Matthew Seely Master Thesis Strategic Management

8/10/2019 Matthew Seely Master Thesis Strategic Management

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What makes entrepreneurs rich?

An insti tu ti on al expl an at io n of en trep re neur ia l su cces s in Co nf uc ia n Asi a

Matthew Seely 338555

MScBA Strategic Management

Coach: Dr. Patrick Reinmoeller

Co-reader: Dr. Orietta Marsili

September 12, 2011


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Preface

This thesis is submitted in partial fulfilment of the degree of MScBA in Strategic Management at

RSM. The copyright of this Master thesis rests with the author. The author is responsible for its

contents. RSM is only responsible for the educational coaching and cannot be held liable for the

content.

Since I began the initial work on my thesis in late November 2010, it has been a large part of my

life. It has been a challenging experience at times, but I have learned from my experiences and

genuinely enjoyed the process.

Studying billionaires has been particularly interesting. Our society is obsessed with the super-

rich. While working on my thesis I could not help but notice the abundance of news articles in

the popular media about billionaires and other highly successful entrepreneurs; it is rare for me

to go more than a day or two without stumbling on such an article. While people are curious

about these highly successful entrepreneurs, little research has been done to understand the

causes of their success. It has been my pleasure to provide some insight that may satisfy this

curiosity.

I would like to thank my thesis coach Dr. Patrick Reinmoeller for his guidance from the start of

this undertaking and my co-reader Dr. Orietta Marsili for her feedback. They have both provided

valuable critiques that have helped me to strengthen this work.


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Executive summary

This thesis proposes to answer a simple question: what makes entrepreneurs rich? Not all

entrepreneurs do get rich, of course, which makes it all the more interesting to question why this

is the case. While this is a simple question, the answer is complex and can be answered in

different ways. Past research has focussed on how individual characteristics, for example

individualism (Busenitz, Gomez, and Spencer 2000)and age (Evans and Leighton1989a;

Reynolds, 1997;Peters, Cressy, and Storey,1999;Delmar and Davidson, 2000), affect

entrepreneurship. This study takes a different approach, however, focussing on factors outside of

the individual.Grant (1991)suggests that certain factors provide countries with a national

advantage, a view widely held in the field of strategic management and stemming from Michael

E. Porters seminal work, The Competitive Advantage of Nations (1990). This thesis argues that

institutions are at the heart of the advantage described by Porter and that these advantages can

benefit entrepreneurs.

Institutional literature is rooted in sociology. Paul J. DiMaggio and Walter W. Powell (1983)

explain that businesses adopt practices due to pressures from external organizations and cultural

expectations. Over the past three decades, researchers have attempted to identify the specific

institutions that affect businesses. In his 1980 work, Cultures Consequences, Geert Hofstede

identified four cultural values that predicted economic growth: power distance, uncertainty

avoidance, individualism, and masculinity. This list of factors grew to five, with the inclusion of

long-term orientation added by Hofstede and Bond (1988). These factors have been a starting

point for institutional literature, with several others being identified by scholars in the three

decades following Hofstedes original contribution. Scholars such as Kostova (1999), Ghemawat

(2001), and Berry, Guillen, and Zhou (2010)have attempted to integrate these findings into

comprehensive frameworks.

This study selects relevant institutional factors based on the framework of Berry et al. (2010).

Their framework identifies nine institutional dimensions based on a survey of previous

theoretical publications and empirical research. These dimensions are economic, financial,

political, administrative, cultural, demographic, knowledge, global connectedness, and

geographic. Their framework deals specifically with institutional distanceshowever, not the

absolute value of institutions; that is to say that they look at the difference between the


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institutions in different countries, rather than the institutions themselves. This study, in contrast,

looks at the absolute value of institutions and focuses on the following seven institutional factors:

economic, financial, political, cultural, demographic, knowledge, and global connectedness.

Based on these seven institutional factors and a survey of relevant literature, this thesis proposes

that increases in the following institutional variables are positively related to entrepreneurial

success: economic development, financial development, taxes and government spending,

property rights, immigration, individualism, long-term orientation, population, population

density, the percentage of the people aged 15 to 64, innovation, and global connectedness.

Research has suggested that entrepreneurship is most prevalent in highly developed and under-

developed nations (Wennekerset al., 2005). This is consistent with other researchers (Gilad and

Levine, 1986)who have proposed that entrepreneurs are either pushed into entrepreneurshipby circumstances or pulled in by opportunities. Research has shown that the prevalence of

push entrepreneurs is associated with a strong economy, while no such relationship exists for

pull entrepreneurs (Acs, 2006). This suggests that a developed economy will offer greater

opportunities for entrepreneurial success. To test this, this thesis proposes:H1 Increased

economic development is positively related to entrepreneurial success.

Scholars (Schumpeter1934;King and Levine,1993; Greenwoodand Smith, 1997)have argued

that strong financial institutions generate more successful entrepreneurs. Countries that are better

developed financially offer the funds to back entrepreneurs, but financial institutions also take on

an important role, which is screening entrepreneurs and selecting those with the greatest promise.

To test this, this thesis proposes:H2 Increased financial development is positively related to

entrepreneurial success.

Siu and Martin (1992)suggest that Hong Kongers are more likely to become entrepreneurs due

to their low levels of taxation. Empirical research suggests, however, that high tax rates actually

encourage entrepreneurship (Aronson, 1991; Blau, 1987; Carson, 1984; Evansand Leighton,

1989b;Long, 1982). Research into theForbes list of billionaires has shown that taxes and

government spending do not adversely affect the accumulation of extreme wealth (Neumayer,

2004). The following is proposed:H3a Increased taxes and government spending positively

relate to entrepreneurial success.


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Research has shown a positive relationship between property rights and economic growth (Hall

and Jones, 1999;Keefer and Knack, 1997; Knackand Keefer, 1995)and large wealth

accumulation by individuals (Neumayer, 2004). Therefore, this thesis proposes:H3b The

protection of property is positively related to entrepreneurial success.

Research has shown that immigrants are more likely to become entrepreneurs than non-

immigrants in the United States (Borjas, 1986; Collins and Moore, 1970). Chinese immigrants in

particular, have been found to have greater entrepreneurial success than people born domestically

(Redding, 1995). The following is therefore tested:H4a High immigration levels are positively

related to entrepreneurial success.

Several studies (McGrath, MacMillan, and Scheinberg, 1992;Shane, 1992;Shane1993; Tiessen,

1997)have emphasized the importance of individualism in entrepreneurship. Hofstede and Bond(1988) suggest that long-term orientation a characteristic of socially supportive cultures is

related to entrepreneurial success. To test these seemingly opposed views, the following are

proposed:H4b Increased levels of long-term orientation are positively related to

entrepreneurial success andH4c Increased levels of individualism are positively related to


Research shows that population growth and density contribute positively to the number of

entrepreneurial start-ups in a country (Armingtonand Acs, 2002)but it is unclear if this will

affect success. The following hypotheses are proposed to test this:H5a Increased population

levels are positively related to entrepreneurial success andH5b High population density is

positively related to entrepreneurial success.

