

dfres 1993 1992 1953 1585 1608 1579 1044 1576

BIC 7182.8 7146.3 7129.1 5274.6 5347.1 5323.1 3662.2 5256.7

55

Table B6 Presented in this table are the results of the multiple regression analysis for the full sample of 2005 firms, trimmed to 5% on both tails. The dependent variable is Tobin's Q, the model shows 𝑄 𝑖,𝑡. The independent variables are: 1) Ownership as a dummy variable indicating the presence of managers owning >50% (=1) or managers owning <50% (=0), 𝑂𝑊𝑁𝐸𝑅′𝑖 ;2) Region as a variable indicating the region of firm to be the US (=0) or Europe (=1), 𝑅𝐸𝐺𝐼𝑂𝑁′ 𝑖 ; 3) the interaction between Ownership and Region, taking 1 for the interaction of European firms and owned by managerial shareholders, Owner*Region; 4) a dummy variable indicating the legal origin of a firm’s country to be common law (=1) or civil law (=0), 𝐶𝑂𝑀𝑀𝑂𝑁′𝑖;5) a dummy variable indicating the legal origin of a firm’s country to be civil law (=1) or common law (=0), 𝐶𝐼𝑉𝐼𝐿′𝑖;6) LNMarketcap, the natural logarithm of market capitalization, 𝐹𝐼𝑅𝑀𝑆𝐼𝑍𝐸′𝑖,𝑡; 7) LNEmployees the natural logarithm of number of employees, 𝐸𝑀𝑃𝐿𝑂𝑌𝐸𝐸𝑆′𝑖,𝑡; 8) LNManagers, the natural logarithm of number of managers, 𝑀𝐴𝑁𝐴𝐺𝐸𝑅𝑆′𝑖,𝑡; 9) the value of creditor rights as constant variable indicating the creditor rights level of a firm’s country from 0-4 (La Porta et al., 1998), 𝐶𝑅 𝑖; 10) Anti-Director rights indicating the shareholder rights level of a firm’s country from 0-6 (La Porta et al., 1998), 𝐴𝐷𝑅 𝑖11) SIC codes to control for industry, as seen in Table A2; variables use following vectors: 𝛽, column vector of (time-variant) parameters; K-dimensional using 𝑥′ 𝑖,𝑡 as time-variant row vector of explanatory variables and 𝛾, column vector of (time-invariant) parameters; M-dimensional, using 𝑧′ 𝑖 as time-invariant row vector of explanatory variable, where 𝛼 0 is the constant term. Firm performance is constant when all variables seem insignificant, Table 1 provides further definitions, standard errors (𝑐 𝑖 + 𝑢 𝑖,𝑡) are to be found in the brackets. This sample comprises 2005 observations for both US and European firms with a sample period between 2001 and 2015. Statistical significance is denoted with ***, ** and * at the 0.1%, 5% and 10% level, respectively. All regressions use random effects.

Variable (6) (7) (8) (9) (10) (11) (12) (13)

Ownership 10.717*** 19.141*** 12.425*** -2.436 11.392*** 9.048*** -12.437 14.831***

[1.44] [1.93] [1.52] [2.47] [1.88] [2.62] [7.95] [2.16]

SIC 1 8.428 7.102 7.935 6.348 6.557 7.099 8.96 5.315

[5.19] [5.14] [5.3] [5.1] [5.06] [5.29] [7.84] [5.22]

SIC 2 3.875 2.973 5.337 4.888 5.103 4.689 4.58 3.03

[5.14] [5.09] [5.24] [5] [4.97] [5.21] [7.64] [5.14]

SIC 3 2.669 1.779 4.273 4.074 4.067 3.264 3.62 2.339

[5.15] [5.1] [5.26] [5.01] [4.98] [5.23] [7.65] [5.15]

SIC 4 3.794 2.788 3.686 4.191 4.396 3.332 4.408 2.832

[5.34] [5.29] [5.46] [5.2] [5.17] [5.42] [8.09] [5.33]

SIC 5 3.458 2.951 4.117 3.468 3.528 2.622 3.055 2.322

[5.26] [5.21] [5.38] [5.11] [5.08] [5.33] [7.86] [5.26]

SIC 6 2.233 1.48 1.11 0.732 0.819 -0.342 0.495 -0.296

[5.39] [5.34] [5.5] [5.33] [5.3] [5.53] [8.16] [5.44]

SIC 7 5.203 3.428 5.982 5.38 5.491 5.358 6.13 3.822

[5.11] [5.07] [5.22] [4.97] [4.94] [5.18] [7.63] [5.11]

