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The relationship between development, globalization and the English language

Heber Rowan

A study submitted as part of the requirements for a MA in

Development.

Dublin City University 2012

Word count

17,202

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Declaration

I hereby certify that this material, which I submit for assessment on the program of study leading to the award of MA in Development, is entirely my own work and has not been

taken from the work of others, save as and to the extent that, such work has been cited and acknowledged within the text of my work.

Signed: Heber Rowan

________________________

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Table of contents Acknowledgements 5

Abstract 6

Abbreviations 7

Tables 8

Chapter one 9

Introduction 10

Methodology 12

Globalisation, development and languages 13

Contemporary globalisation and languages 15

Economic development and human capital 16

Primary independent variables 17

Dependent variable 17

Discussion of independent variable 18

Hypothesis of study 19

Structure 20

Chapter two Literature review 21

Introduction 22

Attitudes towards English 23

English as a neutral language 24

Historical precedents 25

Language costs 29

Second languages and the status quo 30

Why is there a relationship between ELP and economic development? 34

Language growth and economic growth, intertwined? 38

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Regional languages 38

Cultural effects of English media 40

Social mobility from education, criticisms 41

Population 43

Conclusion 44

Chapter three Empirical findings and analysis 45

Introduction 47

Methodology 48

Indicators used 48

Notes on Table 7 49

Table 7 data analysis 54

Table 8 & 9 analyses 55

Table 10 and 11 analyses 59

Conclusion 63

Chapter four

Thesis conclusions

Suggestions for future study

The value of English

Appendix

Bibliography

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Acknowledgements

This thesis would not be possible without the aid and guidance of the department of Law and Government at Dublin City University in particular, Niamh Gaynor, David Doyle and Noelle Higgins. I would also like to thank my parents for their encouragement during my studies and Travis Selmier of Tennessee University for his keen insights on this topic. Finally, I would like to thank the staff of Manba High School, Gunma, Japan for their patience while I completed this thesis.

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Abstract

This thesis examines the relationship between international trade, development and English. It attempts to see is there is a link between prosperity levels and proficiency in the English language. As globalization has often been viewed as a panacea for development, there is a gap in our understanding of the relationships between global languages, economic development and globalization. The links between the effects of languages on FDI and trade are documented, yet there has been little study on how English proficiency levels in non-native speaking countries impact their development or/and globalization. To address that gap, this thesis posits that since language skills are vital for entering the global economy, countries with higher levels of globalization and economic development will generally have higher English language proficiency levels (ELP). This thesis compares ELP scores, globalization indexes, ease of business rankings and population, FDI, GNI, GDP and export levels in order to illustrate that relationship. It finds that both GDP and GNI per capita can be associated with higher ELP scores, meaning that stronger, economically developed states are likely to be more proficient speakers of English.

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Abbreviations EF: English First

ELP: English Language Proficiency

ESL: English as a Second Language

FDI: Foreign Direct Investment

GDP: Gross Domestic Product

GNI: Gross National Income

IELTS: International English Language Testing System

TOEFL: Test of English as a Foreign Language

WTO: World Trade Organisation

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Tables Table 1 Language distance between key languages.

Table 2 Trade Language hierarchy

Table 3 Summary of transaction costs effects on language

Table 4 EF English proficiency Index, Score levels and rankings

Table 5 Exports and ELP scores interrelated

Table 6 GNI and ELP scores

Table 7 TOEFL ELP scores, Ease of business rankings and Globalisation scores

Table 8 IELTS ELP scores and economic development indicators

Table 9 Data correlation coefficient results of Table 8

Table 10 TOEFL ELP scores and Economic development indicators

Table 11 Correlation coefficient findings from Table 10

Table 12 Population and TOEFL score rankings

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Chapter One

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Introduction

It has been ascertained by Dreher (2006: 38) that globalized countries generally trade

more with others and that they are more economically developed1. It has been further

established by Selmier & Oh (2012) that global languages have an effect on international

trade and investments. While the effect of particular levels of global languages’ proficiency

have on countries learning those languages is little understood. English is the hallmark

‘global language’ on account of its ubiquity. Globally it is estimated that up to two billion

people worldwide are learning English today2, more than the combined populations of the

every native English speaking country in the world. With so many focusing their efforts on

the English language, it is germane that we question the utility of that endeavour by finding

out by how much learning and using English, can aid a country’s economic development. In

this thesis I examine the linkage between English language proficiency and economic

development, investigating if globalised and developed states are more often proficient

speakers of international languages. I find strong correlation between economic development

indicators such as GDP per capita, GNI per capita and ELP scores. While other languages

shall be reflected on, English is our focus. Why? Its dominance:

“The choice has fallen on English not because it is more beautiful or more expressive, but just because

it is already more widespread than any of the other potential candidates.”3

Indeed, like the modern rise of ‘Facebook’ (a social networking website), in order to connect

with the rest of the world online, millions choose one language because for them it's the best

1 Dreher, Axel. Does globalization affect growth? Evidence from a new index of globalization. Applied 2 “English has official or special status in at least seventy five countries with a total population of over two billion”. Frequently asked Questions, The English language. http://www.britishcouncil.org/learning-faq-the-english-language.htm Accessed 29/7/2012

3 Dyer, Gwynne. “The worldwide triumph of English”. 23/05/2012 The Japan Times Online

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allows for the widest range of communication possibilities. In a globalized world, those who

can effectively utilize the largest spans of communications open to them hold ‘the edge’4 in

the global economy. First, I will look at why certain languages have that edge, then how and

by how much does speaking them matter in keeping that ‘edge’. To begin our examination of

how it does that, I will establish the ubiquity of English within the context of globalization, to

get a fix on how one language commands such an expanse of users that it is called a global

language.

Languages are a skill and since so much can be affected by language in life, it is fair

to say that they are a power of their own. In particular I will examine their symbiotic

relationship with economies. Block and Cameron (2001) argued,

“Some commentators have suggested (e.g. Heller 1999a) that languages are coming to be treated more

and more as economic commodities, and that this view is displacing traditional ideologies in which

languages were primarily symbols of ethnic or national identity. The commodification of language

affects both people’s motivations for learning languages and their choices about which languages to

learn. It also affects the choices made by institutions (local and national, public and private) as they

allocate resources for language education.”5

Therefore, it is arguable that languages are often learnt because of their close relationship to

economies (and states to a lesser extent). Thus, choosing to learn a particular language can

mean one has accepted its relevance in an economy. English is relevant in the global

economy, so arguably wherever one is, there will be an economic advantage to speaking that

language. Some countries have recognized that advantage by teaching English in public

schools from an early age. So why are such skills so central to education in most countries?

4 The desired factor in employment markets for individuals and the attractive quality for investors to a particular country. 5 Block, David. Cameron, Deborah. Globalization and Language Teaching. Taylor & Francis e-Library, 2001 p. 5

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The economy simply demands it as new labor markets open in international spheres when

domestic economies become counterparts to the global economy; a global skill or language is

needed to interact with it. Since accepting the utility or edge of English in the global economy

appears central to achieving growth. I need to see if the facts and statistics justify the actions

of billions around the world. That is why in this thesis I investigate the relationship of

economic development indicators to ELP to see if it really matters.

Methodology

How I justify those actions, is through an examination of economic development

indicators and ELP scores. The indicators that I examine are as follows: population figures,

ease of business rankings, export levels, FDI inflow levels, GDP per capita, GNI per capita

matched with their respective globalisation index rankings. I scrutinise IELTS and TOFEL

scores of non-native Globalisation speaking countries. Our specific interest will rest with

finding out how many non-native English-speaking countries are high on globalisation scale,

GDP, GNI and ELP scores. The higher correlations I can draw with those variables, the

stronger a case I can build for my argument that a higher degree of globalization and

economic development is tied to higher levels of English proficiency and economic

development.

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Globalization, development and languages

Since this thesis examines the impacts of ELP levels on states’ economies, I am

essentially asking if speaking more English is a good thing. Which is in effect, also asking

whether globalization is a good thing. Likewise, does it maximise the greatest ‘good’ for the

largest number of people and the least harm?

Globalization, a convergence of world affairs means change, namely that life in the

present is different from that of the past. The values and desires in a previous generation may

have been different but some aspects remain arguably constant. According to Amartya Sen

(1999: 36) development is ‘a process of expanding the real freedoms that people enjoy’6, such

as freedom from poverty, attainment of civil rights, education and health care. Sen regards

education in skills such as literacy and numeracy as basic freedoms7 as they allow for

increases in life freedoms by spreading economic opportunities in a supportive background8.

Bruthiaux (2002: 277) also concurs with Sen’s view.

“Economic development of the most urgent kind should be viewed more narrowly as

a process of societal change leading to tangible improvements in and greater control

for the most disadvantaged members of a society over their living conditions.”9

Sen (1999: 3) defines economic growth to be an important means to achieve freedoms that all

members of society aim to obtain10. Therefore from Dreher’s (2006: 38) correlations between

6 Sen, Amartya. Development as Freedom. 1999 Oxford University Press p. 36

7 Ibid p. 13 8 Ibid p.91 9 Bruthiaux, Paul. Hold Your Courses: Language Education, Language Choice, and Economic Development. TESOL Quarterly, 36:3 Language in Development (Autumn, 2002), p. 275-296

10 Sen, Amartya. Development as Freedom. 1999 Oxford University Press p. 3

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globalization and economic growth11, we can conceive a relationship between globalization

and development. Furthermore if multilingualism levels are such a significant factor on

language costs to aid FDI (Selmier & Oh 2012), we can acknowledge a relationship between

ELP and development (specifically globalized economic development).

Understanding what ELP does (as an indicator or otherwise) is crucial to my

understanding of globalization and international investment. As language, I argue, has always

impacted the dynamics of human trade and interactions. Adam Smith once even said, “The

propensity to truck, barter, and exchange one thing for another is an innately human

characteristic”12. In modern times its influence is arguably greater, through the Internet,

media, international trade and FDI. As Block & Cameron (2001: 12) stated:

“Any invocation of ‘worldwide social relations’ unfettered by ‘the constraints of

Geography’ must immediately raise questions about language. Language is the primary medium of

human social interaction, and interaction is the means through which social relations are constructed

and maintained. While much everyday interaction still occurs, as it has throughout human history,

within local networks, large numbers of people all over the world now also participate in networks,

which go beyond the local. New communication technologies enable individuals to have regular

exchanges with distant others whom they have never met face-to-face.”13

Phillipson (2001: 187) further agrees and in essence defines English as the language of

globalization.