Economists (Erb, Harvey, and Viskanta,1997;Huyn, Mallik, and Hettihewa, 2006) have linked

firm performance to the working-age population, while entrepreneurship researchers (Evans and

Leighton,1989a; Reynolds, 1997;Peters et al.1999;Delmaret al., 2000)found mixed results

for the influence of age on entrepreneurial tendencies. The following is therefore proposed:H5c

An increase in the percentage of the population aged 15 to 64 is positively related to


Nations vary according to their ability to produce and commercialize new technologies (Furman,

Porter, and Stern,2002). It has been suggested that national innovation systems have allowed


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certain Asian countries to thrive economically (Hou and Gee , 1993; Kim, 1993; Nelson and

Rosenberg,1993). To test the effect on entrepreneurship, the following is proposed:H6 High

levels of innovation are positively related to entrepreneurial success.

According to Berry et al.(2010)global connectedness the ability to interact with other parts of

the world is as an important institutional dimension in international business. Research has not

shown how this may affect entrepreneurial success, however. To test this, the following is

proposed:H7 Increased global connectedness is positively related to entrepreneurial success.

To test these hypotheses, this study performed a series of regression analyses using

entrepreneurial success as a dependent variable, and several institutional measures as

independent variables. To measure entrepreneurial success, this study used the annualForbes list

of billionaires from 1996 to 2011. Specifically, it calculated the total wealth, the average wealth,and the total number of billionaires per country and used these three metrics as measures of

entrepreneurial success. In doing so, this study takes a broad view of what it is to be an

entrepreneur. The independent variables used come from the World Banks World Development

Indicators (WDI), the Heritage Foundation and Wall Street Journals Index of Economic

Freedom, the CIA World Factbook, and World Values Survey from a variety of years.

Because many studies have focussed on cultural institutions, this thesis takes a different

approach and controls for culture, in order to focus on other institutional factors. To do this, it

has focussed on China, Hong Kong, Japan, South Korea, and Singapore countries1that belong

to an area that the Global Leadership and Organizational Behaviour Effectiveness Research

Project (GLOBE) refers to as Confucian Asia2.

This research found mixed support for H1. The evidence suggests that a strong economy relates

positively to entrepreneurial success, but that countries that rely heavily on exports to support

their economy will produce less successful entrepreneurs. Mixed support was also found for H2;

it appears that financial development is positively related to entrepreneurial success, but that the

number of domestic companies is associated with a decrease in average billionaire wealth. Partial

1Although Hong Kong is part of China, this paper uses the term country loosely, applying it to self-governingregions such including Hong Kong.

2Confucian Asia also includes Taiwan; however, Taiwan is excluded from this study due to a lack of available data,from sources such as the World Bank, which do not distinguish between Taiwan and China in all of their data.


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support was found for H3a, with gross national expenditure contributing to the total number of

billionaires, but with no evidence suggesting that taxation increases entrepreneurial success.

Mixed support was found for H3b, with some evidence suggesting that property protection

contributes to entrepreneurial success, but with other evidence finding them opposed. Mixed

support was found for H4a, with evidence suggesting that the percentage of international migrant

stock is associated with an increase in entrepreneurial success, but with evidence that net

migration and Ethnic Chinese population decrease entrepreneurial success. No support was

found for H4b or H4c, though this is not surprising as this study controlled for cultural factors.

The models were significant however, indicating that culture is a predictor of entrepreneurial

success. Mixed support was found for H5a, with birth rate negatively contributing to the number

and overall value of billionaires and population growth contributing negatively to average

billionaire wealth. Population growth, however, contributed to average wealth. H5b yielded

mixed support; population density contributed significantly to entrepreneurial success, however

urban population contributed negatively to entrepreneurial success. Support was found for H5c,

with the percentage of the population aged 15 to 64 contributing positively to the model; the

percentage aged 65 and older also contributed significantly. Mixed support was found for H6,

with some variables indicating that knowledge increases entrepreneurial success and others

indicating the opposite effect. Mixed support was also found for H7, indicating that some forms

of global connectedness contribute to entrepreneurial success, while others have a negative

impact.

Overall, this study finds that institutional factors influence entrepreneurial success. Given the

fact that many of the results are contradictory, it suggests that the individual variables should be

investigated. That is to say, that rather than looking at a broad term like global connectedness;

future research should focus on specific factors such as Internet users or international tourism

expenditures. Alternatively, this research provides variables, which may be aggregated to create

new predictors of entrepreneurial success, which can be included together in a regression

analysis. Finally, this thesis offers some guidance for practitioners, suggesting that entrepreneurs

should seek out markets with supportive institutions and that governments cannot create

successful entrepreneurs merely through low taxes and spending, but must invest in building

institutions to support entrepreneurs.


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Contents

Preface............................................................................................................................................. 3

Executive summary......................................................................................................................... 5

Chapter 1 Introduction............................................................................................................... 13

Introduction............................................................................................................................... 13

Introduction to the problem definition...................................................................................... 14

Chapter 2 Theory ....................................................................................................................... 19

Problem definition and research questions............................................................................... 19

Economic factors .................................................................................................................. 19

Financial factors.................................................................................................................... 20

Political factors ..................................................................................................................... 20

Cultural factors...................................................................................................................... 22

Demographic factors............................................................................................................. 24

Knowledge factors................................................................................................................ 25

Global connectedness factors................................................................................................ 26

Chapter 3 Methodology ............................................................................................................. 29

Research objectives................................................................................................................... 29

Research design ........................................................................................................................ 29

Chapter 4 Results....................................................................................................................... 35

Economic factors ...................................................................................................................... 35

Financial factors........................................................................................................................ 36

Political factors ......................................................................................................................... 38

Cultural factors.......................................................................................................................... 40

Demographic factors................................................................................................................. 41

Knowledge factors.................................................................................................................... 44

Global connectedness factors.................................................................................................... 45

Chapter 5 Conclusion................................................................................................................. 47

Discussion................................................................................................................................. 47

Limitations................................................................................................................................ 55

Implications for research........................................................................................................... 56

Implications for practitioners.................................................................................................... 58

Works Cited................................................................................................................................... 61

Appendix....................................................................................................................................... 65


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Chapter 1 Introduction

Introduction

When considering entrepreneurial success in Asia it is difficult to ignore the growth of highly

successful Chinese individuals, as is reflected in the 64 Chinese billionaires that were identified

by a recentForbes list (Forbes, 2010). In absolute numbers, this sounds impressive; China is the

country with the second greatest number of billionaires (a distant second to the United States

with over 400). When the size of China's population is taken into account, it is considerably less

impressive. In China there is approximately one billionaire for every 20 million people (or 0.05

billionaires per million people; see appendix, Figure 1). This is one third of the global statistic of

0.15 billionaires for every million people and nowhere near the United stated with 1.29

billionaires for every million Americans. Closer (geographically and culturally) to China,

Singapore and Japan have 0.87 and 0.17 billionaires per million people, respectively. The biggest

contrast however, is with Hong Kong, a Special Administration Region of the People's Republic

of China3, which boasts a whopping 3.58 billionaires for every million people a greater number

per capita than any other country in the world excepting Monaco.4

The difference in success rates in these countries begs the question: what factors have created

this variance? Economists have long pointed to external factors that have allowed certain nations

to gain an advantage over others (Grant, 1991). In the field of strategic management Michael E.

Porter (1990)offers a framework that explains national advantage according to external factors.