SIC 8 6.587 5.5 7.341 7.062 6.981 6.435 9.27 5.551

[5.31] [5.26] [5.43] [5.17] [5.14] [5.39] [7.93] [5.3]

SIC 9 4.051 3.286 3.886 2.244 2.599 -1.701 3.336 1.485

[11.96] [11.84] [12.22] [11.58] [11.54] [11.81] [26.59] [11.65]

Region -11.778*** -8.601*** -2.552

[1.11] [1.2] [3.43]

Ownership*Region -18.629*** -16.419***

[2.88] [3.06]

Common Law 6.735* 2.960* -0.085

[3.28] [1.45] [4.1]

Civil Law -3.036 -3.155* -2.109

[3.45] [1.55] [3.02]

LNMarketCap 0.321 0.316 0.475 0.224 0.24

[0.26] [0.26] [0.26] [0.36] [0.26]

LNManagers -5.473*** -5.666*** -6.468*** -5.233** -4.394***

[0.99] [0.97] [1.02] [1.61] [1.07]

LNEmployees -1.212*** -1.185*** -1.169*** -1.584*** -1.180***

[0.3] [0.3] [0.31] [0.46] [0.3]

Ownership*Common 14.736***

[3.11]

Ownership*Civil -13.801***

[3.15]

Creditor Rights -0.161 -0.007

[0.46] [1.07]

Ownership*Creditor -2.785

[1.54]

Sharholder Rights 0.643

[0.9]

Ownership*Shareholder 5.093**

[1.76]

Constant 7.28 6.909 -3.594 10.592 13.765* 12.996* 13.091 14.837*

[5.01] [4.96] [5.88] [5.86] [5.78] [6.01] [9.38] [6.67]

R-sqr 0.086 0.105 0.056 0.133 0.133 0.112 0.135 0.142

dfres 1993 1992 1953 1585 1608 1579 1044 1576

BIC 18565.7 18531.5 18288.9 14398.8 14589 14389.5 9969.6 14357.4

56

Table B7 Presented in this table are the results of the multiple regression analysis for the full sample of 2005 firms. The dependent variable is LNROA_P_L_last, using the natural logarithm of return on assets with the profit/loss method of last year., the model shows 𝐿𝑁𝑄 𝑖,𝑡 . The dinependent variables are: 1) Ownership as a dummy variable indicating the presence of managers owning >50% (=1) or managers owning <50% (=0), 𝑂𝑊𝑁𝐸𝑅′𝑖 ;2) Region as a variable indicating the region of firm to be the US (=0) or Europe (=1), 𝑅𝐸𝐺𝐼𝑂𝑁′ 𝑖 ; 3) the interaction between Ownership and Region, taking 1 for the interaction of European firms and owned by managerial shareholders, Owner*Region; 4) a dummy variable indicating the legal origin of a firm’s country to be common law (=1) or civil law (=0), 𝐶𝑂𝑀𝑀𝑂𝑁′𝑖;5) a dummy variable indicating the legal origin of a firm’s country to be civil law (=1) or common law (=0), 𝐶𝐼𝑉𝐼𝐿′𝑖;6) LNMarketcap, the natural logarithm of market capitalization, 𝐹𝐼𝑅𝑀𝑆𝐼𝑍𝐸′𝑖,𝑡 ; 7) LNEmployees the natural logarithm of number of employees, 𝐸𝑀𝑃𝐿𝑂𝑌𝐸𝐸𝑆′𝑖,𝑡; 8) LNManagers, the natural logarithm of number of managers, 𝑀𝐴𝑁𝐴𝐺𝐸𝑅𝑆′𝑖,𝑡; 9) the value of creditor rights as constant variable indicating the creditor rights level of a firm’s country from 0 -4 (La Porta et al., 1998), 𝐶𝑅 𝑖; 10) Anti-Director rights indicating the shareholder rights level of a firm’s country from 0-6 (La Porta et al., 1998), 𝐴𝐷𝑅 𝑖11) SIC codes to control for industry, as seen in Table A2; variables use following vectors: 𝛽, column vector of (time-variant) parameters; K-dimensional using 𝑥′ 𝑖,𝑡 as time-variant row vector of explanatory variables and 𝛾, column vector of (time-invariant) parameters; M-dimensional, using 𝑧′ 𝑖 as time-invariant row vector of explanatory variable, where 𝛼 0 is the constant term. Firm performance is constant when all variables seem insignificant, Table 1 provides further definitions, standard errors (𝑐 𝑖 +𝑢 𝑖,𝑡) are to be found in the brackets. This sample comprises 2005 observations for both US and European firms with a sample period between 2001 and 2015. Statistical significance is denoted with ***, ** and * at the 0.1%, 5% and 10% level, respectively. All regressions use random effects.