“English is integral to the globalisation processes that characterise the contemporary post-cold-war

phase of aggressive casino capitalism, economic restructuring, McDonalisation, and militarisation on

11 Dreher, Axel. Does globalization affect growth? Evidence from a new index of globalization. Applied Economics, 2006, 38. P. 1092 12 Smith, V. 1998. The two faces of Adam Smith. Southern Economic Journal 65(1): 1-19. 13 Block, David. Cameron, Deborah. Globalization and Language Teaching. Taylor & Francis e-Library, 2001 p. 12

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all continents. English is dominant in international politics and commerce, its privileged role being

strengthened through such bodies as the United Nations, the World Trade Organisation, and regional

groupings such as the North American Free Trade Agreement and the European Union.”14

This is arguing that higher end processes of globalization revolve around the use of English.

However Philipson adds a caveat,

“Many write loosely that English is the world language, but to describe English in such terms ignores

the fact that a majority of the world’s citizens do not speak English, whether as a mother tongue or as a

second or foreign language”15.

Yet as Weber (1999: 12) reminds us, it simply is comparatively the world’s most influential

language16. The numbers of its native speakers, secondary speakers, the economic muscle of

the countries that speak it, its socio-literary prestige and the number of major international

fields it leads such as science and aviation for example: all exemplify its influence. Therefore

while it is not the language of the entire world, it is more so than any other. That is why I am

driven to investigate this aspect of ELP in my thesis. As it asks if globalization and the

growth of English is a good thing for an economy since so many choose to devote so much

time and energy to one language.

Economic development and human capital

English proficiency is a skill and an attribute of human capital utilised in the global

economy. I study ELP as an integral part of economic development because, like other skills,

it increases the employment opportunities available, in tertiary or services industries that are

free from the insecurities of pestilence and hunger that can characterise primary industries

14 Philipson, Robert. English for Globalisation or for the world’s people? International Review of Education. 47: ¾ Globalisation Language and Education (Jul, 2001) p.187

15 Ibid p. 188 16 Weber, George. The World’s 10 most Influential Languages. AATF National Bulletin. 24: 3 (January 1999) p.22

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like agriculture. “Generally, the incomes of workers are closely correlated with value added

per employee”17, which means that in general, a worker’s value is determined by the level of

their skills. As economies and individuals have adjusted their education and skills to adapt to

such changing circumstances, certain basic skills have become requirements in such changing

circumstances. As Spence (2011: 34) stated,

“The highly educated, and only them, are enjoying more job opportunities and higher incomes.

Competition for highly educated workers in the tradable sector spills over to the non-tradable sector,

raising incomes in the high-value-added part of that sector as well. But with fewer jobs in the lower-

value-added part of the tradable sector, competition for similar jobs in the non-tradable sector is

increasing. This, in turn, further depresses income growth in the lower-value-added part of the non-

tradable sector. Thus, the evolving structure of the global economy has diverse effects on different

groups of people in the United States. Opportunities are expanding in the tradable sector because that

job market must remain competitive with the tradable sector. But opportunities are shrinking for the

less well educated.”18

The same could be said for developing economies. As middle-income countries expand their

education systems and their citizens make greater investments in education, they foster

human capital development, or people led growth, (particularly where there is limited natural

resources). This is why in emergent economies like South Korea one can observe high levels

of investment in education and foreign language education (5% of GDP in 2009)19. A policy

that may be working in the developing world as of late, as The Economist noted20:

“The combined output of the emerging world accounted for 38% of world GDP (at market exchange

rates) in 2010, twice its share in 1990. If GDP is instead measured at purchasing-power parity,

emerging economies overtook the developed world in 2008 and are likely to reach 54% of world GDP

this year.”

17 Spence, Micheal. 2011 ‘The Impact of Globalization on income and employment’. Foreign Affairs. July/August, 90 (4): 32 18 ibid p. 34 19 Worldbank development indicators accessed 2012 20 Economist, The. Emerging vs developed economies, Power shift. 4th August 2011

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While a portion of that growth can be attributed to the economic growth of China,

globalization and the use of international languages has had a profound impact on trade. As

human capital becomes a valuable commodity, at times the issue may not be what you sell but

how you sell it, as “the merchant speaks the customer’s language”21. It is that reason that

makes ELP so valuable to economic development as it lowers the ‘communication costs’

between potential investors due to a shorter ‘linguistic distance’ between the two as Selmier

& Oh (2012) articulate.

So far I have provided an opening theoretical basis as to why English is seen as

crucial to growth in the global economy (i.e. why the link between economic development

and ELP levels are investigated). Next I will explain the dependent and independent variables

to specify what factors influence my research on the possible tangible benefits of English.

Primary independent variables

These comprise of factors that influence the outcome I am examining.

1. Levels of globalization, ease of business rankings, GNI per capita, GDP per capita,

FDI inflows and exports in the examined states.

2. The speaking of a non-major or major trade language in a state.

3. The population size of a state in question.

Dependent variable

My dependent variable, being the outcome of the independent variables, is as follows:

21 Graddol, David. 1997 ’The Future of English?: a Guide to Forecasting the Popularity of English in the 21st Century’. London: British Council, p. 29

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The English language proficiency (ELP) levels in the countries researched.

Discussion of independent variables

To reiterate my research question: Do economically strong, globalized states often

speak more English than less strong ones? Hence, we examine how languages are tied to

trade and to globalization and my independent variables to provide greater substance to my

argument.

Now we will explain my selection of the independent variables.

1. Levels of globalization of the examined states. This tenant of my research concerns

us, as economically developed states are more likely to be globalized states.

Globalization levels and ease of business rankings are a means of demonstrating how

interconnected a country is to the world. GNI and GDP per capita provide with

reliable indicators of the size of the economies studied. While exports levels,

demonstrate the magnitude of their outward international trade similarly; FDI inflows

show how well a country is trusted and how valued its markets are to warrant FDI.

2. The speaking of ‘a major trade language’ may impact incentives or the cost of

speaking English for a state.

3. Population size may bear on the ELP levels in countries that have high levels of FDI

and globalization. As a larger domestic market in a country discourage high ELP as

the country may have less of a need to enter foreign markets for trade and investment,

than a small country unable to support itself through its small domestic market.

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Hypothesis of study

I can surmise in particular that:

1. Foreign direct investment, international trade, and globalization levels are higher in

states with higher levels of English language proficiency (ELP).

Due to the ‘spin-off’ effect of international trade influenced by such an international

language, I can draw a further possible hypothesis.

2. Higher FDI levels are often linked with higher globalization figures.

In other words, the increased use of English, a major trade language, is a good thing overall

for countries to encourage in a global free market economy. This would account for the links

between high ELP levels and globalization that my comparison analysis in chapter three

demonstrates.

The success and growth of a language and its speakers, is linked with the political and

economic clout of its speakers. I consider this hypothesis and investigate the perception that

the fortunes of languages are tied to power of their speakers.

3. An increase in the economic strength or GDP growth of a country can lead to an

increase in the number of learners of that country’s national language.

4. A small population can encourage higher ELP usage while a larger population can

often mean a lower level of ELP.

Finally I raise my fourth hypothesis as a proposal to account for ELP scores in certain

countries. In chapter three we address this in detail.

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Structure

This thesis is structured into four chapters. The chapter one introduces the concept of

a global language, the development of ELP as an essential skill of human capital, the

relationship of languages to globalization. Closing with the dependent, independent variables

and hypotheses of this thesis.

Chapter two consists of an extensive literature review on debates over the relationship

of languages and development. It covers areas such as the knowledge economy, globalization,

lingua fracas, communication costs, cultural impacts of English, foreign direct investment,

historical precedents, imperialistic and neutral attitudes towards English.

The third chapter gives an empirical analysis of economic development indicators

with ELP scores. It consists of a methodology introduction, globalization rankings of states

with ELP scores, ease of business rankings, GNI, FDI, GDP, population and export levels. It

calculates the relationships of two different ELP testing systems, IELTS and TOEFL with the

same economic development indicators (and population figures). It adjusts each series of

countries so that they can match the same indicators. It then discusses the results and notes

limitations of the data provided.

Chapter four comprises of a conclusions and final analysis of the research of this

thesis. It suggests areas for further study and concludes that are sufficient relationships to

support research question of this thesis.

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Conclusion

Now that we have given an outline of the arguments and content in this chapter, I shall

progress to chapter two. In it we provide an extensive literature review on the relationship of

global languages to FDI and globalization: to provide awareness of current debates on

language, development and globalisation. As billions learn English they do so for its promise

of a better life. We need to know if their dreams in some way match their reality.

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Chapter two

Literature Review

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Introduction

Current literature on the relationship of global languages and globalization debates the

practical merit or ‘imperialist imposition’ of global languages. It contrasts economic growth

and its advantages for development with the death of languages and their culture. While there

is some literature that links globalization and languages, there has been little discussion on the

relationships between English proficiency levels, globalization and economic development.

This matters as the link between the ability of countries to improve their growth and use

human capital is generally established. Yet the degrees to which states are able to improve

with a specific skill, like an international language, are not fully understood. It is found that

they do assist trade and FDI, but none (aside from the 2011 EF proficiency report) investigate

the scale of their impact in different states. That is why we attempt to address that impact in

this thesis. Since globalization often means that states are highly geared for development, the

question arises, are English speaking countries more likely to be developed ones?

As trade is conducted through language, it is arguably a logical influence on economic

development. Similarly, it stands to reason that, global languages or lingua francas in turn

influence global trade. Trade and increased communication/relations between states can be a

mutually beneficial enterprise. As unfamiliar parties learn the value of trusting one another,

they open new markets and become less hostile to one another. According to Gartzke’s (2007:

182) capitalist peace theory, “Free markets and development (…) lead nations closer together,

or (...) down grade historical animosities” 22. Which means that on average democracy and

free trade promote peace building. For such peaceful relationships to establish and prosper,

they must open dialogues. Often when their languages are very different, a lingua franca is

sometimes used. According to House (2003: 557), a lingua franca is an intermediary

22 Gartzke, Erik. (January 2007) ‘The Capitalist Peace’. American Journal of Political Science. 51(1): 182

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language that is characterized by its negotiability, openness to integration with other

languages and variability of speaker proficiency23. Variability of speaker proficiency is what

concerns us in this thesis, as its possible impacts on development are substantial.

However, before I address that relationship specifically, I shall discuss some current

debates in contemporary literature concerning this topic to add context to my research in

chapter three.

Attitudes towards English

In chapter one I mentioned how human capital skills such as ELP are seen as

commodity in themselves and how human capital can raise income opportunities in a state.