Porter (1990) claims national advantage depends on entrepreneurs, but it also reasons that

entrepreneurs benefit from national advantage; a country with a national advantage should

produce entrepreneurs who are more successful than countries without an advantage. While

Porter (1990) does not specifically address institutions, they are at the root of the four factors he

describes. A growing field of literature does however, deal explicitly with the institutional causes

of business success (Berryet al., 2010).

Institutional literature is rooted in sociology. Paul J. DiMaggio and Walter W. Powell (1983),

drawing on the work of Max Weber, attempt to explain the homogeneity of organizations; they

3Although Hong Kong is part of China, this paper uses the term country loosely, applying it to self-governingregions such including Hong Kong.

4Monaco has an astronomical 30.3 billionaires per million people; however, this figure is merely the result ofhaving a single billionaire among its miniscule population of 33,000 people.


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argue that businesses adopt practices not because they are beneficial (though they may be), but

due to what they call institutional isomorphism. Organizations, according to this theory, do not

tend to diversify in their forms, but rather they tend to become more and more alike. DiMaggio

and Powell (1983) note, for instance, how American college textbook publishing evolved from

an industry with several business models, to a two-model industry. Institutions are what cause

this tendency toward homogeneity, as firms give in to formal and informal pressures by other

institutions and to cultural expectations (DiMaggio and Powell, 1983).

Institutional literature has attempted to identify the specific cultural variables that affect business,

the economy, and entrepreneurship. Much of the existing literature on the institutional causes of

entrepreneurship has focussed narrowly on cultural institutions, stemming from the cultural

dimensions identified by Geert Hofstede (1980). Over the past three decades institutional

literature has grown to include several other institutional dimensions (Berryet al., 2010), but no

study has empirically tested a comprehensive variety of institutional factors to determine their

effects on entrepreneurship.

This study will fill this gap in the literature, testing the effects of a variety of institutional factors

outlined by Berry et al. (2010)on entrepreneurial success. In order to focus on the variety of

institutional factors, and not just cultural factors, this study will specifically focus on the cultural

cluster identified by the Global Leadership and Organizational Behaviour Effectiveness Research

Project (GLOBE) as Confucian Asia. Confucian Asia includes China, Hong Kong, Japan, South

Korea, Singapore, and Taiwan (Houseet al., 2004). This study examines entrepreneurship in five

of these six countries: China, Hong Kong, Japan, South Korea, and Singapore (Taiwan is

excluded due to a lack of available data). As a measure of entrepreneurial success, this study will

use the personal wealth of billionaires in five of these countries over a 16-year period (1996-

2011). It will use regression analyses to assess the relationship between a variety of institutional

variables and entrepreneurial success.

Introduction to the problem definition

The concept of national advantage is nothing new. As Robert M. Grant (1991)points out,

national advantages are central to Adam Smiths 1776 work, The Wealth of Nations, which

introduces the concept of competitive advantage. Expanding on this in 1821, David Ricardo

developed the theory of comparative advantage of nations which asserts that a country gains an


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advantage in an industry due to its situation, its climate, and its other natural or artificial

advantages (Ricardo, 1821). Eli Heckscher and Bertil Ohlin5pick up this line of thinking in the

20thcentury, arguing that nations gain a comparative advantage over others due to factors of

production such as land, labour and capital (Ohlin, 1933). Michael Porter (1990)argues that

comparative advantage based on factors of production is not sufficient to explain patterns of

trade. Factor conditions still maintain an important role within Porters framework, albeit in

conjunction with three other determinants of national advantage (firm strategy, structure, and

rivalry; demand conditions; and related and supporting industries).

This paper does not disagree with Porters view that a variety of determinants interacts to create

a national advantage, but it focuses on one important cause: institutional differences. This does

not contradict Porter; in fact, it is compatible with his arguments. Porter (1990)does not deal

with institutional factors in any great depth, but he does say that the institutional structure

surrounding companies stimulates them to gain competitive advantage. Each of the

determinants described by Porter (1990)is influenced by institutions. Factor conditions include

natural resources, but according to Porter (1990), a nation does not inherit but instead creates

the most important factors of production such as human resources or a scientific base. For

example, educational institutions influence skilled labour (a factor condition). Demand

conditions are affected by a variety of institutions, including social, government and economic

institutions influencing the habits of consumers. Related and supporting industries are mostobviously affected by institutions established by the government. For example, the United States

large military investments create a supporting industry for the countrys weapons manufacturers.

Rivalry is largely influenced by government institutions. Countries like China limit foreign

competition by placing limitations on foreign direct investment. Even democratic, capitalist

countries impose trade tariffs and some limit domestic rivalry with control boards. Cultural

institutions also play a role; Porter (1990)notes that values and the perceived prestige of certain

industries in a given country drive investment and labour into the sector, creating greater rivalry.

Cultural institutions have received a great deal of attention in management scholarship.

Management scholars have embraced the four cultural dimensions identified by Geert Hofstede

(Berryet al., 2010). Hofstede (1980)conducted a survey of IBM employees in 40 countries

5Although Bertil Ohlin is the sole author ofInterregional and International Trade, he gives shared credit to EliHeckscher for the Heckscher-Ohlin model described in it.


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between 1967 and 1973. He identified power distance, uncertainty avoidance, individualism, and

masculinity as the four factors distinguishing one culture from another. Hofstede and Bond

(1988)later expanded these four factors to include Confucian dynamism also called long-term

orientation. Of the cultural factors identified by Hofstede, entrepreneurial studies have focussed

on individualism in particular (Busenitzet al., 2000). Although Hofstedes measures have been

embraced by management scholars, they have not gained popularity in the social sciences and

they have been criticized in other business fields such as international business, marketing, and

accounting (Berryet al., 2010). Berry et al. (2010)identify four specific problems with

Hofstedes measures. First, they criticize Hofstedes focus on culture, ignoring other

distinguishing dimensions of countries. Ghemawat (2001), in contrast, identifies culture as one

of four dimensions, or distances, that distinguish countries; the others are administrative,

geographic, and economic. Secondly, they criticize Hofstedes assumption that culture is static.

Tang and Koveos (2008)share this criticism, arguing that if cultural values correlate with

national wealth, then they cannot be static (as national wealth changes over time). This is

supported by Inglehart and Baker (2000)who found evidence of massive cultural change in

their study of 65 societies comprising 75% of the worlds population. Thirdly, they claim that

researchers studying individual managers make an ecological fallacy by assuming that the

results of Hofstedes general study can be applied to individual members of the group. Finally,

they criticize Hofstedes assumption that a survey of employees with a single company can be

generalized to the entire population.

Given the problems with Hofstedes cultural measures, it should come as no surprise that studies

relying on them have provided mixed and contradictory results (Berryet al., 2010). Studies have

shown cultural distance increases full ownership as a foreign entry strategy, that it encourages

joint ownership as a foreign entry strategy, and that it has no effect on ownership as a foreign

entry strategy (Berryet al., 2010). Some studies have found a correlation between lower foreign

subsidiary dissolution rates and greater cultural distances; others have found no relationship

(Berryet al., 2010). These mixed results suggest that although culture may influence businesses,

it alone is insufficient to explain the differences between countries.