Variable (6) (7) (8) (9) (10) (11) (12) (13)

Ownership -0.242 0.289 -0.179 -0.388* 0.034 -0.357 -1.297* -0.291

[0.13] [0.36] [0.14] [0.18] [0.24] [0.24] [0.56] [0.42]

SIC 1 -0.19 -0.176 -0.142 -0.705 -0.66 -0.65 0.039 -0.599

[0.56] [0.56] [0.56] [0.59] [0.59] [0.59] [1.04] [0.6]

SIC 2 0.113 0.12 0.167 -0.415 -0.372 -0.39 -0.51 -0.35

[0.52] [0.52] [0.53] [0.54] [0.54] [0.54] [0.86] [0.55]

SIC 3 0.094 0.086 0.121 -0.584 -0.552 -0.538 -0.499 -0.505

[0.52] [0.52] [0.52] [0.54] [0.53] [0.54] [0.86] [0.55]

SIC 4 -0.097 -0.097 -0.017 -0.663 -0.593 -0.636 -0.402 -0.568

[0.52] [0.52] [0.53] [0.55] [0.54] [0.55] [0.88] [0.55]

SIC 5 -0.042 -0.022 0 -0.615 -0.565 -0.573 -0.822 -0.525

[0.52] [0.52] [0.53] [0.54] [0.54] [0.54] [0.88] [0.55]

SIC 6 -0.012 -0.015 0.041 -0.761 -0.719 -0.732 -0.957 -0.687

[0.52] [0.52] [0.53] [0.55] [0.54] [0.55] [0.9] [0.56]

SIC 7 0.207 0.205 0.252 -0.356 -0.299 -0.333 -0.234 -0.277

[0.51] [0.51] [0.52] [0.53] [0.53] [0.53] [0.86] [0.54]

SIC 8 -0.017 -0.026 0.028 -0.543 -0.492 -0.508 -0.435 -0.462

[0.53] [0.53] [0.54] [0.55] [0.55] [0.55] [0.92] [0.56]

SIC 9 0.396 0.4 0.522 -0.054 0.005 -0.042 0 0.018

[0.79] [0.79] [0.8] [0.85] [0.84] [0.85] [0.86]

Region -0.018 0.073 -0.112

[0.14] [0.15] [0.3]

Ownership*Region -0.608 0.045

[0.38] [0.45]

Common Law -0.074 0.12 -0.142

[0.23] [0.13] [0.32]

Civil Law -0.28 -0.157 -0.258

[0.24] [0.14] [0.23]

LNMarketCap 0.052 0.049 0.055 -0.015 0.052

[0.03] [0.03] [0.03] [0.05] [0.03]

LNManagers -0.157 -0.16 -0.175 -0.435* -0.161

[0.11] [0.11] [0.12] [0.21] [0.12]

LNEmployees -0.01 -0.011 -0.004 0.051 -0.015

[0.03] [0.03] [0.03] [0.05] [0.03]

Ownership*Common 0.371

[0.31]

Ownership*Civil -0.444

[0.3]

Creditor Rights 0.045 0.051

[0.04] [0.08]

Ownership*Creditor 0.036

[0.11]

Sharholder Rights -0.071

[0.08]

Ownership*Shareholder 0.256

[0.16]

Constant 1.644** 1.568** 1.712** 1.689* 1.841** 1.576* 3.619*** 1.871**

[0.51] [0.52] [0.52] [0.66] [0.67] [0.67] [1.04] [0.72]