This is reflected in the attitudes of ESL learners. For instance, according to Katsos (2011: 3)

the world has become a place where “speaking English is becoming a basic skill rather than

an advantage”24. Particularly when a “growing number of universities require English for

admission or graduation, and many now offer degree programs entirely in English to compete

with the top-ranked institutions in the U.S and U.K”25. Boyle (1997: 177) argued concerning

the Hong Kong case,

“Hong Kong Chinese have always used the English language very pragmatically – as a means of doing

better; business and secondly, that those with English quickly felt a sense of superiority over others. In

other words, though there was no compulsion to learn English (education was voluntary), the

commercial usefulness and the social prestige of the English language made it a highly desirable

commodity.”26

23 House, Juliane. English as a lingua franca: A threat to multilingualism? Journal of Sociolinguistics 7:4, 2003 p. 557 24 English First, English Proficiency Index 2011 p. 3

25 Ibid 26 Boyle, Joseph. 1997 ‘Imperialism and the English Language in Hong Kong’. Journal of Multilingual and Multicultural Development. 18 (3): 177

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Furthermore through the examples cited by Nunan’s (2003) report on English in the Asia-

Pacific region we can see that the age at which a number of states begin their public education

programs of the English language has fallen or remains to be low27. Despite this, authors like

Graddol (1997: 62) disagree and call it dangerous.

Public attitudes towards massive language loss in the next few decades, for example, is [sic]

unpredictable. It would be easy for concerns about this issue to become incorporated into the wider

environmental consciousness, which seems to be spreading around the world. The spread of English

might come to be regarded in a similar way as exploitative logging in rainforests; it may be seen as

providing a short-term economic gain for a few, but involving the destruction of the ecologies which

lesser-used languages inhabit, together with the loss of global linguistic diversity.28

The relationship of cultures to languages is central to literature in this subject. Thus we

counter Graddol’s views by showing how current literature agrees with us, that global English

is ‘a good thing’ within the context of my globalised society and that it is the nature of

languages to adapt to new circumstances.

English as a neutral language

With pragmatic attitudes characterising the growth of English, it is no wonder that it

often viewed as a neutral language, (akin to the manufactured language Esperanto). In this

section I argue that positive views of English are due to its relationship to international trade

and investment, painting the English language as a neutral agent in globalization.

The fundamentals of business have always been the same, trust and agreement

between different partners are always required before any trade or investment can be made.

27 Nunan, David. "The Impact of English as a Global Language on Educational Policies and Practices in the Asia-Pacific Region." TESOL Quarterly 37.4 (2003): 589-613

28 Cited by Maurais, Jacquest. Morris, Micheal A. Languages in a Globalising World. Cambridge University Press 2004 p.49

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That trust requires a medium to convey and propagate itself, language. Adler (2001: 215)29

found that trust, is becoming the most crucial cost between trading actors, whereas authority

is a weaker factor in international trade. Language Selmier & Oh (2012) argue, is the key

component in building of trust between investors. Business, like language goes through a

variety of new circumstances that can require adaptation. In any market the cheapest medium

of exchange is required and used before others. English, as a language of multiple origins and

widespread usage is often the cheapest medium of exchange.

With billions of English speakers now in existence and so few native speakers, it

stands to reason that the majority of English conversations happen without non-native

speakers. Pidgin English or regionalised variations are an important result of those types of

conversations, promoting a ‘cultureless’ language30. Furthermore, when non-native speakers

use a foreign language more often, the idioms within that language are diluted down so that

communication is prioritized rather than idiomatically correct expression.

Idioms themselves are what make a language an expression of a culture. Though they

are not necessary per se for basic communication in trade. Meaning that the more a language

is used outside of its socio-linguistic setting among native speakers, the less tied it becomes to

them. Therefore as English has become such a widespread trade language its quotidian, non-

native usage makes it arguably less culturally threatening to others. Furthermore English-

speaking countries often have a ‘low context culture’ that requires less cultural acquaintance

to understand them than a ‘high context culture’ like that of Japan’s would. As Selmier & Oh

(2012: 27) theorised,

29 Adler, Paul S. Market, Hierarchy, and Trust: The Knowledge Economy and the Future of Capitalism. Organization Science. 12: 2 Mar – Apr 2001 p. 215

30 Selmier, W.T. and Oh, C.H. 2012 ‘The Power of Major Trade Languages in Trade and Foreign Direct Investment, Review of International Political Economy.’ Review of International Political Economy, iFirst p.3

27

“There may be a higher incentive to learn English in the Spanish world because there is less cultural

cohesion. Spanish speakers identify with their own country – Argentine, Cuban, Spanish, Mexican –

rather a cultural, and linguistic, center as there exists with French.”31

This suggests that in cases where a language is not a defining or central aspect of nation state,

the protective urge to maintain it can be less than when language and nationality are highly

interlinked, as in France for example.

“English’s variety of cultures, on the other hand, may positively influence the adoption of English as a

lingua franca. Because underlying cultures in English language transactions are contextually specified

by the contracting parties—South African, Indian, Australian, Jamaican and so on—a particular cultural

orientation is not imposed. Analogous to Abdelal and Meunier’s view contrasting the French desire for

codification of global investments’ rules of the game with an American predilection for a laissez faire

approach to global capital flows, so English transactional use may be relatively more laissez faire. That

is, English usage may assume a less culturally grounded position in international economic transactions

than would French usage32.”

Despite English’s low culture context it is argued that it encourages the phenomenon

of ‘language death’. Where a minor language’s community dies off due to the idea that

speaking the language in question, isn’t useful anymore when a majority speak another

dominant language. Echoing the fears of Graddol, Johnstone (2000: 159) states,

“The seemingly irresistible rise of global English forces other languages on the defensive, as they strive

to maintain their space in a rapidly changing world. All countries are affected, but particularly those

where English is the majority first language. To what extent will serious and large-scale social

motivation for learning other languages be able to survive among first-language English speakers over

the next fifty years? (Johnstone 2000: 159)”33

31 ibid p.21 32 ibid p.3 33 Maurais, Jacquest. Morris, Micheal A. Languages in a Globalising World. Cambridge University Press 2004 p.25

28

Since a language can uniquely express the culture it came from and states often arise from a

distinct culture, nationalism can label English as a threat. As when one learns a language from

a particular country one invariably learns or absorbs many aspects of that language’s

culture34. So when people are drawn to another language out of economic concerns, it can

mean that identity with a culture of a less widely spoken language is not worth maintaining.

Thus when culture and identity become homogenised in globalization, languages can die out.

This runs counter to my proposal that English’s growth in globalization can be a good thing.

“This global spread of English has been described as linguistic imperialism (Phillipson, 1992), the

thesis being that English, under the innocuous guise of a helpful language for business and travel, has

become a potent weapon for cultural and economic domination. Others see the spread of English more

positively, maintaining that the English language has become globalized for historical and practical

reasons, and that it can help the development of poor countries without necessarily endangering their

cultures (Quirk & Widdowson, 1985).”35

Since I am asking if higher degrees of ELP are linked with economic development and

globalization, I describe ‘the good’ as from economic growth and increased employment.

Wherein, I see English as neutral or beneficial, not imperialistic. Nederveen Pieterse (2004)

reminds us that no culture remains static, describing globalization as hybridization, where

local cultures adapt globalization differently, discounting globalization as imperialist. House

(2003: 575) also finds that “English as a lingua franca is not, for the present time, a threat to

multilingualism”36. This reminds us that all languages arise from the environment of their

speakers. For example, if speakers of a particular language live in an isolated community,

they would adapt their language to suit the distinct characteristics of their community.

Concurrently the speakers of a global language adapt their languages to that the diverse

34 MacNamara, John. 1971’The Irish Language and Nationalism’. The Crane Bag. 1(2) :42 35 Boyle, Joseph. 1997 ‘Imperialism and the English Language in Hong Kong’. Journal of Multilingual and Multicultural Development. 18 (3): 169 36 House, Juliane. English as a lingua franca: A threat to multilingualism? Journal of Sociolinguistics 7:4, 2003 p. 575

29

global community. Meaning that, in essence all languages are practical expressions of new

circumstances. Arguably, the fast adopters of new linguistic elements or new languages have

‘the edge’ over others. That is why I examine the possible effects of ELP levels, to investigate

this question.

Historical precedents

International trade has been in existence for thousands of years. Though up till

recently it was possible to avoid the global economy with isolationist policies known as

protectionism (increasing the price of imports) for example. Nowadays it is a substantial

economic challenge to remain self-sufficient, so countries have to look outward to grow.

Hence I ask, why it that way now and not previously?

“Whether we consider English a “killer language” or not, whether we regard its spread as benign

globalisation or linguistic imperialism, its expansive reach is undeniable and, for the time being,

unstoppable. Never before in human history has one language been spoken (let alone semi-spoken) so

widely and by so many.”37

Yet from history we know that no language has remained dominant for long.

“…Would the Latin forecaster, living on one of the seven hills more than two and a half thousand

years ago, have had the luck of being able to imagine the success of what would have been in today’s

terms only a regional language? Or a few centuries later, how to make the Near East student understand

the indispensable nature of Aramaic, the great international language of the times, and then what to

answer if he had retorted disdainfully that this language would no longer be spoken two thousand years

later, except in a few villages of northern Syria?”38

37 Fishman, Joshua A. The New Linguistic order. Foreign Policy, 113, Winter 1998-9. P.26 38 Maurais, Jacquest. Morris, Micheal A. Languages in a Globalising World. Cambridge University Press 2004 p.26 Citing Jacquois, Guy 1999, n,1

30

However, the concept of a global or common language has been in existence for millennia.

Latin vulgaris’s usage across the Roman Empire aided the unification of the vast empire by

easing distant trade links by breaking communication barriers within a diverse imperial army;

establishing a hegemony of commerce and culture over ancient Europe. Overall, common

languages have arisen from one form of power, economic and/or political. The question

before us is whether, if English follows this strict pattern, validating hypothesis three?

An increase in the economic strength or GDP growth of a country can lead to an increase

in the number of learners of that country’s national language.

Language costs

The effect of particular languages on economic development and international

investment arises from their effect as mediums of communication. Selmier & Oh (2012)

establish that there is a preferential, lower cost to speaking English or lingua francas in the

course of international trade and investment relative to other languages. This allows us to

understand how languages can be tied to economic development. In this section, I discuss

their findings at length as they provide a strong context to build my central argument that

degrees of ELP can be related to economic development.

“When the respective peoples of a country-pair engage in bilateral trade and investment and speak

different languages, they must negotiate in one or both of those languages, or in a lingua franca.2

When the two languages – a ‘language pair’ – are the same or very similar, there is little linguistic im-

pediment to trade and investment as transaction costs decline (Helliwell, 1999; Hutchinson, 2002; Oh

and Selmier, 2008). But as the distance between the language pair increases, transactions costs mount

up. (…)Portuguese and Spanish speakers may easily communicate by bridging the short distance

between their language pair; this capacity to bridge exists for other tightly grouped language clusters

aside from the Romance languages. But with very distant languages like French and Chinese, language

31

learning or use of a lingua franca is required.”39

Table 1 Language distance between key languages.

Source: Selmier, W.T. and Oh, C.H. 2012 ‘The Power of Major Trade Languages in Trade and Foreign Direct Investment, Review of International Political Economy.’ Review of International Political Economy, iFirst p.5

Table 1 demonstrates there is a smaller cost distance between what they term ‘major trade

languages’, languages that are spoken in a number of different countries. Which means that

between speakers of say Greek and Chinese, they are likely to use one of the major languages

to bridge the greater transaction costs, as they are the most distance of language pairs40.