Scholars have expanded on Hofstedes dimensions. In the field of psychology, Schwartz and

Bilsky (1990)identify seven cultural dimensions in their study of data from Australia, Finland,


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Hong Kong, Spain, and the United States. These values, which exist in varying degrees in each

of the countries studied, are achievement, enjoyment, maturity, prosocial, restrictive conformity,

security, and self-direction. The GLOBE project was initiated in 1991 by Robert J. House

(Hofstede, 2006). Despite some differences, Hofstede maintains that the GLOBE project was

designed as a replication and elaboration of his original 1980 study (Hofstede, 2006). The

GLOBE project identifies nine cultural dimensions: uncertainty avoidance, power distance,

societal collectivism, in-group collectivism, egalitarianism, assertiveness, future orientation,

performance orientation, and humane orientation (House, Javidan, Hanges, and Dorfman, 2002).

Although these studies have expanded on Hofstedes cultural dimensions, they still do not fully

explain the differences between countries. Other researchers have expanded their research

beyond cultural factors to consider broader institutional factors.

Tatiana Kostova (1999)presents the country institutional profile (CIP) as an alternative to

cultural measures. The CIP consists of three components: regulatory, cognitive, and normative

(Kostova, 1999). The regulatory component deals with a countrys laws and rules (Kostova,

1999). The cognitive component relates to the schemas, frames, inferential sets, and

representations [that] affect the way people notice, categorize, and interpret stimuli from the

environment (Kostova, 1999). The normative component is concerned with a nations values

and norms (Kostova, 1999), such as those represented in Hofstedes cultural dimensions.

The problem with Kostovas framework is that the operational definitions are somewhat

ambiguous and open to interpretation. Ghemawat (2001)offers a more concrete framework for

institutional distances. According to his CAGE framework, the distinguishing institutional

distances between countries are cultural, administrative, geographic, and economic (Ghemawat,

2001). Cultural distance encompasses linguistic, ethnic, religious and normative differences;

administrative distance includes political ties, government policies and institutional weaknesses;

geographic distance includes not only the distance between two points, but physical remoteness,

shared borders, access to waters, country size, transportation/communication links, and

differences in climate; and economic distance includes the difference between consumer incomes

as well as the differences in the cost and quality of several resources (Ghemawat, 2001).

Interestingly, Ghemawat (2001)asserts that different distances affect particular industries to

varying degrees; for instance, the food industry is strongly affected by cultural distance, while


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the electrical power industry is much more strongly affected by geographic distance.

Recent work by Berry et al. (2010)offers a more in-depth framework for measuring institutional

distances. Through a review of the existing literature, they identify nine institutional distances. In

addition to Ghemawats cultural, administrative, geographic and economic distances, they also

identify financial, political, demographic, knowledge, and global connectedness as distinguishing

cross-national distances (Berryet al., 2010). Each of the identified distances is supported both by

theoretical literature and empirical studies (Berryet al., 2010).

Previous institutional literature has focussed on institutional distances, specifically on the effect

that distance has when entering foreign countries. This paper differs from this approach. Rather

than looking at the effect of institutional distances, this paper looks at the effect of the

institutions per se. Rather than looking, for instance, at how the distance between China andHong Kong affects the entry of a business from one locale to the other, this paper seeks to

understand which institutional factors lead to entrepreneurial success in China and Hong Kong

individually (and each of the other countries studied). To do this, this study focuses on the

absolute value of the institutional factors. This is a return to the tradition of early modern

economists like Adam Smith and David Ricardo, and embodied by Michael Porters diamond

framework, of trying to understand what factors lead to a national advantage. This paper goes

further, however, incorporating the institutional theories that have developed over the past three

decades.


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Chapter 2 Theory

Problem definition and research questions

This paper aims to show the influence of institutional factors on entrepreneurial success in the

Confucian Asian cultural cluster. In their review of institutional literature Berry et al. (2010)

identify eight distinct institutional dimensions affecting business: economic distance, financial

distance, political distance, administrative distance, cultural distance, demographic distance,

knowledge distance, and global connectedness distance. Additionally, they identify geographic

distance as an important non-institutional dimension. Berry et al. (2010)measure the institutional

distance between countries, however this study treats the dimensions as absolute values. The goal

of this research is not to find out if the institutional proximity of countries is beneficial, but rather

if the institutional factors in each country lead to a national advantage. This study uses seven of

the dimensions identified by Berry et al. (2010). It omits geographic distance, as it is a non-

institutional dimension, because it cannot be measured as an absolute value, and because this

study opts to focus on a group of geographically close countries; while geographic distance may

be important to entrepreneurship, its impact is neutralized by the focus of this study. It also omits

the administrative dimension. Administrative distance deals with shared colonizer-colony links,

language, religion, and legal systems. This dimension is omitted because while it may be relevant

to the institutional distance between countries, the literature does not indicate that these

dimensions have an impact on the competitive advantage of the nation. This paper will attempt toshow the relationship between seven institutional dimensions (economic, financial, political,

cultural, demographic, knowledge, and global connectedness respectively) and entrepreneurial

success.

Economic factors

Wennekers, Van Stel, Thurik and Reynolds (2005)demonstrate that there is a U-shaped

relationship between nascent entrepreneurship and economic development, meaning that nascent

entrepreneurship is most common in the most developed and least developed nations; nascent

entrepreneurship is least common in moderately developed nations. This suggests that people are

likely to become entrepreneurs because either they have nothing to lose or they have enough that

they can afford to lose some. This is consistent with Gilad and Levine (1986)who suggest there

are two types of entrepreneurship: push entrepreneurship and pull entrepreneurship.


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Entrepreneurs are either pushed into entrepreneurship due to adverse economic and social

conditions or they are pulled in by opportunities in the market (Giladet al., 1986). It is unclear

from previous research, however, whether the level of success is the same for entrepreneurs in

underdeveloped and highly developed nations or if there is a relationship between economic

development and entrepreneurial success. Research has shown however, that while push

entrepreneurs have no significant impact on the economy, pull entrepreneurs strongly and

positively impact the economy (Acs, 2006). This suggests that entrepreneurs who find

opportunities presented by a strong economy will be more successful than those who are forced

into entrepreneurship due to lack of other options; this paper therefore proposes the following:

H1: Increased economic development is positively related to entrepreneurial success.

Financial factors

Joseph Schumpeter (1934)argues that banks play an important role in developing entrepreneurs

by selecting and investing the ones that are most promising. King and Levine (1993)continue

with this line of thinking, asserting that financial institutions play an active role in evaluating,

managing, and funding the entrepreneurial activity that leads to productivity growth.

Greenwood and Smith (1997)share this view. Additionally, Greenwood and Smith (1997)claim

that financial markets give entrepreneurs access to capital which allows them to specialize,

undertake professional development , and invest in technology. In turn, these lead to a higher rate

of growth in the economy (Greenwoodet al., 1997). This literature suggests that strong financial

institutions should lead to greater entrepreneurial success, both because they serve as gatekeepers

investing only in high-potential entrepreneurs and by giving entrepreneurs the resources they

need to grow. The following is therefore proposed:

H2: Increased financial development is positively related to entrepreneurial success.

Political factors

Siu and Martin (1992)suggest that free market economic policies and low taxation encourage

Hong Kong citizens to become entrepreneurs because they are able to keep more of their money.