R-sqr 0.016 0.02 0.023 0.049 0.053 0.044 0.111 0.049

dfres 573 572 563 461 467 458 175 455

BIC 1964.1 1967.9 1942.5 1577.9 1589.7 1573.2 663.4 1589.5

57

Table B8 Presented in this table are the results of the multiple regression analysis for the full sample of 2005 firms. The dependent variable is LNROE_P_L_last, using the natural logarithm of return on equity with the profit/loss method of last year., the model shows 𝐿𝑁𝑄 𝑖,𝑡. The independent variables are: 1) Ownership as a dummy variable indicating the presence of managers owning >50% (=1) or managers owning <50% (=0), 𝑂𝑊𝑁𝐸𝑅′𝑖 ;2) Region as a variable indicating the region of firm to be the US (=0) or Europe (=1), 𝑅𝐸𝐺𝐼𝑂𝑁′ 𝑖 ; 3) the interaction between Ownership and Region, taking 1 for the interaction of European firms and owned by managerial shareholders, Owner*Region; 4) a dummy variable indicating the legal origin of a firm’s country to be common law (=1) or civil law (=0), 𝐶𝑂𝑀𝑀𝑂𝑁′𝑖;5) a dummy variable indicating the legal origin of a firm’s country to be civil law (=1) or common law (=0), 𝐶𝐼𝑉𝐼𝐿′𝑖;6) LNMarketcap, the natural logarithm of market capitalization, 𝐹𝐼𝑅𝑀𝑆𝐼𝑍𝐸′𝑖,𝑡 ; 7) LNEmployees the natural logarithm of number of employees, 𝐸𝑀𝑃𝐿𝑂𝑌𝐸𝐸𝑆′𝑖,𝑡; 8) LNManagers, the natural logarithm of number of managers, 𝑀𝐴𝑁𝐴𝐺𝐸𝑅𝑆′𝑖,𝑡; 9) the value of creditor rights as constant variable indicating the creditor rights level of a firm’s country from 0-4 (La Porta et al., 1998), 𝐶𝑅 𝑖; 10) Anti-Director rights indicating the shareholder rights level of a firm’s country from 0-6 (La Porta et al., 1998), 𝐴𝐷𝑅 𝑖11) SIC codes to control for industry, as seen in Table A2; variables use following vectors: 𝛽, column vector of (time-variant) parameters; K-dimensional using 𝑥′ 𝑖,𝑡 as time-variant row vector of explanatory variables and 𝛾, column vector of (time-invariant) parameters; M-dimensional, using 𝑧′ 𝑖 as time-invariant row vector of explanatory variable, where 𝛼 0 is the constant term. Firm performance is constant when all variables seem insignificant, Table 1 provides further definitions, standard errors (𝑐 𝑖 +𝑢 𝑖,𝑡) are to be found in the brackets. This sample comprises 2005 observations for both US and European firms with a sample period between 2001 and 2015. Statistica l significance is denoted with ***, ** and * at the 0.1%, 5% and 10% level, respectively. All regressions use random effects.

Variable (6) (7) (8) (9) (10) (11) (12) (13)

Ownership -0.184 0.625 -0.147 -0.244 0.332 -0.134 -1.009 0.209

[0.13] [0.42] [0.14] [0.18] [0.26] [0.24] [0.55] [0.47]

SIC 1 -0.153 -0.137 -0.067 -0.549 -0.603 -0.557 0.347 -0.461

[0.57] [0.56] [0.57] [0.59] [0.59] [0.59] [1.02] [0.6]

SIC 2 0.063 0.078 0.136 -0.396 -0.411 -0.436 -0.241 -0.37

[0.53] [0.53] [0.53] [0.54] [0.54] [0.54] [0.84] [0.55]

SIC 3 0.127 0.113 0.204 -0.46 -0.496 -0.486 -0.004 -0.442

[0.53] [0.53] [0.53] [0.54] [0.53] [0.54] [0.84] [0.54]

SIC 4 0.124 0.119 0.207 -0.419 -0.426 -0.429 0.234 -0.36

[0.53] [0.53] [0.54] [0.55] [0.54] [0.55] [0.86] [0.55]

SIC 5 -0.025 -0.002 0.059 -0.557 -0.593 -0.602 -0.423 -0.517

[0.53] [0.53] [0.54] [0.54] [0.54] [0.54] [0.86] [0.55]

SIC 6 -0.099 -0.107 -0.02 -0.556 -0.596 -0.581 -0.179 -0.52

[0.53] [0.53] [0.53] [0.55] [0.54] [0.55] [0.88] [0.56]

SIC 7 0.303 0.301 0.387 -0.175 -0.208 -0.205 0.246 -0.123

[0.52] [0.52] [0.53] [0.53] [0.53] [0.53] [0.84] [0.54]

SIC 8 0.012 -0.009 0.06 -0.423 -0.427 -0.447 0.064 -0.38

[0.54] [0.54] [0.55] [0.55] [0.55] [0.55] [0.9] [0.56]

SIC 9 0.169 0.172 0.291 -0.118 -0.15 -0.162 0 -0.074

[0.8] [0.8] [0.81] [0.85] [0.84] [0.85] 0 [0.86]

Region -0.067 0.047 -0.23

[0.15] [0.16] [0.3]

Ownership*Region -0.896* -0.334

[0.44] [0.5]

Common Law -0.227 -0.114 -0.343

[0.23] [0.13] [0.32]