39 Selmier, W.T. and Oh, C.H. 2012 ‘The Power of Major Trade Languages in Trade and Foreign Direct Investment, Review of International Political Economy.’ Review of International Political Economy, iFirst p.7 40 ibid p.12

32

Table 2 Trade Language hierarchy

Source: Selmier, W.T. and Oh, C.H. 2012 ‘The Power of Major Trade Languages in Trade and Foreign Direct Investment, Review of International Political Economy.’ Review of International Political Economy, iFirst p.19 Table 2 demonstrates the effect linguistic transaction costs and their effect on both trade and

FDI. Save with Spanish and Arabic, their gravity analysis finds that major languages are often

associated with improved FDI. They also find no difference in international trade levels from

languages (with the notable exception of English). Which is crucial as this supports their

argument that FDI is affected by language. For example, a non-major trade language country

trades 67% more with an English speaking country versus an Arabic speaking one. It would

also invest 80% more FDI with an English speaking country than an Arabic speaking one

(ceteris paribus)41.

Table 3 Summary of transaction costs effects on language

41 ibid p. 21

33

Source: Selmier, W.T. and Oh, C.H. 2012 ‘The Power of Major Trade Languages in Trade and Foreign Direct Investment, Review of International Political Economy.’ Review of International Political Economy, iFirst p.20

Table 3 summarises the findings of Selmier & Oh (2012). The variable ‘same language’

refers to when a country has the same official language as another42. ‘Language distance’

refers to “the continuous linguistic distance between language pairs as measured by similarity

of words (rather than on the basis of grammatical similarity or language tree estimation)”43.

Their findings on ‘direct communication’ or “the percentage of speakers in both countries

who can communicate directly”44 show us the effect of multilingualism in countries on both

trade and FDI.

Multilingualism or the speaking of numerous languages is my second variable45.

Which is crucial to this thesis, as arguably multilingualism levels can be changed by a state’s

education policies, and thus possibly impacting long-term economic growth. Selmier & Oh’s

(2012) findings of such a significant relationship between languages, trade and FDI; infer that

learning languages matters to economic growth. Which support hypothesis one:

Foreign direct investment, international trade and globalization levels are higher in

states with higher levels of English Language proficiency (ELP).

If speakers of a minor language wish to conduct international trade or investments they may

likely use an international language. The economic transactions of FDI and international

42 ibid p.9 43 ibid p. 10 44 ibid p.9 45 “The speaking of a non-major or major trade language in a state”.

34

trade, ultimately affect economic development, as Borensztein. De Gregorio. & Lee (1998:

121) uncover46. They demonstrate that FDI has a positive impact on economic growth and on

human capital levels, after controlling for initial income, human capital, government

consumption and the parallel market premium for foreign exchange47. As the higher the level

of human capital in the host country, the higher the effect of FDI has on economic growth48.

Dreher (2006) and Dollar (1992) further find that there is a relationship between globalization

and economic growth. Therefore I include the findings of their analysis; to show how higher

ELP levels generally mean higher economic development levels.

Second languages and the status quo

“Historically, speaking a second language – or more specifically speaking a highly

valued second language- was a marker of the social and economic elite”49. The Tables 4, 5,

and 6 are extracts from a study that demonstrates such a hypothesis and my thesis as a whole.

In effect showing that richer states generally speak better English50. It must also be noted that

the figures are not statistically controlled, as the test takers were people able to afford the test

and motivated to study for it by professional or academic reasons. Thereby not providing a

complete picture of the ELP of learners in the surveyed states. Yet the results are stimulating

as The Economist reviewed the same report,

“Finally, one surprising result is that China and India are next to each other (29th and 30th of 44) in the

rankings, despite India’s reputation as more Anglophone. Mr Hult says that the Chinese have made a

broad push for English (they're "practically obsessed with it”). But efforts like this take time to

46 Borensztein, E. De Gregorio, J. Lee, J-W. 1998 ‘How does foreign direct investment affect economic growth?’ Journal of International Economics. 45. 121

47 Ibid p. 123 48 Ibid p.121 49 English First, English Proficiency Index 2011 p. 6 http://www.ef-ireland.ie/sitecore/__/~/media/efcom/epi/pdf/EF-EPI-2011.pdf p.5 Accessed 3/6/11

50 R.L.G. Who Speaks English? Economist, The. 5th April 2011

35

marinade through entire economies, and so may have avoided notice by outsiders. India, by contrast,

has long had well-known Anglophone elites, but this is a narrow slice of the population in a country

considerably poorer and less educated than China. English has helped India out-compete China in

services, while China has excelled in manufacturing. But if China keeps up the push for English, the

subcontinental neighbour's advantage may not last”51.

This infers that ELP can be used as the tool of development, whereby with apt investment in

education; states can develop on a level playfield of ELP. Yet as Inglewood & Woodward

(1967: 40) found in less development countries when social mobility is low, a second

language (such as English) can be key to securing government employment, a strong

indication of social mobility and elite status52. Though if equal status is granted to the major

languages in a state, a language (like English) can become a neutral factor in social

mobility53. Next I analyse Tables 4, 5 and 6 from the ‘English First 2011’ English proficiency

report and note that generally speaking, richer countries are more likely to speak better

English.

Table 4 EF English Proficiency Index, Score levels and rankings

1 Norway 69.09

23 Italy 49.05

2 Netherlands 67.93 24 Spain 49.01

3 Denmark 66.58

25 Taiwan 48.93

4 Sweden 66.26

26 Saudi Arabia 48.05

5 Finland 61.25 27 Guatemala 47.80

6 Austria 58.58 28 El Salvador 47.65

7 Belgium 57.23

29 China 47.62

8 Germany 56.64 30 India 47.35

9 Malaysia 55.54

31 Brazil 47.27

10 Poland 54.62

32 Russia 45.79 11 Switzerland 54.60

33 Dominican Republic 44.91

12 Hong Kong 54.44

34 Indonesia 44.78 13 South Korea 54.19 35 Peru 44.71 14 Japan 54.17 36 Chile 44.63 15 Portugal 53.62

37 Ecuador 44.54 16 Argentina 53.49 38 Venezuela 44.43 17 France 53.16 39 Vietnam 44.32

18 Mexico 51.48

40 Panama 43.62

51 ibid 52 Woodward, Margaret. Inglehart, Ronald F. October 1967 ‘Language Conflicts and Political Community’. Comparative Studies in Society and History. 10(1): 40 53 ibid p. 45

36

19 Czech Republic 51.31

41 Colombia 42.77 20 Hungary 50.80

42 Thailand 39.41

21 Slovakia 50.64 43 Turkey 37.66

22 Costa Rica 49.15

44 Kazakhstan 31.74 Source: English First, English Proficiency Index 2011 http://www.ef-

ireland.ie/sitecore/__/~/media/efcom/epi/pdf/EF-EPI-2011.pdf p.5 Accessed 3/6/11

Table 5 Exports and ELP scores interrelated

54

Source: English First, English Proficiency Index 2011 http://www.ef-ireland.ie/sitecore/__/~/media/efcom/epi/pdf/EF-EPI-2011.pdf p.6 Accessed 3/6/11

37

Table 6 GNI and ELP scores

Source: English First, English Proficiency Index 2011 http://www.ef-ireland.ie/sitecore/__/~/media/efcom/epi/pdf/EF-EPI-2011.pdf p.7 Accessed 3/6/11

Table 4 provides us with a ranking of ELP levels in 44 different countries, though a direct

comparison of those rankings to economic development indicators to support my first

hypothesis is needed. Table 5 and 6 do that by establishing a positive correlation between the

export levels, GNI levels and ELP scores. I find export levels to be a valid variable as they

easily demonstrate the size of the economies in question.

Since Tables 4, 5 and 6 provide the only currently available research on the

relationship of high ELP scores and economic development indicators, I believe that a

broader range of indicators is needed to substantiate hypothesis one instead. My literature

review suggests that the most significant relationship of languages and development can be

seen in FDI levels. For language plays a crucial role in the establishment of trust and the

38

long-term success of FDI and in turn FDI plays an important role in improving living

standards. Therefore, further research in this area is warranted and I investigate how FDI can

be related to ELP scores in chapter three.

Why is there a relationship between ELP and economic development?

So far I have explained how current literature finds how languages grow with globalization

and international business. Though before I investigate the broader relationships of ELP

scores I need to address why such relationships arise in order to provide a complete picture of

the processes at work. While the ‘low cost’ and perceived neutrality of English are important

factors accounting for part of its growth, there are diverse ranges of factors involved. I will

explore some of those possible reasons such as English language media, population,

education spending, and the relationship of economic clout to a languages ‘soft’ power.

Language growth and economic growth, intertwined?

As I hypothesize, languages skills help to facilitate a globalised economy making

employment sectors of that country more adaptable and open to trade with other countries.

Dreher (2006: 38) finds a positive correlation between globalization levels of states and their

economic growth rates: meaning that if languages facilitate globalization by facilitating trade,

through the “lowered transaction costs” that Selmier & Oh (2012) discuss55. I can associate

improved social mobility levels from the overall increase in income levels that economic

growth would provide. Graddol56 citing Ammon (1995: 30) states,

55 Selmier, W.T. and Oh, C.H. 2012 ‘The Power of Major Trade Languages in Trade and Foreign Direct Investment, Review of International Political Economy.’ Review of International Political Economy, iFirst

56 Graddol, David. The Future of English?: a Guide to Forecasting the Popularity of English in the 21st Century. London: British Council, 1997 p. 28

39

“The language of an economically strong community is attractive to learn because of its business

potential, knowledge of the language potentially opens up the market for producers to penetrate a

market if they know the language of the potential customer.”

Furthermore Fishman (1999: 26) found that English-speaking countries account for

approximately 40% of the world’s gross domestic product.57 Therefore giving a direct

incentive for the rest of the world to adapt itself to such a massive market and learn how to

trade with it. Graddol cites Coulmas (1992), noting that the number of students learning

Japanese as a foreign language closely mirrored a rise in the value of the Japanese yen against

the US dollar58. Such a scenario can be further reflected with a rise in the number of students

of Mandarin, China’s national language after years of economic growth. Yet as The

Economist commented,

“The question remains whether the Mandarin rush will prove a fad. Japanese and Russian also had

“hot” periods, only to recede in popularity”59.

This raises a further question, are the motivations for learning English different to those of

Mandarin, Spanish or French? It is clear that it has had sustained growth for centuries though

one cannot fail to wonder, are its days, numbered with the rise of a rival power?

While Mandarin may be a strong lingua franca regionally for example, it pales in face

of the widespread and swift rise of English that was promoted by a prosperous colonising

state. Crystal (2003: 9) argued while colonial rulers may establish some languages; it takes

the strong economy of the colonising state to maintain and expand its language 60 .