If this is true then differences in taxation may explain the differences in entrepreneurial success

in Confucian Asia. Siu and Martins (1992)assertion is not empirically demonstrated however,

and a paper published by the International Monetary Fund suggests that high personal taxes can


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actually encourage entrepreneurship (Gordon, 1998); empirical studies on the effects of taxation

on entrepreneurship support this view (Aronson, 1991; Blau, 1987; Carson, 1984; Evanset al.,

1989b;Long, 1982). Djankov, La Porta, Lopez-De-Silanes, and Shleifer (2002)conclude that

highly regulated environments discourage entrepreneurship, however Baumol (1990, 1993)

argues that entrepreneurship levels are the same regardless of government regulation, but that

increased regulation encourages entrepreneurs to operate outside of the government regulation

(i.e. unlicensed or illegally). Capelleras, Mole, Greene, and Storey (2008)support Baumol (1990,

1993), showing that new firm size and growth differs between highly regulated and lightly

regulated countries (in this case Spain and the United Kingdom respectively) only when

unregistered businesses are excluded from consideration. None of these aforementioned studies

deals explicitly with extremely successful entrepreneurs (i.e. multi-millionaires or billionaires),

but a previous study of theForbes list of billionaires concluded that [n]either a higher fiscal

burden, nor a greater extent of government intervention, nor a greater extent of governmental

interference with prices and wages, has a negative effect on the incidence of great wealth

(Neumayer, 2004). This same study also concluded that government spending on social welfare

does not have a negative impact on large wealth accumulation. Together, these studies suggest

that tax rates and government spending will not adversely affect the success of entrepreneurs. It

is unclear whether taxation and government spending will affect entrepreneurial success. To test

this, the following hypothesis is proposed:

H3a: Increased taxes and government spending positively relate to entrepreneurial success.

Research shows private property rights are linked to increased economic growth (Hallet al.,

1999;Keeferet al., 1997; Knacket al., 1995). Neumayers (2004)study supports this,

concluding that private property rights positively affect large wealth accumulation while

socialist/communist dictatorships negatively affect large wealth accumulation. China appears to

offer a counter-example to the idea that private property rights are linked to economic growth as

it has experienced large economic growth despite its poor protection of private property but this

economic growth has not been the result of the private sector (Che and Qian, 1998). Nee (1992)

argues that private entrepreneurs in China are unwilling to reinvest their profits in their

businesses due to the lack of private property rights protecting them. This suggests that

entrepreneurs are less likely to be successful in countries with poor private property rights. From


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this, it is proposed:

H3b: The protection of property is positively related to entrepreneurial success.

Cultural factors

As noted above, much of the research into the institutional factors affecting national advantage

and entrepreneurial success have focussed on culture. The countries studied in this research

Confucian Asia are strongly influenced by Chinese culture. Siu and Martin (1992)suggest that

the success of entrepreneurs in Hong Kong may be attributed to the presence of Chinese

immigrants. This is supported by the fact that Chinese immigrants have had disproportionate

success in entrepreneurship relative to natives in several East Asian countries (Redding, 1995)

and it is consistent with empirical studies in the United States (Borjas, 1986; Collinset al., 1970)

that have shown immigrants are more likely to become entrepreneurs. Additionally, research in

Australia has shown that a large proportion of its wealthiest people are immigrants (Gilding,

1999). Not all immigrant communities are equally successful, however. Tarun Khanna (2007)

compares the success of overseas Chinese to overseas Indians, and finds that the Chinese expat

community is much more successful because while the Chinese diaspora (the community of

ethnically Chinese people living outside of China) is embraced by the Chinese government, the

Indian government has historically shunned the Indian diaspora. This suggests that Chinese

immigrants may be more inclined toward entrepreneurial success than immigrants may in

general. The success of Chinese immigrants may stem from characteristics inherent to all

immigrants, Chinese culture, or as Redding (1995)suggests from a combination of the two. It

reasons to hypothesize that countries with high levels of immigration (and in particular high

levels of Chinese immigration) will have a greater incidence of entrepreneurial success. The

following hypothesis is proposed:

H4a: High immigration levels are positively related to entrepreneurial success.

Academic literature has given considerable attention to the role that individualism plays in

entrepreneurship; individualism has been shown to be positively related to entrepreneurial output

as measured by the number of patents (Shane, 1992, 1993)and entrepreneurs typically are more

individualist than collectivist in nature (McGrathet al., 1992). Not surprisingly, individualist


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cultures are more entrepreneurial than collectivist cultures (Tiessen, 1997). The verdict is not

entirely clear in the academic community, however. Some studies have concluded that power

distance and individualism are the driving factors in entrepreneurial orientation, but others have

concluded that lower uncertainty avoidance is the key factor (Berryet al., 2010). Importantly,

while many studies have focussed on individualism as a driver of entrepreneurship, other

research has shown a lack of correlation between the two (Busenitzet al., 2000). Some literature

contradicts the notion that individualism drives entrepreneurship, showing that collectivist

nations actually spur entrepreneurial endeavours (Franke,Hofstede, and Bond, 1991; Hofstedeet

al., 1988). Stephan and Uhlaner (2010)studied entrepreneurship in socially supportive cultures

compared to performance-based cultures finding that Confucian countries (which scored high in

the measures of socially supported cultures) were found to have higher rates of entrepreneurship.

This suggests that Confucian cultures may be more inclined to entrepreneurial success. Hofstede

and Bond (1988) argue that Confucian dynamism contributes to economic growth and

entrepreneurship. Hofstede and Bonds concept of Confucian dynamism distinguishes two

different types of Confucian values, those that are oriented to the future and those that are

oriented to the present and past. Countries with high levels of Confucian dynamism are said to

emphasize future oriented Confucian values but to place less emphasis on Confucian values

oriented to the present and past. While entrepreneurship literature has focused on opportunity

recognition and short-term orientation (Shaneand Venkataraman, 2000)this suggests that long-

term orientation may actually be related to entrepreneurial success.

There is a tension between Siu and Martins (1992) assertion that Hong Kong entrepreneurs are

successful because they do not have a Confucian outlook, and that of Hofstede and Bond (1988)

who argue that Confucian dynamism (or long-term orientation) encourages entrepreneurship. It

could well be that while long-term orientation and individualism are seemingly opposed, they are

not mutually exclusive and can both be beneficial to entrepreneurs in different ways, and cultures

that can draw on both traditions will produce more successful entrepreneurs. Two hypotheses are

proposed to test this:

H4b: Increased levels of long-term orientation are positively related to entrepreneurial success.

H4c: Increased levels of individualism are positively related to entrepreneurial success.


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Demographic factors

Demographic factors affect the attractiveness of markets and their potential for growth (Berryet

al., 2010). Research suggests that population growth is positively related to the number of

entrepreneurial start-ups in a country (Armingtonet al., 2002). A growing population can

encourage pull entrepreneurs by providing growing consumer markets and it can create push

entrepreneurs as it creates more competition for employment particularly when population

growth is driven by immigration (Wennekerset al., 2005). The following hypothesis is proposed:

H5a: Increased population levels are positively related to entrepreneurial success.

Research has also shown that population density is positively related to the number of start-ups

in a country (Armingtonet al., 2002). This is consistent with Porters (1990)view that

competitive advantage arises from particular clusters, such as the electronics cluster in Silicon

valley or the automotive cluster in Detroit. The following is therefore proposed:

H5b: High population density is positively related to entrepreneurial success.

In the field of economics, researchers have focussed on the effect of age on firm performance.