Civil Law -0.341 0.076 -0.179

[0.24] [0.14] [0.23]

LNMarketCap 0.044 0.04 0.046 -0.03 0.042

[0.03] [0.03] [0.03] [0.05] [0.03]

LNManagers -0.064 -0.071 -0.053 -0.246 -0.05

[0.12] [0.11] [0.12] [0.21] [0.12]

LNEmployees 0.063 0.061 0.056 0.111* 0.055

[0.03] [0.03] [0.03] [0.05] [0.03]

Ownership*Common 0.461

[0.33]

Ownership*Civil -0.619*

[0.31]

Creditor Rights -0.017 0.049

[0.04] [0.08]

Ownership*Creditor 0.015

[0.12]

Sharholder Rights -0.092

[0.08]

Ownership*Shareholder 0.275

[0.16]

Constant 2.381*** 2.285*** 2.495*** 1.935** 1.967** 1.888** 3.441*** 2.196**

[0.52] [0.52] [0.53] [0.67] [0.67] [0.67] [1.02] [0.72]

R-sqr 0.017 0.024 0.021 0.06 0.063 0.055 0.084 0.058

dfres 554 553 545 448 454 445 166 442

BIC 1917.2 1919.4 1900.3 1535.2 1549 1529 625.4 1545.5

58

10 Appendix-C Table C1 Log-Log Model using simple OLS and the full sample of 2005 firms. The dependent variable is Tobin's Q, using the natural logarithm of return on equity with the profit/loss method of last year., the model shows 𝑄 𝑖,𝑡 . The independent variables are: 1) Ownership as a dummy variable indicating the presence of managers owning >50% (=1) or managers owning <50% (=0), 𝑂𝑊𝑁𝐸𝑅′𝑖 ;2) Region as a variable indicating the region of firm to be the US (=0) or Europe (=1), 𝑅𝐸𝐺𝐼𝑂𝑁′ 𝑖 ; 3) the interaction between Ownership and Region, taking 1 for the interaction of European firms and owned by managerial shareholders, Owner*Region; 4) a dummy variable indicating the legal origin of a firm’s country to be common law (=1) or civil law (=0), 𝐶𝑂𝑀𝑀𝑂𝑁′𝑖;5) a dummy variable indicating the legal origin of a firm’s country to be civil law (=1) or common law (=0), 𝐶𝐼𝑉𝐼𝐿′𝑖;6) LNMarketcap, the natural logarithm of market capitalization, 𝐹𝐼𝑅𝑀𝑆𝐼𝑍𝐸′𝑖,𝑡 ; 7) LNEmployees the natural logarithm of number of employees, 𝐸𝑀𝑃𝐿𝑂𝑌𝐸𝐸𝑆′𝑖,𝑡; 8) LNManagers, the natural logarithm of number of managers, 𝑀𝐴𝑁𝐴𝐺𝐸𝑅𝑆′𝑖,𝑡; 9) the value of creditor rights as constant variable indicating the creditor rights level of a firm’s country from 0 -4 (La Porta et al., 1998), 𝐶𝑅 𝑖; 10) Anti-Director rights indicating the shareholder rights level of a firm’s country from 0-6 (La Porta et al., 1998), 𝐴𝐷𝑅 𝑖11) SIC codes to control for industry, as seen in Table A2; 𝛼 0 is the constant term. Firm performance is constant when all variables seem insignificant, Table 1 provides further definitions, This sample comprises 2005 observations for both US and European firms with a sample period between 2001 and 2015. Statistical significance is denoted with ***, ** and * at the 0.1%, 5% and 10% level, respectively.