Nevertheless any analysis of this hypothesis must consider the lack of sufficient statistical

57 Fishman, Joshua A. The New Linguistic order. Foreign Policy, 113, Winter 1998-9. P.26 58 Graddol, David. The Future of English?: a Guide to Forecasting the Popularity of English in the 21st Century. London: British Council, 1997 p. 28 59 "Mandarin's Great Leap Forward." The Economist 18 Nov. 2010 60 Crystal, David. English as a Global Language. 2nd ed. Cambridge, UK: Cambridge UP, 2003 p. 9

40

data on the amount of English language learners61, which I will attempt to address in chapter

three. In the case of English, I could account for its rise not just from colonisation, but also

from the colonised states themselves, like America for example. Thus for a language to grow

in strength, it may require adopter countries to use that language before it can reach the higher

status of a ‘global language’ above a major trade language or lingua franca. For the economic

attractiveness of a language to act on a global level, it must transcend mere regional usage to

become part of globalization.

Regional languages

Not all authors agree with my idea that ‘global languages’ help international business

and economic development, some argue that regional languages have a bigger impact over

peoples’ lives. Fishman (1999: 29) argues that they should on the basis that regional lingua

francas are central to promoting social mobility within the developing world62. He argues

that the only things that make a real, lasting difference on people’s lives are the growth in

regional interactions such as trade, travel, the spread of religions, interethnic marriages, as

they affect the widest variety of people. They do so by facilitating agricultural and

commercial expansion across local boundaries and foster literacy and education in highly

multilingual areas63. He argues that the spread of English is forever etched along social class

lines, age, gender and profession64. Thus English wouldn’t have the same impact on as many

people as a regional lingua franca would, according to him. That kind of analysis ignores the

wider system at work of inter-regional and global trade that has defined the twentieth century.

61 Graddol, David. The Future of English?: a Guide to Forecasting the Popularity of English in the 21st Century. London: British Council, 1997 p. 17 62 Fishman, Joshua A. The New Linguistic order. Foreign Policy, 113, Winter 1998-9. P.28-29 63 ibid P.31 64 ibid p. 28

41

For instance, within Europe German and French are confined to trade primarily within

Europe itself, but to trade outside of the EU companies often use English65. Clearly regional

languages play an important part of trade due to shorter socio-linguistic distance between

them. Yet English (within the EU) is the most studied language at every level of education66.

Clearly regional economic dominance is not a central tenant of a language’s growth globally

but a part thereof. If economic power determined the lingua franca of a region German or

French would be the language of Europe. Yet they are not, (French is the second most learned

foreign language across Europe)67. The immense scope of opportunities that English provides

globally over other European languages is a stronger carrot to most. Meaning that there are

limitations on my hypothesis four.

An increase in the economic strength or GPD growth of a country can lead to an

increase in the number of learners of that country’s national language.

Regional languages do play a role in development, though the carrot of global opportunities

has placed English above the normal circumstances that would support my fourth hypothesis,

since it is so widespread and grows from the numerous economies.

Cultural effects of English media

Globalization is arguably as much a change in global trade dynamics as it is a cultural

shift. So far in answering ‘why’ some countries appear to speak better English and are richer

than others. I discussed the concept of shared ideas through languages and the economic draw

of English. Though the effect of shared culture through mass media deserves consideration.

65 Ibid p. 29 66 Mejer, Lene. Boateng, Sadiq Kwesi. Turchetti, Paolo. Eurostat: Population and Social Conditions 49/2010 http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-SF-10-049/EN/KS-SF-10-049-EN.PDF Accessed 10/7/11 p.5 67 ibid

42

Tracey (1985: 22) found that most imported television programs around the world

originate from the United States68 and are therefore arguably are more likely to increase the

proliferation of English. Danan (2004: 73) notes its influence in The Netherlands through the

use of subtitling foreign media.

“Dutch children devote half of their television viewing time on average to subtitled programs (Koostra

& Benntjes, 1999: 59). In Belgium also, many children can speak and understand some English even

before they start learning English at school, presumably because of their frequent exposure to English-

language subtitled television programs (d’Ydewelle & Pavakanum, 1997: 146). As for adults, they

often view subtitling as a perk allowing them to learn or maintain their knowledge of a foreign

language, especially English, thanks to preference for subtitled programs in many countries. For

example, a 1977 survey conducted by the Dutch Broadcasting Service (NOS) revealed that 70% of their

spectators favoured subtitling, most often because it allowed them to increase their language

proficiency (De Bot et al., 1986:74)”69.

Such research suggests a link between cultural globalization and ELP. A further reason for

The Netherland’s high ELP rating could be its geographical position within Europe and its

history of international trading that has kept its economy ‘open’ to investments. Indeed due its

central position in continental trade routes and history of international trading it has in effect

advanced the case for multilingualism. Shorter linguistic distance between French, Dutch,

German and English makes it easier to learn each other according to Selmier & Oh’s findings

(2012). Increased exposure to English through subtitling of television facilitates a strong link

with globalization and higher ELP levels.

68 Tracey, Micheal. (1985) “The Poisoned Chalice? International Television and the Idea of Dominance”. Daedalus. 114: 4, 17-56 69 Danan, Martine. 2004 ‘Captioning and Subtitling: Undervalued Language Learning Strategies’. Translators’ Journal. 49 (1): 73

43

Social mobility from education, criticisms

So far I have posited that linguistic education is a good thing that leads to greater

socio-economic opportunities. Though some critique this view (concerning education in

general realist terms), Pennycock (1994: 48) states:

“The assumed causal link between education and development was rejected not because a critical

analysis of the role of education in capitalist societies suggested that it was a crucial factor in

reproducing social and cultural inequalities”70.

He further cites Bowles and Gintis (1976) and Bourdieu (1973) 71, who argue that education

has increased inequalities and that the developed world can’t gain any kind of economic,

social or political upper hand when investing in education. Secondly, those educational

systems in former colonies consolidate the culture and language of their former masters. I

reject those ideas. For in recent years the developing world is on average, growing

substantially, particularly due to the growth of China. The ‘Asia-tigers’ such as Taiwan and

South Korea lead the world in numerous industries, such as software development and

electronic manufacturing. Yet as I have already countered: languages like cultures are never

static and may take on new forms of identities. It is erroneous to suggest that learning less or

speaking more languages is a bad thing. Now that I have introduced some ‘whys’ English is

central to debates on globalization, economic development and human capital led growth, I

shall demonstrate some of the ‘hows’ in chapter three as the available literature has

insufficiently addresses the questions I raise.

70 Pennycook, Alastair. The Cultural Politics of English as an International Language. London: Longman, 1994. P. 48

71 ibid

44

Population

A final variable to account for higher ELP scores in some countries over others may

be population, my third independent variable and fourth hypothesis.

A small population can encourage higher ELP usage while a larger population can

often mean a lower level of ELP.

I draw that hypothesis after encountering research by Ginsburgh et al (2005).

“The larger the native population who speaks the language, the less speakers are prone to learn another

language; the more the foreign language is spoken, the more it attracts others to learn it; the larger the

distance between two languages, the smaller the proportion of people who will learn it.” 72

English has grown exponentially on account of its ubiquity, which gives it an unparalleled

economic draw. This idea can be understood in terms of markets: the larger the domestic

market in a country, the less people are drawn to learn a foreign language as they have

enough of a market (people) to let their business grow domestically without needing to

expand abroad. Whereas in a small country, it can be reasoned that since there is less of a

draw for FDI if they only speak their native language. The small state will accordingly be

drawn to learn the major language of the nearest and biggest markets, often the lingua franca.

In Chapter three I analyse population figures in my empirical analysis to provide further

investigation on this variable in Table 12.

72 Ginsburgh, Victor. Ortuno-Ortín, Ignacio. Weber, Shlomo. ‘Learning Foreign Languages. Theorectical and empirical implications of the Selten and Pool model’. Center for Economic Policy Research. Discussion paper No. 4942 March 2005 p.11

45

Conclusion

This chapter has given an outline of debates concerning why the English language has

grown and why it is related to globalization and international trade. I have touched on the

empirical research as to how some languages will prosper before others on account of

international trade and investment. This being due to the ‘lower cost’ of lingua francas in

trade and why the growth of languages is often tied to the economic clout of its speakers.

Central idea reasoning is that, “the merchant speaks the customer’s language”73 and

the need for trust to build relationships between potential investors is paramount. I raised the

connection of languages and ideas as much of the literature cites the growth of global English

as cultural death. I then countered by citing historical precedents of cultural and linguistic

changes. Since languages are often tied to economic fortunes, I raised hypothesis four and

discussed the effects of colonialism and global trade.

I then touched on the idea that languages grow with respect to the economic clout of

their speakers. Then I raised the concern that regional languages, have a greater effect on

development. I countered that ‘regionalism not globalization’ idea by noting how English is

the most popular language in the EU instead of French or German. Which demonstrate the

draw of a global language over regional languages. I considered the effect of English

language media aid English’s growth. Then I countered the assertion education enforces

inequalities and finally, I raised the population variable in language growth.

73 Graddol, David. The Future of English?: a Guide to Forecasting the Popularity of English in the 21st Century. London: British Council, 1997 p. 29

46

This chapter concludes that many authors consider English as a language of power and

progress74. Some oppose it on anti-imperialist grounds, while many view it as a practical

asset. An idea based on a substantial relationship between globalised growth and the increase

in English language. Next I will demonstrate further research of my own on those

relationships to consolidate this thesis.

74 Pennycook, Alastair. The Cultural Politics of English as an International Language. London: Longman, 1994. p. 13

47

Chapter three Empirical findings and analysis

48

Introduction

As I have demonstrated in my literature review there is a lack of in depth investigation

on the effect of ELP scores to economic development indicators. Thus I provide a greater

degree of indicators than the English First report in chapter two provides. This will allow a

more convincing picture of the strong relationship of ELP to economic development.

Methodology

As aforementioned in chapter one, I build my case by demonstrating how highly

proficient non-native English speaking countries are often highly globalised and

economically developed. I do this through a ranking scheme in Table 7 that has removed the

native English speaking countries of the TOEFL ELP scores so that one can observe the

relationship effectively.

Due to incomplete data, not all countries were chosen for correlation coefficient

analysis. I needed to do this due to the limited range of consistent data on all countries. I

provide IELTS ELP scores and economic indicators in Table 9. Then, I selected 36 countries

that have all of available economic development indicators that I use. I also selected the

closest available time series (2011 and 2010) so that one can compare as many states’ ELP

scores as possible. Afterward I exported that data to Microsoft Excel so that I could calculate

a correlation coefficient. I also provide evidence for those calculations with Table 8 that

shows the full data sets.

In Table 11, I employed the same methodology with TOEFL scores so that I could

account for an equal picture of states’ ELP scores with the indicators available from the

World Bank. However, I used the 2010 time series for the economic indicators, as more

statistics were available from that year than from 2011.