Erb, Harvey, and Viskanta (1997)found a correlation between the population aged 25 and 45 and

stock returns in the United States. In the United States, shares were rapidly driven up during the

1990s by baby boomers as they entered their prime earning years and began saving forretirement (Poterba, 2001). Huyn et al. (2006)found a positive relationship between the size of

the population aged 40 to 65 and the superannuation fund.6The relationship between age and

stock prices suggests that entrepreneurs may benefit from investments in the market depending

on the age demographics of the population. It is unclear, however, if these studies based in

western, English-speaking countries translate to Asia.

A variety of studies has yielded different results with respect to the relationship between age and

a propensity toward entrepreneurship. Peters et al. (1999)found that younger individuals are less

likely to be or become self-employed and Evans and Leighton (1989a)found the average

entrepreneur to be over 40-years-old. Research into nascent entrepreneurs contradicts this;

American research has found a concentration of nascent entrepreneurs in the 25-to 34-year-old

6The superannuation is an Australian pension fund with a mandatory component paid by employers.


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demographic; 9.7% of individuals in this demographic are categorized as nascent entrepreneurs

twice the overall American average and people in this demographic are responsible for 71% of

all start-ups (Reynolds, 1997). A replication of the study in Sweden demonstrated different

results, with only 3% of Swedes in the 25-to 34-year-old demographic being classified as

nascent entrepreneurs (Delmaret al., 2000). The authors attribute this difference to two factors.

Firstly, they explain that Swedes in that demographic are more indebted than their American

counterparts leaving them less able to bare the risks of entrepreneurship (Delmaret al., 2000).

Secondly, they speculate that because there are more women in the workforce there are more

families with two working parents during the age when children are raised, leaving little room

for the efforts needed to set up a business (Delmaret al., 2000). This suggests that while age may

affect entrepreneurial propensities, cultural and other institutional factors are also at play.

Furthermore it is unclear whether the variation of entrepreneurial rate by age is a reflection of

age per se or generational differences (Verheul, Wennekers, Audretsch, and Thurik, 2002).

Research clearly shows that the age demographics of a population are important factors for

entrepreneurship and that they are related to stock market growth, but research has yet to explore

their effect on entrepreneurial success. Furthermore, these studies have focussed on Western

nations so the relationship between age demographics and entrepreneurial success in Confucian

Asia may or may not hold. The following is therefore proposed:

H5c: An increase in the percentage of the population aged 15 to 64 is positively related to


Knowledge factors

Furman, Porter, and Stern (2002)suggest that nations vary according to their national

innovative capacity, or their ability to produce and commercialize new technologies. This

echoes the view of Nelson and Rosenberg (1993)who suggest the rise of Japan and newly

industrialized countries like South Korea and Taiwan and the decline of the United States sincethe 1970s can be attributed to differences national innovation systems. Kim (1993)suggests

that the rise of South Korea in the 20thcentury can be attributed to its national innovation system

while Hou and Gee (1993)offer a similar explanation for the rise of the Taiwanese economy.

While research shows a positive correlation between innovation and economic growth, the


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relationship between innovation and entrepreneurship differs. Interestingly, research has shown

that the relationship between innovative capacity and nascent entrepreneurship is not linear, but

U-shaped, with the greatest incidence of nascent entrepreneurship being found in the countries

with the greatest and least innovative capacity (Wennekers, Van Stel, Thurik, and Reynolds

2005). The authors of the study note, however, that the U-shaped relationship is not as robust

when the United States is removed from the analysis (Wennekerset al., 2005). Also of interest is

that no Asian countries are shown to have high rates of nascent entrepreneurship and innovative

capacity in this study. Asian countries, in general, are shown to have high rates of nascent

entrepreneurship and low innovative capacity, while some (most notably Japan) have low levels

of nascent entrepreneurship and moderate innovative capacity (Wennekerset al., 2005). This is

consistent with Kim (1976), who suggests that innovation is not as important in newly

industrialized, non-western countries because the technological know-how already exists and

does not need to be innovated. Kim and Lau (1994)further support this, showing that economic

growth in Hong Kong, South Korea, Singapore and Taiwan can be attributed primarily to

tangible inputs not to technology. This suggests that exploitation of existing knowledge may be

more important than exploration to create knowledge in Confucian Asia.

Studies have yet to show the relationship between innovative capacity and entrepreneurial

success in Confucian Asia. While previous work suggests there is a negative relationship

between innovative capacity and nascent entrepreneurship in Asia (Wennekerset al., 2005),nascent entrepreneurs include push entrepreneurs. For reasons outlined above, it is unlikely

that push entrepreneurs will achieve the same level of success as pull entrepreneurs. Given

that pull entrepreneurs are more likely to be successful, the following is proposed:

H6: High levels of innovation are positively related to entrepreneurial success.

Global connectedness factors

Berry et al.(2010)identify global connectedness as an important institutional dimension ininternational business. They define global connectedness as the ability of resident individuals

and companies to interact with other parts of the world, obtain information, and diffuse their own

activities (Berryet al., 2010). The importance of global connectedness is supported by Rugman

and Verbeke (2003)who emphasize the importance of transnational clusters, in response to

Porters (1990)regional clusters. Global connectedness can be measured using tourism and


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Internet use as measures (Berryet al., 2010). Much has been written about the factors affecting

rates of Internet adoption in different countries (Andrs, Cuberes, Diouf, and Serebrisky, 2010;

Guillnand Zhou, 2005;Oxleyand Yeung, 2001;Wunnavaand Leiter, 2009)but little attention

has been paid the effects of Internet use on the economy or entrepreneurship.

The Internet is an enabling technology (Porter, 2001). In economics its been suggested that the

Internet has the potential to connect countries, which are isolated from economic hubs and trade

routes, to the global economy (Duntand Harper, 2002). In the field of export marketing, Hamill

(1997)suggests that the Internet can help SMEs connect with international clients to export

products and overcome the barriers to internationalization, while empirical research by Lu and

Julian (2007)shows Internet use can increase export marketing performance.

Surprisingly little literature addresses the role of global connectedness on entrepreneurship. AnAmerican study found that while there is a positive connection between owning a personal

computer and becoming an entrepreneur, the link between Internet access and entrepreneurship is

negative and statistically insignificant (Fairlie, 2006). The author of the study suggests that there

may be a relationship between Internet access and entrepreneurial performance; however, the

study did not test this hypothesis. Prahalad and Hammond (2002)suggest that the Internet and

other digital technologies can connect the poor in the so-called bottom-of-the-pyramid-nations,

increasing their entrepreneurial prospects. This can also benefit international firms who are able

to connect with these entrepreneurs. For example, one of DuPonts Latin American subsidiaries

used Internet kiosks to interact with customers and farmers in remote areas (Prahaladand

Hammond, 2002).

Asian countries have developed transnational economies and become experts in manufacturing,

but while they have developed sophisticated, computer-based manufacturing systems, knowledge

management is relatively underdeveloped (Ernst, 2001). Guilln and Surez (2005)present the

world as digitally divided with high levels of Internet use in high-income OECD countries and

relatively low levels of Internet use in other countries. They project that this divide will only

increase with time. This divide has the potential to stifle developing nations in Asia, while giving

a strategic advantage to those nations that are better connected to the world.