Dependent Variable Tobin's Q [1] [2] [3] [4] [5] [6] [7] [8] Ownership 0.463*** 0.992*** 0.398*** 0.188* 0.179* 0.970*** 0.908*** 0.646*** Region -1.037*** -0.834*** -0.990*** -0.642*** -0.530*** -0.767*** -0.727*** -0.377*** Ownership x Region -1.172*** -1.119*** -1.114*** -0.993*** LN Market Cap -0.040*** 0.049*** 0.060*** -0.036*** 0.057*** LN Managers -0.622*** -0.677*** -0.652*** SIC 1 0.728* 0.417 0.409 0.656* SIC 2 1.112*** 0.757* 0.810** 1.060*** SIC 3 0.839** 0.506 0.537 0.789** SIC 4 0.616* 0.191 0.236 0.557 SIC 5 0.732* 0.513 0.542 0.704* SIC 6 -0.004 -0.176 -0.096 -0.039 SIC 7 1.022*** 0.675* 0.704* 0.927** SIC 8 1.190*** 0.864** 0.890** 1.130*** SIC 9 1.141 0.92 0.901 1.099 Constant 0.930*** 0.837*** 1.584*** 1.297*** 0.324 0.278 0.845* 0.314 R-Squared 0.125 0.146 0.131 0.177 0.215 0.175 0.179 0.229 N 2002 2001 2001 1977 1968 1992 1991 1967 BIC 7186.1 7146.4 7180.2 6989.7 6965.1 7146.3 7142.4 6936.2 Table C2 European Log-Log Model using simple OLS. The dependent variable is Tobin's Q, using the natural logarithm of return on equity with the profit/loss method of last year., the model shows 𝑄 𝑖,𝑡. The independent variables are: 1) Ownership as a dummy variable indicating the presence of managers owning >50% (=1) or managers owning <50% (=0), 𝑂𝑊𝑁𝐸𝑅′𝑖 ;2) Region as a variable indicating the region of firm to be the US (=0) or Europe (=1), 𝑅𝐸𝐺𝐼𝑂𝑁′ 𝑖 ; 3) the interaction between Ownership and Region, taking 1 for the interaction of European firms and owned by managerial shareholders, Owner*Region; 4) a dummy variable indicating the legal origin of a firm’s country to be common law (=1) or civil law (=0), 𝐶𝑂𝑀𝑀𝑂𝑁′𝑖;5) a dummy variable indicating the legal origin of a firm’s country to be civil law (=1) or common law (=0), 𝐶𝐼𝑉𝐼𝐿′𝑖;6) LNMarketcap, the natural logarithm of market capitalization, 𝐹𝐼𝑅𝑀𝑆𝐼𝑍𝐸′𝑖,𝑡; 7) LNEmployees the natural logarithm of number of employees, 𝐸𝑀𝑃𝐿𝑂𝑌𝐸𝐸𝑆′𝑖,𝑡; 8) LNManagers, the natural logarithm of number of managers, 𝑀𝐴𝑁𝐴𝐺𝐸𝑅𝑆′𝑖,𝑡; 9) the value of creditor rights as constant variable indicating the creditor rights level of a firm’s country from 0-4 (La Porta et al., 1998), 𝐶𝑅 𝑖; 10) Anti-Director rights indicating the shareholder rights level of a firm’s country from 0 -6 (La Porta et al., 1998), 𝐴𝐷𝑅 𝑖11) SIC codes to control for industry, as seen in Table A2; 𝛼 0 is the constant term. Firm performance is constant when all variables seem insignificant, Table 1 provides further definitions, This sample uses a sample period between 2001 and 2015. Statistical significance is denoted with ***, ** and * at the 0.1%, 5% and 10% level, respectively.

Dependent Variable Tobins Q [1] [2] [3] [4] [5] [6] [7] [8] Ownership -0.274** -0.274** -0.191* -0.220* -0.219** -0.285*** -0.192* -0.219** LN Market Cap 0.056*** 0.072*** 0.077*** 0.060*** 0.077*** LN Managers -0.125* -0.131* -0.131* SIC 1 -0.263 -0.302 -0.302 -0.263 SIC 2 0.444 0.494 0.42 0.444 SIC 3 0.022 0.039 -0.008 0.022 SIC 4 -0.087 -0.071 -0.131 -0.087