49

I selected IELTS as a testing system of ELP as it has the largest international usage,

over one million test takers in 200875. Furthermore, the testing system comprises of speaking,

writing, reading and listening abilities: a wide range criteria to determine ELP. They also

found that a slight majority, 51% undertake the test in order to study in a foreign university76.

However, for the purpose of my statistical analysis, the IELTS data is limited. First, the test

ranges from a band of 1 as the lowest to 9 as the highest possible score. Making the accuracy

range of IELTS scores limited. Secondly, there is a smaller range of countries available for

analysis from their aggregate database. To address this problem, I constructed Table 7 and 10

that use the more extensive TOEFL ELP scores to demonstrate the relationship of

globalization to economic development. I include it so that one may view the significant

relationship of globalization to ELP scores and trade. I also include the available economic

globalization rankings so that I may provide further material to address my two main

hypotheses.

1. Foreign direct investment, international trade and globalization levels are higher in

states with higher levels of English Language proficiency (ELP).

2. Higher FDI levels are often linked with higher globalization figures.

Indicators used

I add export levels as most states aim for a level of import-export balance in their

trade levels, in order to tip trade exchanges in their favour. They aim to make the language

effect reciprocal leading to a greater reliance on lingua fracas for promoting multi-lateral

trade due to its lower costs77. Furthermore when a transnational corporation is located in a

75 IELTs press release 4/6/08 www.ielts.org accessed on 27/8/12 www.ielts.org/Docs/press_release_London_27_nov_2008.doc 76 ibid 77 Graddol, David. The Future of English?: a Guide to Forecasting the Popularity of English in the 21st Century. London: British Council, 1997 p. 29

50

non-English speaking country, joint ventures between different parts of the company’s

branches from different countries tend to adopt English as their working lingua franca78.

Thereby making export levels a useful economic development indicator to my empirical

analysis. GDP per capita is used to determine the size of the state’s economy. GNI per capita

is utilised to judge the economic capita of individuals in the state in question. Population

figures are used to address my third independent variable to determine if population has an

impact on ELP scores. Finally, FDI inflow figures are given as FDI proved central to my

literature review in chapter two.

Table 7 TOEFL ELP scores, Ease of business rankings and Globalization scores

ELP Ranking Country

TOEFL iBT mean ELP scores 2010

KOF Globalization Index score 2011

Ease of business rankings 2011 1=most friendly to business

1 Netherlands 100 91.16 25

2 Denmark 99 88.26 3

3 Singapore 98 84.39 1

4 Austria 98 91.67 26

5 Belgium 97 92.6 22

6 Finland 95 86.43 7

7 Germany 95 85.1 14

8 Slovenia 95 79.88 31

9 Switzerland 95 88.97 20

10 Luxembourg 94 85.62 44

11 Portugal 94 87.28 24

12 South Africa 93 68.81 29

13 Estonia 93 80.22 19

14 Iceland 93 73.71 6

15 Israel 93 74.2 28

16 Zimbabwe 92 48.48

17 Argentina 92 46.68 107

18 Costa Rica 92 67.12 115

19 India 92 42.74 126

78 Ibid p. 32

51

20 Norway 92 83.23 4

21 Sweden 92 89.26 9

22 Czech Republic 91 86.33 58

23 Romania 91 71.25 66

24 Croatia 90 75.95 74

25 Slovakia 90

26 Greece 89 76.98 94

27 Hungary 89 45

28 Italy 89 81.12 81

29 Monaco 89

30 Malaysia 88 73.22 13

31 Pakistan 88 39.92 99

32 Faroe Islands 88

33 Poland 88 79.66 56

34 Belarus 87 40.35 63

35 Bulgaria 87 75.12 53

36 France 87 87.65 23

37 Serbia 87 62.27 86

38 Spain 87 84.71 38

39 Nicaragua 86 58.7 112

40 Paraguay 86 54.15 96

41 Puerto Rico 86 37

42 Latvia 86 70.32 16

43 Lithuania 86 73.64 21

44 French Polynesia 86

45 Swaziland 85 54.56 118

46 Brazil 85 52.43 120

47 Jamaica 85 82

48 Mexico 85 58.37 47

49 Peru 85 35

50 Cyprus 85 82.81 34

51 Aruba 84

52 El Salvador 84 67.71 106

53 Macedonia, FYR 84 65.87 17

54 Russian Federation 84 48.96 114

55 Ukraine 84 62.09

56 Ecuador 83 50.47 124

57 Venezuela 83

58 Bangladesh 83 33.34 116

59 Bosnia and Herzegovina 83 64.76 119

52

60 Moldova 83 69.51 75

61 Lebanon 83 98

62 Madagascar 82 46.51 131

63 Bolivia 82 58.8

64 Chile 82 74.68 33

65 Cuba 82

66 Panama 82 68.64 55

67 Bhutan 82 136

68 Sri Lanka 82 43.52 83

69 Guatemala 81 59.13 91

70 Hong Kong 81 2

71 Korea, RO 81 61.59 5

72 Armenia 81 61.41 49

73 Montenegro 81 67.95 50

74 Egypt 81 49.01 104

75 Colombia 80 52.61 36

76 Dominican Republic 80 59.46 102

77 Eritrea 79

78 Kenya 79 40.03 103

79 Nigeria 79 61.61 127

80 Kyrgyzstan 79 60.88 64

81 Nepal 79 28.88 101

82 Georgia 79 11

83 Iran 79 25.69 138

84 Zambia 78 68.11 78

85 Indonesia 78 61.73 123

86 Kazakhstan 78 66.6 41

87 Korea, DPR 78

88 Turkey 78 54.25 65

89 Bahrain 78 69.67 32

90 Morocco 78 49.85 88

91 Tunisia 78 62.5 40

92 Uganda 77 48..25 117

93 China 77 50.88 85

94 Uzbekistan 77

95 Albania 77 58.7 76

96 Jordan 77 73.2 90

97 Syria 77 38.31 128

98 Azerbaijan 77 62.46 60

99 Turkmenistan 76

100 Ethiopia 76 25.11 105

53

101 Thailand 75 67.05 12

102 Algeria 75 49.16

103 Macao 75

104 Myanmar 74

105 Oman 74

106 Afghanistan 74

107 Mongolia 73 63.18 80

108 Vietnam 73 59.28 92

109 United Arab Emirates 73 70.99 27

110 Mozambique 73 57.9 133

111 Iraq 72

112 Sudan 72 129

113 Yemen 72 54.05 93

114 Congo, DRC 72

115 Kosovo 71 111

116 Qatar 71 69.57 30

117 Cameroon 71 40.81

118 Togo 71 51.68

119 Japan 70 69.13 15

120 Kuwait 70 64.6 61

121 Guinea 70 50.07

122 Sierra Leone 70 37.28 135

123 Gabon 69 48.21

124 Rwanda 69 29.61 39

125 Tanzania 68 35.28 121

126 Libya 68

127 Angola 68 71.39

128 Liberia 68

129 Niger 67 27.23

130 Lao, PDR 67

131 Burundi 67 28.22

132 Cape Verde 67 56.23 113

133 Cote D'Ivoire 66 51.4

134 Tajikistan 66 141

135 Burkina Faso 66 37.74

136 Congo 66 58.59

137 Saudi Arabia 65 8

138 Benin 65 37.1

139 Senegal 65 42.54

140 Chad 64 40.7

54

141 Cambodia 64 61.89 141

142 Honduras 63 67.36 142

143 Mali 63 46.69 143

144 Mauritania 62 63.52 144

Sources: Test and Score Data Summary for TOEFL Internet-based and Paper-based Tests, January 2010-December 2010 Test Data. www.ets.org/Media/Research/pdf/TOEFL-SUM-2010.pdf Accessed 6/6/11

Dreher, Axel. Does globalization affect growth? Evidence from a new index of globalization. Applied Economics, 2006, 38. P. 1091-1110. Updated in Dreher, Axel (2008) http://globalization.kof.ethz.ch/ Accessed 6/7/11

World Bank economic development indicators http://databank.worldbank.org/ddp/home.do Accessed 20/7/2012

Notes on Table 7

1. The globalization figures are taken from Dreher’s 2006 paper that has been updated

by the author to 2011 figures by his own index methodology. Furthermore, the data

from the TOELF scores are constructed from 2010 Internet based test data and the

IELTS data are from 2011. Therefore since this is the most current available data, for

the effective purposes of this research I consider it of the same year, since I am unable

to find more recent data.

2. TOEFL scores are arguably the most statistically valid. As they are a greater range of

EPL scores than the EF or IELTS data. TOEFL provides more data from a wider

sampling of test takers countries. Yet the IELTS test is the largest test in terms of

overall test takers.

3. The TOEFL data report states: “Because of the unreliability of statistics based on

small samples, means are not reported for subgroups of less than 30. Due to the

rounding section scores means may not add up to the total score mean.”79

79 Test and Score Data Summary for TOEFL Internet-based and Paper-based Tests, January 2010-December 2010 Test Data. www.ets.org/Media/Research/pdf/TOEFL-SUM-2010.pdf Accessed 6/6/11 p.9

55

4. The states in the tables include only those that are comparable with one another. For

example, a state that is included in the TOEFL scores but not in the other tables of

globalization or ease of business rankings, has been left in to aid comparison.

Conversely states that are not included in the TOEFL scores but that are mentioned in

the other rankings are omitted. This is because I view the TOEFL scores to be the base

of my empirical analysis.

5. Native English speaking states of: Ireland, Canada, USA, UK, Australia and New

Zealand have been omitted. Other English speaking states such as Singapore, South

Africa and Jamaica were also considered though due to their multi lingual make up

they were included in the indexes.

6. As the TOEFL data states in reference to its current publication, “because of changes

in region and/or country boundaries, certain countries may have been added or deleted

since the previous table was published”80.

7. The ease of business rankings are also modified by excluding the native English

speaking countries.

Table 7 data analysis

Table 7 provides a compelling case for hypotheses one and two81 as numerous

developed states match high scores on the globalization index and ease of business rankings.

In Table 7 I can see that The Netherlands is highest in the TOEFL scores table with an

average 100% ELP score. Furthermore it has a score of 67.93 and places second on Table 4 in

the English First rankings. In Table 7 it is third on the globalization index with a rating of

91.16. Denmark provides another strong example of hypotheses one and two, from Table 7. It

80 ibid 81 1. International trade, investment and globalization levels are higher in states with higher levels of English language proficiency (ELP). 2. Higher FDI levels are often linked with higher globalization figures.

56

is also 6th on globalization index with a score of 88.96 and ranks an impressive 3rd on the

World Bank’s ease of business rankings. Furthermore when one observes the relationship

with the first twenty-four countries’ globalisation scores and their TOEFL EPL scores; a

correlation coefficient of 0.541590993 can be calculated, a considerable relationship.