Empirical research on the effects of global connectedness and entrepreneurship, specifically in


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Confucian Asia, are lacking. This study will fill this gap by testing the following hypothesis:

H7: Increased global connectedness is positively related to entrepreneurial success.


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Chapter 3 Methodology

Research objectives

This paper aims to contribute to academic literature dealing with the success of entrepreneurs

across Confucian Asia. It also aims to contribute to institutional literature more broadly by

demonstrating that institutions have an effect on entrepreneurial success. No research has

attempted to explain the varying levels of entrepreneurial success in Confucian Asia and this

research will fill this void.

This research aims to provide practical insight for practitioners as well; by studying the

institutional factors that affect entrepreneurial success in the countries studied, it can allow

international entrepreneurs, investors, and expanding businesses to assess which countries are

more conducive to the success of entrepreneurial ventures. While this does not help countries intheir home markets, it can provide insight into selecting new countries for international

expansion.

Perhaps most importantly, this research will benefit policy makers in the countries covered by

this study. Porter (1990)asserts that governments play a role in shaping the context and

institutional structure surrounding companies and in creating an environment that stimulates

companies to gain competitive advantage. By understanding which institutions are most

important, governments can focus on creating the institutions that are conducive to

entrepreneurial success. Although not all institutions can be controlled or influenced by policy

makers, some can. This research will provide policy makers with insight into how government

institutions can encourage or stifle entrepreneurial success.

Research design

The above hypotheses were examined by way of a longitudinal study of billionaires from a

narrow cultural cluster of countries: China, Hong Kong, Singapore, South Korea, and Japan,

using publicly available secondary data. As has been noted above, many studies have focussed

on the cultural differences as a source of advantage. By focussing on a cultural cluster of

countries, it allows the other institutional factors to be examined more closely. A cultural cluster

is a group of countries distinguished by geographic proximity, mass migration and ethnic social

capital, and religious and linguistic similarities (Gupta, Hanges, and Dorfman 2002). The idea of


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cultural clusters can be traced back to the mid-20thcentury in the works of historian Arnold J.

Toynbee and psychologist Raymond Cattell (Guptaet al., 2002). The GLOBE project has been

the most in-depth attempt to group countries into cultural clusters. The project groups the 62

societies studied into 10 cultural clusters: Anglo cultures, Latin Europe, Nordic Europe,

Germanic Europe, Eastern Europe, Latin America, Sub-Sahara Africa, Arab cultures, Southern

Asia, and Confucian Asia (Houseet al., 2004). This paper focuses specifically on Confucian

Asia. These countries offer an interesting opportunity to investigate the role that institutions play

in creating a national advantage because they share strong similarities in many respects, yet there

are distinct differences and the success of entrepreneurs varies greatly across these countries.

This provides an opportunity to isolate the specific institutional factors that cause entrepreneurial

success.

Confucian Asia has a distinct worldview influenced by the teachings of Confucius and Buddha

(Houseet al., 2004). Some countries in Confucian Asia have sizeable ethnic Chinese

populations, including Hong Kong (95% of the population), and Singapore (77% of the

population); South Korea and Japan have smaller but significant Chinese populations (CIA,

2010). At the same time, there are sharp differences between countries in Confucian Asia. A

communist government has been in power in China since the 1949; the British returned Hong

Kong to the Chinese after 156 years of rule in 1997; Singapore was founded as a British colony;

Korea existed as part of Japan for much of the first half of the 20th

century and is now dividedinto a capitalist South and communist North; and Japan was under American occupation

following the Second World War, emerging as an economic powerhouse (CIA, 2010). These

unique histories have caused profound institutional differences between Confucian Asian

countries. Whitley (1992)describes the East Asian countries (specifically Japan, South Korea,

Hong Kong, and Taiwan) as having unique business systems that have developed as a result of

their particular national histories. While Western countries share common norms, capital market-

based financial systems, and similar methods for skill development within organizations, the

differences between East Asian countries are profound (Whitley, 1992). The authority relations

and structures of firms within East Asia vary considerably between countries due to the different

pre-industrial histories in each country (Whitley, 1992). Siu and Martin (1992)suggest that Hong

Kong entrepreneurs are successful because of specific institutional difference, but no study has

empirically demonstrated that institutional differences can explain the differing levels of


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entrepreneurial success in Confucian Asia. This study aims to fill this gap, showing how

institutional differences can explain the variance in entrepreneurial success between countries in

this cluster.

This study examined the personal wealth of billionaires across these regions as listed byForbes

annual list of billionaires from 1996 to 2011.Forbes lists the total estimated wealth of

billionaires at an individual level on an annual basis. This individual-level data was used to build

a country-level data set. The billionaires were organized by their country of citizenship as

listed byForbes and then the values were added to give total billionaire wealth per country,

averaged to give the average billionaire wealth per country, and counted to give the number of

billionaires per country. The result was a country-level data set giving the total value, average

value, and total number of billionaires by country for the years 1996 to 2011. The wealth of these

billionaires was considered in relation to a variety of institutional factors over the course of

multiple years. The specific institutional measures are outlined in Table 2 of the appendix. These

measures come from a variety of sources including the World Banks World Development

Indicators (WDI), the Heritage Foundation and Wall Street Journals Index of Economic

Freedom, and the CIA World Factbook. For the cultural measures, Geert Hofstedes (1980)four

measures and Hofstede and Bonds (1988)additional measure of long-term orientation were

measured using corresponding questions from the World Values Survey; this is in keeping with

the methods used by Berry et al. (2010)and Hofstede and Minkov (2010).

The sample for total billionaire had a mean of 42.742, a median of 25.8, a mode of 1.30, and a

standard deviation of 43.49313. The average billionaire wealth sample had a mean of 965534, a

median of 2.79, a mode of 1.3, and a standard deviation of 1.268979. The number of billionaires

had a mean of 13.5 with a median of 7.5, a mode of 4, and a standard deviation of 16.63229. The

descriptive statistics for all dependent and independent variables are included in Table 3 of the

appendix. The sample size varied by variable the maximum number of data points for any

variable was 80 (one data point per year for 16 years in 5 countries). The number of available

data points for each variable is listed in Table 2. Some of the data (demographic figures and

cultural values) were only available on a periodic basis. Missing data between available data

points was interpolated in keeping with the methodology of Berry et al. (2010). Missing values

outside of the range of available data points were excluded from the analyses.


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This study conducted three separate multiple regression analyses for each hypothesis using the

dependent variables of the number of billionaires per country, the total value of these billionaires,

and the average wealth of billionaires by country. The independent variables that were used for

each hypothesis are the institutional measures outlined in Table 2. This was done to ascertain

whether or not these institutional variables are, in fact, related to entrepreneurial success as

proposed in the hypotheses.

To test H1, this thesis used the following model:

ES =B0 +B1 * ANI +B2 * Inf +B3 * IGDP +B4 * IUSD +B5 * EGDP +B6 * EUSD + ;

where ES is entrepreneurial success, ANI is adjusted net income, Inf is inflation, IGDP is imports

as a percentage of GDP, IUSD is imports in US dollars, EGDP is exports as a percentage of GDP,

and EUSD is exports in US dollars.

To test H2, the following model was used:

ES =B0 +B1 * LDC +B2 * MCap +B3 * DC + ;

where ES is entrepreneurial success, LDC is the number of listed domestic companies, MCap is

the market capitalization of listed domestic companies, and DC is the domestic credit available

to the private sector.