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SIC 5 0.314 0.346 0.311 0.314 SIC 6 -0.357 -0.231 -0.333 -0.357 SIC 7 0.372 0.387 0.356 0.372 SIC 8 0.405 0.396 0.381 0.405 SIC 9 0.782 0.728 0.771 0.782 Constant -0.127*** -0.127*** -1.119*** -1.072*** -1.263*** -0.27 -1.284*** -1.263*** R-Squared 0.012 0.012 0.033 0.038 0.136 0.108 0.131 0.136 N 876 876 875 873 864 867 866 864 BIC 2397.8 2397.8 2385.2 2386.1 2353 2368.9 2352.4 2353 Table C3 US Log-Log Model using simple OLS. The dependent variable is Tobin's Q, using the natural logarithm of return on equity with the profit/loss method of last year., the model shows 𝑄 𝑖,𝑡 . The independent variables are: 1) Ownership as a dummy variable indicating the presence of managers owning >50% (=1) or managers owning <50% (=0), 𝑂𝑊𝑁𝐸𝑅′𝑖 ;2) Region as a variable indicating the region of firm to be the US (=0) or Europe (=1), 𝑅𝐸𝐺𝐼𝑂𝑁′ 𝑖 ; 3) the interaction between Ownership and Region, taking 1 for the interaction of European firms and owned by managerial shareholders, Owner*Region; 4) a dummy variable indicating the legal origin of a firm’s country to be common law (=1) or civil law (=0), 𝐶𝑂𝑀𝑀𝑂𝑁′𝑖;5) a dummy variable indicating the legal origin of a firm’s country to be civil law (=1) or common law (=0), 𝐶𝐼𝑉𝐼𝐿′𝑖;6) LNMarketcap, the natural logarithm of market capitalization, 𝐹𝐼𝑅𝑀𝑆𝐼𝑍𝐸′𝑖,𝑡; 7) LNEmployees the natural logarithm of number of employees, 𝐸𝑀𝑃𝐿𝑂𝑌𝐸𝐸𝑆′𝑖,𝑡; 8) LNManagers, the natural logarithm of number of managers, 𝑀𝐴𝑁𝐴𝐺𝐸𝑅𝑆′𝑖,𝑡; 9) the value of creditor rights as constant variable indicating the creditor rights level of a firm’s country from 0 -4 (La Porta et al., 1998), 𝐶𝑅 𝑖; 10) Anti-Director rights indicating the shareholder rights level of a firm’s country from 0-6 (La Porta et al., 1998), 𝐴𝐷𝑅 𝑖11) SIC codes to control for industry, as seen in Table A2; 𝛼 0 is the constant term. Firm performance is constant when all variables seem insignificant, Table 1 provides further definitions, This sample uses a sample period between 2001 and 2015. Statistical significance is denoted with ***, ** and * at the 0.1%, 5% and 10% level, respectively.

[1] [2] [3] [4] [5] [6] [7] [8] Ownership 1.289*** 1.289*** 1.112*** 0.640*** 0.611*** 1.270*** 1.103*** 0.611*** LN Market Cap -0.099*** 0.067** 0.073*** -0.094*** 0.073*** LN Managers -1.106*** -1.127*** -1.127*** SIC 1 0.984 0.714 0.702 0.984 SIC 2 1.150* 0.507 0.696 1.150* SIC 3 0.931 0.41 0.521 0.931 SIC 4 0.788 0.106 0.288 0.788 SIC 5 0.852 0.44 0.556 0.852 SIC 6 0.003 -0.606 -0.308 0.003 SIC 7 1.150* 0.618 0.746 1.150* SIC 8 1.453** 0.894 1.042 1.453** Constant 0.985*** 0.985*** 2.622*** 1.960*** 0.904 0.493 1.911** 0.904 R-Squared 0.067 0.067 0.097 0.207 0.227 0.089 0.114 0.227 N 1129 1129 1128 1104 1096 1121 1120 1096 BIC 4651.4 4651.4 4622.2 4393.5 4420.2 4681.4 4657.1 4420.2

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Table C4 Full sample Linear-Log Model using simple OLS. The dependent variable is Tobin's Q, using the natural logarithm of return on equity with the profit/loss method of last year., the model shows 𝑄 𝑖,𝑡. The independent variables are: 1) Ownership as a dummy variable indicating the presence of managers owning >50% (=1) or managers owning <50% (=0), 𝑂𝑊𝑁𝐸𝑅′𝑖 ;2) Region as a variable indicating the region of firm to be the US (=0) or Europe (=1), 𝑅𝐸𝐺𝐼𝑂𝑁′ 𝑖 ; 3) the interaction between Ownership and Region, taking 1 for the interaction of European firms and owned by managerial shareholders, Owner*Region; 4) a dummy variable indicating the legal origin of a firm’s country to be common law (=1) or civil law (=0), 𝐶𝑂𝑀𝑀𝑂𝑁′𝑖;5) a dummy variable indicating the legal origin of a firm’s country to be civil law (=1) or common law (=0), 𝐶𝐼𝑉𝐼𝐿′𝑖;6) LNMarketcap, the natural logarithm of market capitalization, 𝐹𝐼𝑅𝑀𝑆𝐼𝑍𝐸′𝑖,𝑡; 7) LNEmployees the natural logarithm of number of employees, 𝐸𝑀𝑃𝐿𝑂𝑌𝐸𝐸𝑆′𝑖,𝑡; 8) LNManagers, the natural logarithm of number of managers, 𝑀𝐴𝑁𝐴𝐺𝐸𝑅𝑆′𝑖,𝑡; 9) the value of creditor rights as constant variable indicating the creditor rights level of a firm’s country from 0-4 (La Porta et al., 1998), 𝐶𝑅 𝑖; 10) Anti-Director rights indicating the shareholder rights level of a firm’s country from 0-6 (La Porta et al., 1998), 𝐴𝐷𝑅 𝑖11) SIC codes to control for industry, as seen in Table A2; 𝛼 0 is the constant term. Firm performance is constant when all variables seem insignificant, Table 1 provides further definitions, This sample uses a sample period between 2001 and 2015. Statistical significance is denoted with ***, ** and * at the 0.1%, 5% and 10% level, respectively.