A number of states on Table 7 also suggest that emergent economies are more often

globalised ones that most likely have been aided by higher levels of ELP. For example, I can

see that Costa Rica has a high score on the overall TOEFL score of 92 and an EF score of

49.15 (from Table 4) suggesting that it has a moderately high level of average proficiency. It

also ranks it 49th place on the globalization scale with a 67.12 score, implying that there is a

link between levels of English and globalization. However the ELP score difference indicate

a degree of disparity between the EF and TOEFL testing systems. That is why I also

investigate the IELTS ELP scores on Table 8 and provide my economic comparisons on

Table 10 with the TOEFL data.

In Table 7 I have provided further evidence for my research question and hypothesis one.

“Do economically strong globalized states often speak more English than less strong

ones?”

Foreign direct investment, international trade and globalization levels are higher in

states with higher levels of English Language proficiency (ELP).

The test takers of EF, TOEFL and IELTS are not a neutral statistical sampling; therefore they

do not provide a complete picture of national ELP levels. For example the test takers may

have had access to the Internet and the resources to fund their testing and test preparation. For

example the TOEFL data report states:

“This is not a conclusive study of English learning levels, as there isn’t perfect statistical validity to

57

what is presented. This is due to the optional nature of the ELP testing systems that do not demonstrate

the full average ELP scores of a country. Rather only the scores of those motivated to learn English for

the purposes of academic or professional purposes. (…) The TOEFL test provides accurate scores at the

individual level”.82

Evidentially caution is required when looking at the data. For example, on Table 7

Nepal’s score of 79 in the TOEFL scores, 6.1 in the IELTS scores and a score of 28.88 on the

globalization index are a demonstration of the elite driven nature of the test and not a

demonstration of the counter hypothesis to my argument. For instance, I can clearly see a

pattern within the first twenty-four states in all columns of the table, richer states such as

Denmark, Singapore, Germany and Finland are high in all of the globalization scales and

IELTS and TOEFL scores83. Zimbabwe is a deviant case in Table 7, as it is ranked 16th on

the TOEFL score rankings and 97th on the globalization rankings. However overall, Table 7’s

finding agrees with hypothesis one, that globalised states (as demonstrated by the index)

generally speak more English than less globalised states. To provide further investigation of

hypothesis one and two I now present Table 8.

Table 8 IELTs ELP scores and economic development indicators

Country Population total, 2011

IELTS general score 2011

FDI inflow 2010 current US$

GDP 2011 per capita current US$

GNI 2011 PPP current US$ Exports 2010 US$

Bangladesh 150493658 5.9 916907186.4 674.9316307 2.91453E+11 18471882567

Brazil 196655014 6.4 48506489215 10992.94249 2.26106E+12 2.32982E+11

China 1344130000 6 1.85081E+11 4432.963557 1.13254E+13 1.7524E+12

Colombia 46927125 5.8 6899263970 6237.515632 4.52399E+11 45380913791

Egypt 82536770 6.2 6385600000 2698.365074 5.08214E+11 46732278108

France 65436552 6.8 33671510316 39170.2647 2.34635E+12 6.51676E+11

Germany 81726000 6.8 46957103440 39851.67172 3.28332E+12 1.52605E+12

82 Test and Score Data Summary for TOEFL Internet-based and Paper-based Tests, January 2010-December 2010 Test Data. www.ets.org/Media/Research/pdf/TOEFL-SUM-2010.pdf Accessed 6/6/11 p.9 83 A correlation coefficient analysis of those 24 countries was 0.541590993

58

Hong Kong SAR 7071600 6.4 71066137585 31757.81138 3.64103E+11 5.00452E+11

India 1241491960 6.1 24159180720 1375.391157 4.48804E+12 1.73899E+11

Indonesia 242325638 6.3 13770580771 2951.699149 1.09892E+12 3.83542E+11

Iraq 32961959 5.8 1426400000 33788.44828 1.24322E+11

Italy 60770000 6.2 9593553196 2532.323671 1.96609E+12 5.4373E+11

Japan 127817277 5.8 -1358906189 43063.13637 4.53899E+12 8.33704E+11

Jordan 6181000 5.9 1701408451 4369.998242 36898591838 12628309859

Kenya 41609728 6.9 185793189.9 794.7672094 71728066834 8861227717

Korea, Rep. 49779000 5.5 -150100000 20540.17693 1.50771E+12 5.31504E+11

Lebanon 4259405 6.2 4279880835 9226.570859 59623069442 8169900000

Malaysia 28859154 7 9167201907 8372.830966 4.38252E+11 2.31385E+11

Mauritius 1286051 6.4 431046226.2 7583.893655 18983704424 5098199088

Mexico 114793341 6.3 20207632419 9132.807729 1.73621E+12 3.13742E+11

Nepal 30485798 6.1 87816143.56 534.5219887 38512780350 1533378052

Nigeria 162470737 6.4 6048560295 1242.479795 3.74124E+11 74609666790

Pakistan 176745364 6.1 2018000000 1018.872762 5.09615E+11 23971198055

Philippines 94852030 6.1 1298000000 2140.121591 3.94938E+11 69463700090

Romania 21390000 6.3 2941000000 7539.357263 3.23882E+11 37961046651

Russian Federation 141930000 6.3 43287698500 10481.36702 2.84525E+12 4.44611E+11

Saudi Arabia 28082541 4.5 21560173333 16423.44024 6.98484E+11 2.61859E+11

Singapore 5183700 7.4 38638121024 41986.82583 3.0992E+11 4.41593E+11

South Africa 50586757 7.6 1224280433 7271.729185 5.4568E+11 99398844054

Sri Lanka 20869000 6.1 478212000 2400.015575 1.15991E+11 10746568194

Thailand 69518555 5.5 9678888214 4613.680162 5.83285E+11 2.27224E+11

Turkey 73639596 5.7 9038000000 10049.77356 1.23177E+12 1.5509E+11

Ukraine 45706100 5.8 6495000000 2973.981709 3.2378E+11 69227565815

United Arab Emirates 7890924 4.3 3948300000 39624.70188 3.80513E+11 2.31978E+11

Venezuela 29278000 6.3 1209000000 13657.74819 3.69504E+11 1.12424E+11

Vietnam 87840000 5.8 8000000000 1224.314518 2.86641E+11 82513451680

Sources: World Bank development indicator database http://databank.worldbank.org/ddp/home.do

Accessed on 20/7/2012

IELTS researcher data http://www.ielts.org/researchers/analysis_of_test_data/test_taker_performance_2011.aspx Accessed on 10/5/11

Test and Score Data Summary for TOEFL Internet-based and Paper-based Tests, January 2010-December 2010 Test Data. www.ets.org/Media/Research/pdf/TOEFL-SUM-2010.pdf Accessed 6/6/11

59

Table 8 & 9 analyses

Table 8 gives a reliable comparison of 36 different states with fully available economic

indicators. Except for Iraq’s lack of exports figures, I have a complete comparison table of

every state that is included in the IELTS ELP data scores and the economic indicators that I

use. Table 8 is similar to Table 7, in that economically developed states have high ELP

scores. In Table 8, South Africa is scores the highest with 7.6, Singapore at 7.4, Kenya at 6.9,

followed by France and Germany at 6.8. While concurs with my two hypotheses,

1. International trade, investment and globalization levels are higher in states with

higher levels of English language proficiency (ELP).

2. Higher FDI levels are often linked with higher globalization figures.

However when I conducted a correlation coefficient analysis of IELTS scores and the

economic indicators that I use from Table 8 in Table 9: I found a weak level of correlation

compared to my findings in Table 11 that I could attribute to the small range of IELTS ELP

data and the rounding of its figures. However the relationship between FDI, exports and the

IELTS were comparatively notable. Encouraging further investigation with Table 10 and 11.

Table 9 Data correlation coefficient results of Table 8

Source: Author’s own calculations of World Bank development indicators Table 10 TOEFL ELP scores and Economic development indicators

Country TOEFL 2010 2010 Exports US$

2010 FDI net inflows US$

2010 GNI per capita US$

2010 GDP per Capita

Afghanistan 73 2670470341 75650000 910 501.4709467

Albania 77 3508993945 1109557915 8570 3700.738411

Algeria 75 49938918451 2264000000 8060 4566.891032

Angola 68 51400292557 -3227211182 5170 4321.940845

-0.021965527 Population and IELTS 0.09632064 FDI and IELTS -0.022366986 GDP and IELTS -0.004645775 GNI and IELTS 0.078825344 IELTS and Exports (excluding Iraq)