To test H3a, this thesis used the following model:

ES =B0 +B1 * TTR +B2 * GNE +B3 * FiscF +B4 * TF +B5 * MF +B6 * IF +B7 * FinF +B8 *

LF +B9 * GS + ;where ES is entrepreneurial success, TTR is total tax rate, FiscF is fiscal freedom, TF is trade

freedom, MF is monetary freedom, IF is investment freedom, FinF is financial freedom, LF is

labour freedom, and GS is government spending.

The thesis tested H3b using the following model:

ES =B0 +B1 * PRP +B2 * PR +B3 * TRP +B4 * PEC +B5 * TEC +B6 * FFC + ;

where ES is entrepreneurial success, PRP is the number of procedures to register a property, PR

is property rights, TRP is time to register property, PEC is procedures to enforce a contract, TEC

is time to enforce a contract, and FFC is freedom from corruption.

H4a was tested using the model:

ES =B0 +B1 * IMS +B2 * NM +B3 * EC + ;


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where ES is entrepreneurial success, IMS is international migrant stock, NM is net migration,

and EC is Ethnic Chinese population.

H4b and H4c were tested using the following model:

ES =B0 +B1 * AUTH +B2 * OBD +B3 * INDP +B4 * INDV +B5 * FAM +B6 * WRK +B7 *

THR +B8 * NP + ;

where ES is entrepreneurial success, AUTH is respect for authority (power distance), OBD is

obedience (power distance), INDP is independence (individualism), INDV is individual

responsibility (individualism), FAM is importance of family life (femininity), WRK is

importance of work life (masculinity), THR is thrift (long-term orientation) and NP is national

pride (short-term orientation).

H5a was tested using the model:

ES =B0 +B1 * BR +B2 * POPT +B3 * POPG +;

where ES is entrepreneurial success, BR is the birth rate, POPT is the total population, and

POPG is population growth.

H5b was tested according to the model:

ES =B0 +B1 * POPD +B2 * UP +B3 * RD + ;

where ES is entrepreneurial success, POPD is population density; UP is urban population, and

RD is road density.

H5c was tested with the model:

ES =B0 +B1 * LE +B2 * POPMid +B3 * POPEld +;

where ES is entrepreneurial success, LE is life expectancy, POPMid is the percentage of the

population aged 15 to 64, and POPEld is the population aged 65 and over.

H6 was tested using the model:

ES =B0 +B1 * RDE +B2 * JOURN +B3 * RSRCH +B4 * PTNT + ;

where ES is entrepreneurial success, RDE is R&D expenditures, JOURN is the number of

scientific and technical journal publications, RSRCH is the number of researchers in R&D per

million people, and PTNT is the number of patents applied for.

H7 was tested using the model:


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ES =B0 +B1 * MOB +B2 * MOBP +B3 * TRSM +B4 * INTR +B5 * INTRP +B6 * TEL +B8 *

TELP + ;

where ES is entrepreneurial success, MOB is the number of mobile phone subscribers, MOBP is

the number of mobile phone subscribers per 100 people, TRSM is international tourism

expenditures, INTR is the number of Internet users, INTRP is the number of Internet users per

100 people, TEL is the number of telephone lines, and TELP is the number of telephone lines per

100 people.

The regression analyses were performed using the ordinary least squares method. Importantly,

the dependent variables (total billionaire wealth, average billionaire wealth, and total number of

billionaires) are continuous, and there is evidence that that are related to the predictor variables

used in the analyses. The relationship between predictors and dependent variables is linear and

the error is normally distributed and uncorrelated with the predictors. There are, however,

problems with multicollinearity. The implications of multicollinearity are discussed in the

limitations section of the conclusion.


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Chapter 4 Results

Economic factors

This study analyzed the correlation between the total worth of billionaires per country, the

average worth of billionaires per country, and the total number of billionaires per country with

the following economic measurements: export of goods and services in US dollars, the export of

goods and services as a percentage of GDP, inflation, national income, imports of goods and

services in US dollars, and imports of goods and services as a percentage of GDP. A summary of

the expected and resultant coefficient directions is found in Table 4 of the appendix. Details of

the regressions are found in Table 5.

A regression analysis was performed using the total worth of billionaires per country as a

dependent variable, and export of goods and services in US dollars, the export of goods andservices as a percentage of GDP, inflation, national income, imports of goods and services in US

dollars, and imports of goods and services as a percentage of GDP as independent variables.

Adjusted net national income, and imports of goods and services (percentage of GDP)

contributed significantly (p < .001) and positively to the model supporting H1. Every dollar

that adjusted net national income increases, the total value of billionaires in a country increases

by $0.02818. For every percentage point that imports of goods and services increases, total

billionaire wealth increases by $818 million.The exports of goods and services (percentage of

GDP) contributed significantly (p < .001) and negatively to the model, contradicting H1. For

every percentage point that exports of goods and services increases, total billionaire value

decreases by $3.498 billion. Overall, the model yielded an adjusted R squared value of .636 was

found to be statistically significant (p < .001). This indicates that 63.6% of variation in total

billionaire wealth can be predicted based on these economic variables.

A multiple regression analysis of average billionaire wealth per country (the dependent variable)

and export of goods and services in US dollars, the export of goods and services as a percentage

of GDP, inflation, national income, imports of goods and services in US dollars, and imports of

goods and services as a percentage of GDP (the independent variables) was performed. Adjusted

net national income contributed significantly (p < .01) and positively to the model with every

dollar increase to net national income predicting a $0.0006568 increase in average billionaire

wealth, supporting H1. Inflation contributed significantly (p < .05) and positively with every


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percentage increase in inflation, increasing average wealth by $114 million, supporting H1.

Imports of goods and services (percentage of GDP) contributed significantly (p < .001) and

positively, supporting H1; every percentage increase equalled an increase of $117 million in

average wealth. Exports of goods and services (percentage of GDP) also contributed

significantly (p < .001) but negatively, with every percentage point of increase in exports

amounting to a decrease of $149 million in average wealth; this contradicts H1. Overall, the

regression analysis yielded a statistically significant (p < .001) model with an adjusted R square

value of 0.552, indicating that 55.2% of the variation in average billionaire wealth can be

predicted with these economic variables.

A regression analysis using the number of billionaires as the dependent variable and export of

goods and services in US dollars, the export of goods and services as a percentage of GDP,

inflation, national income, imports of goods and services in US dollars, and imports of goods and

services as a percentage of GDP as the independent variables was performed. In this model,

adjusted national income contributed positively and significantly (p < .001) with every dollar

increase in adjusted national income equalling an increase of 8.757E-12 billionaires per country,

offering support for H1. Imports of goods and services (percentage of GDP) contributed

positively and significantly (p < .001) with each percentage point increase equalling an increase

of 0.922 billionaires, also supporting H1. Imports of goods and services (current US$)

contributed negatively and significantly (p < .05) with every dollar increase in imports equallinga decrease of 6.182E-11 billionaires, contradicting H1. Exports of goods and services (percent of

GDP) contributed negatively and significantly (p < .001) to the model; for every percentage

point of increase in exports there was a decrease in the number of billionaires by 0.761,

contradicting H1. Exports of goods and services in US dollars also contributed significantly (p

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