Variable (6) linear (7) linear (8) linear (9) linear (10) linear (11) linear (12) linear (13) linear Ownership 10.479*** 19.097*** 12.326*** 8.462*** 6.586*** 19.715*** 19.455*** 13.647*** [1.44] [1.92] [1.52] [1.53] [1.57] [1.98] [2.42] [2.63] Region -11.904*** -8.613*** [1.09] [1.19] Ownership*Region -19.083*** -19.974*** -19.371*** -16.493** [2.86] [3.02] [5.22] [5.06] Common Law Origin 7.294* 4.803 3.047 6.886* 5.569 0.480 [3.27] [2.84] [2.84] [3.23] [5.65] [5.69] Civil Law Origin -3.057 -3.489 -3.942 -0.589 0.436 0.171 [3.44] [3.03] [3.01] [3.39] [4.57] [4.13] LNMarketCap -0.305 0.25 0.134 [0.23] [0.25] [0.35] LNEmployees -1.761*** -1.268*** -1.678*** [0.28] [0.29] [0.43] LNManagers -5.545*** -4.808** [0.98] [1.61] Creditor Rights -2.640*** -0.009 [0.45] [1.91] Shareholder Rights 1.17 0.781 [2.05] [2.41] Constant 11.960*** 10.442*** 1.21 15.019*** 17.604*** 5.857 -1.055 18.214 [0.78] [0.81] [3.21] [4.4] [4.4] [3.19] [7.28] [10.34] R-sqr 0.08 0.1 0.051 0.099 0.117 0.096 0.068 0.129 dfres 2002 2001 1962 1612 1594 1950 1310 1050 BIC 18510.2 18473.6 18231.3 14530.3 14362.3 18066.6 12681.7 9934.8

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Table C5 - US Sample Panel A: Skewness/Kurtoisis Test for normality joint Variable N P(Skewness) P(Kurtosis) adj chi2(2) P>chi2 TobinsQ 1131 0.00 0.00 . 0.00 LNTobinsQ 1131 0.00 0.97 63.39 0.00 Panel B: Shapiro-Wilk W test for normality Variable N W V z Prob>z TobinsQ 1,131 0.38 435.48 15.12 0.000 LNTobinsQ 1,131 0.95 32.45 8.66 0.000 Panel C: Homogeneity of Variances Summary of Tobins Q Clean Mean Std. Dev. Freq.

0 70.13 131.10 214 1 20.36 67.03 917

Total 29.78 85.22 1,131 Table C6 - European Sample Panel A: Skewness/Kurtoisis Test for normality joint Variable N P(Skewness) P(Kurtosis) adj chi2(2) P>chi2 TobinsQClean 878 0.00 0.00 . 0.00 LNTobinsQ 878 0.20 0.00 28.21 0.00 Panel B: Shapiro-Wilk W test for normality Variable N W V z Prob>z TobinsQClean 878 0.82 101.11 11.37 0.000 LNTobinsQ 878 0.99 6.28 4.53 0.000 Panel C: Homogeneity of Variances Summary of Tobins Q Clean Mean Std. Dev. Freq.

0 1.03 0.99 145 1 1.32 1.21 733

Total 1.27 1.19 878

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Table C7 - Full Sample Panel A: Skewness/Kurtoisis Test for normality joint Variable N P(Skewness) P(Kurtosis) adj chi2(2) P>chi2 TobinsQClean 2,005 0.00 0.00 . . LNTobinsQ 2,005 0.00 0.00 . 0.00 Panel B: Shapiro-Wilk W test for normality Variable N W V z Prob>z TobinsQClean 2,005 0.32718 799.771 17.005 0.000 LNTobinsQ 2,005 0.95711 50.978 10.001 0.000 Panel C: Homogeneity of Variances Summary of Tobins Q Clean Mean Std. Dev. Freq.

0 17.09 38.69 347 1 6.47 21.11 1,658

Total 8.31 25.36 2,005 Table C8 - Homogeneity Tests Region Levenes F Significance US 130.827 0.000 Europe 8.228 0.004 Full Sample 136.587 0.000