60

Argentina 92 80043129709 7055069167 15500 10749.31922

Armenia 81 1928926296 570060000 5640 3030.710627

Austria 98 2.03243E+11 -25153777821 39800 44885.06082

Azerbaijan 76 28553375431 563132000 9240 5843.169753

Bangladesh 83 18471882567 916907186.4 1810 674.9316307

Belarus 87 29886285425 1402800000 13590 5818.854859

Belgium 97 3.7307E+11 82462818286 38330 42832.59415

Benin 65 936849439.4 110930000 1580 741.0655283

Bolivia 82 8093221710 621997989.5 4620 1978.854327

Bosnia and Herzegovina 83 5955887016 231539217.4 8870 4427.347194

Brazil 85 2.32982E+11 48506489215 11000 10992.94249

Bulgaria 87 27570997535 1591245986 13510 6334.618296

Burundi 67 124343225.4 780825.7578 580 241.7868565

Cambodia 63 6080130482 782596735 2070 795.166471

Cameroon 71 6501821162 -551206.6745 2260 1147.021568

Cape Verde 66 639847023 111703556.8 3690 3344.87221

Chad 64 3330988372 781366889.6 1360 760.7122667

Chile 82 82330531713 15094834931 14950 12639.5243

China 77 1.7524E+12 1.85081E+11 7600 4432.963557

Colombia 80 45380913791 6899263970 9020 6237.515632

Congo 65 3411898423 2939300000 330 198.7321643

Congo, DRC 71 10221068040 2815957839 3180 2970.116262

Costa Rica 92 13640920839 1465630464 11290 7773.858272

Cote D'Ivoire 66 9315998520 417933000 1800 1161.249284

Croatia 90 23320067333 426682082 18680 13773.59519

Cyprus 85 9279470199 819564071.1 30910 28779.18555

Czech Republic 91 1.34132E+11 6119055112 23540 18789.00148

Denmark 99 1.57125E+11 -7697030326 41100 56278.44032

Dominican Republic 80 11481949390 1625800000 8990 5195.381237

Ecuador 83 19103445000 167296320.4 7850 4008.237964

Egypt 81 46732278108 6385600000 6030 2698.365074

El Salvador 84 5552600000 -5260000 6460 3460.023288

Estonia 93 14948279058 1539110037 19370 14045.11527

Ethiopia 75 3392169510 288271568.3 1030 357.8563044

Finland 95 94867105263 6870329271 37080 44090.89689

France 87 6.51676E+11 33671510316 34760 39170.2647

Gabon 69 8093879755 170389956.2 13070 8767.825851

Georgia 79 4059158671 816708508.8 4950 2613.695828

Germany 95 1.52605E+12 46957103440 38100 39851.67172

Greece 89 64315599807 429954077.7 27640 26432.96568

Guatemala 81 10666425078 881100000 4630 2873.077445

61

Guinea 70 1649040967 101350000 990 474.4617421

Honduras 63 6731398760 797390628.3 3750 2018.750027

Hong Kong 81 5.00452E+11 71066137585 47270 31757.81138

Hungary 89 1.11324E+11 -37597531717 19550 12863.13383

Iceland 93 7045743381 257515912.2 29350 39463.29089

India 92 3.83542E+11 24159180720 3340 1375.391157

Indonesia 78 1.73899E+11 13770580771 4190 2951.699149

Israel 93 80166087189 5152200000 25760 28522.40858

Italy 89 5.4373E+11 9593553196 31740 33788.44828

Jamaica 85 3645758140 227673925.7 7470 5133.439301

Japan 70 8.33704E+11 -1358906189 34780 43063.13637

Jordan 77 12628309859 1701408451 5810 4369.998242

Kazakhstan 78 65073958807 10768153371 10620 9069.701969

Kenya 79 8861227717 185793189.9 1640 794.7672094

Korea, RO 81 5.31504E+11 -150100000 28830 20540.17693

Kuwait 70 74701813893 80846012.18 53820 45436.79018

Kyrgyzstan 79 2665553547 437586100 2070 880.0385141

Lao, PDR 67 2552473194 278805903.1 2400 1158.129965

Latvia 86 12919973607 369000000 16630 10723.35626

Lebanon 83 8169900000 4279880835 13820 9226.570859

Liberia 67 247410315.5 452342327.6 440 247.3384639

Lithuania 86 24897747765 748454521.2 18010 11046.05185

Luxembourg 94 87415526316 2.07871E+11 61250 104512.1799

Macao 74 30048946188 3486768874 57060 51998.90783

Macedonia, FYR 84 4347688440 207463067.1 11100 4434.48891

Malaysia 88 2.31385E+11 9167201907 14160 8372.830966

Mauritania 61 2240980906 13630000 2400 1044.54796

Mexico 85 3.13742E+11 20207632419 14400 9132.807729

Moldova 83 2299814038 197410000 3370 1631.522436

Mongolia 73 3391588818 1454687963 3660 2249.7659

Montenegro 81 1461700220 760440979.5 12790 6509.717447

Morocco 78 29965343108 1240626688 4580 2795.490228

Mozambique 72 2420628563 789018866.4 900 393.7180597

Nepal 79 1533378052 87816143.56 1210 534.5219887

Netherlands 100 6.04271E+11 -11043033128 41810 46597.08625

Nicaragua 86 2809891330 508000000 2660 1138.633074

Nigeria 79 74609666790 6048560295 2140 1242.479795

Norway 92 1.71883E+11 11746956827 57910 85443.05939

Pakistan 88 18440415000 2018000000 2780 1018.872762

Panama 82 10466631771 2350100000 13050 7614.009247

Paraguay 86 39500954610 346900000 5050 2840.072686

62

Peru 85 1.98463E+11 7328242370 9320 5292.341261

Poland 88 70474736842 9104000000 19180 12303.20792

Portugal 94 74999056000 2668170586 24600 21358.41422

Puerto Rico 86 37961046651 5534454212 76470 25862.72675

Romania 91 4.44611E+11 2941000000 14300 72397.6124

Russian Federation 84 610248325.1 43287698500 19210 7539.357263

Rwanda 68 2.61859E+11 42332000 1150 529.3949489

Saudi Arabia 65 3186285178 21560173333 23150 16423.44024

Senegal 64 13406750508 237194664.8 1910 1033.905319

Serbia 87 326570899 1340235811 11090 5272.527513

Sierra Leone 69 4.41593E+11 86590238.69 820 325.4793668

Singapore 98 70748189404 38638121024 56890 41986.82583

Slovakia 90 30689633907 553142912.2 21870 16036.06927

Slovenia 95 99398844054 366161963.2 26530 22897.93876

South Africa 93 3.736E+11 1224280433 10330 7271.729185

Spain 87 10746568194 41161190258 31420 30026.38553

Sri Lanka 82 13242039827 478212000 5040 2400.015575

Sudan 72 2026700000 2063730998 2020 1538.312702

Swaziland 85 2.29674E+11 135660413.7 5570 3503.160366

Sweden 92 2.83514E+11 -1863474311 40120 49257.08104

Switzerland 95 20894549330 21706578420 49960 67644.33095

Syrian Arab Republic 77 857757705.7 1469196863 5090 2892.755148

Tajikistan 66 5974746148 15787600 2120 820.1831211

Tanzania 68 2.27224E+11 433441913 1430 526.5582778

Thailand 75 1185065422 9678888214 8150 4613.680162

Togo 70 21569372642 41057614.71 990 526.9118545

Tunisia 77 1.5509E+11 1400866285 8960 4193.55474

Turkey 78 10347000000 9038000000 15460 10049.77356

Turkmenistan 76 4086685236 2083000000 7460 3966.823004

Uganda 77 69227565815 543872727.3 1250 514.5119517

Ukraine 84 2.31978E+11 6495000000 6590 2973.981709

United Arab Emirates 73 12269017466 3948300000 46990 39624.70188

Uzbekistan 77 1.12424E+11 822000000 3150 1377.082143

Venezuela 83 82513451680 1209000000 12040 13657.74819

Vietnam 73 9462293188 8000000000 3060 1224.314518

Yemen 72 7141796220 55732515.44 2470 1290.623077

Zambia 78 3608136285 1729300000 1370 1252.696534 Sources: World Bank development indicator database http://databank.worldbank.org/ddp/home.do Accessed 20/7/2012 Test and Score Data Summary for TOEFL Internet-based and Paper-based Tests, January 2010-December 2010 Test Data. www.ets.org/Media/Research/pdf/TOEFL-SUM-2010.pdf Accessed 6/6/11

63

Table 11 Correlation coefficient findings from Table 10 0.049887534

Population and TOEFL scores (full country data set was available)

0.212915805

Exports of goods and services US$ and TOEFL scores

0.163211395

FDI net inflows US$ and TOEFL scores

0.533703925

GNI per capita US$ and TOEFL scores

0.530097905

GDP per Capita Current US$ and TOEFL scores

Source: Author’s own calculations from Table 10 data Table 10 and 11 analyses

From my calculations of Table 10 in Table 11 I can in effect, concur the findings of the EF

report from my literature review, that economically developed states exhibit high ELP scores.

Notably GDP per capita and GNI per capita demonstrate the strongest relationship. Yet,

intriguingly FDI bears a relatively low relationship to TOEFL ELP scores, suggesting a

weaker relationship than my literature implies. Nevertheless, it is clear there is relationship

between economic development and ELP scores according to those figures: in part

confirming hypothesis one.

International trade, investment and globalization levels are higher in states with higher

levels of English language proficiency (ELP).

Next, I shall investigate hypothesis four by comparing population levels to TOEFL ELP

scores have a bearing on each other. So that I may grasp a wider understanding of how ELP

has grown around the world.

Table 12 Population and TOEFL score rankings

Country TOEFL score 2011

Population 2010

Monaco 89 35407

Faroe Islands 88 48708

Aruba 84 107488

French Polynesia

86 270764

Iceland 93 318041

Cape Verde 66 495999

Luxembourg 94 506953

2

Macao 74 543656

Montenegro 81 631490

Bhutan 82 725940

Swaziland 85 1055506

Cyprus 85 1103647

Bahrain 78 1261835

Estonia 93 1340161

Gabon 69 1505463

Qatar 71 1758793

Kosovo 71 1775680

Slovenia 95 2048583

Macedonia, FYR

84 2060563

Latvia 86 2239008

Jamaica 85 2702300

Kuwait 70 2736732

Mongolia 73 2756001

Oman 74 2782435

Armenia 81 3092072

Albania 77 3204284

Lithuania 86 3286820

Mauritania 61 3459773

Panama 82 3516820

Moldova 83 3562062

Puerto Rico 86 3721978

Bosnia and Herzegovina 83 3760149

Liberia 67 3994122

Congo, DRC 71 4042899

Lebanon 83 4227597

Croatia 90 4418000

Georgia 79 4452800

Costa Rica 92 4658887

Norway 92 4889252

Turkmenistan 76 5041995

Singapore 98 5076700

Eritrea 79 5253676

Finland 95 5363352

Slovakia 90 5430099

Kyrgyzstan 79 5447900

Denmark 99 5547683

Nicaragua 86 5788163

Sierra Leone 69 5867536

Togo 70 6027798

Jordan 77 6047000

El Salvador 84 6192993

Lao, PDR 67 6200894

Libya 68 6355112

Paraguay 86 6454548

Tajikistan 66 6878637

Hong Kong 81 7067800

Serbia 87 7291436

United Arab Emirates 73 7511690

Bulgaria 87 7534289

Honduras 63 7600524

Israel 93 7623600

Switzerland 95 7826153

Burundi 67 8382849

Austria 98 8389771

Benin 65 8849892

Azerbaijan 76 9054332

Sweden 92 9378126

Belarus 87 9490000

Dominican Republic 80 9927320

Bolivia 82 9929849

Guinea 70 9981590

Hungary 89 10000023

Czech Republic 91 10519792

3

Tunisia 77 10549100

Rwanda 68 10624005

Portugal 94 10637346

Belgium 97 10895785

Chad 64 11227208

Cuba 82 11257979

Greece 89 11315508

Senegal 64 12433728

Zimbabwe 92 12571454

Zambia 78 12926409

Cambodia 63 14138255

Guatemala 81 14388929

Ecuador 83 14464739

Mali 62 15369809

Niger 67 15511953

Kazakhstan 78 16323287

Burkina Faso 66 16468714

Netherlands 100 16615394

Chile 82 17113688

Angola 68 19081912

Cameroon 71 19598889

Cote D'Ivoire 66 19737800

Syrian Arab Republic

77 20446609

Sri Lanka 82 20653000

Madagascar 82 20713819

Romania 91 21438001

Mozambique 72 23390765

Yemen 72 24052514

Korea, DPR 78 24346229

Saudi Arabia 65 27448086

Malaysia 88 28401017

Uzbekistan 77 28562400

Venezuela 83 28834000

Peru 85 29076512

Nepal 79 29959364

Morocco 78 31951412

Iraq 72 32030823

Uganda 77 33424683

Sudan 72 33603637

Afghanistan 73 34385068

Algeria 75 35468208

Poland 88 38183683

Argentina 92 40412376

Kenya 79 40512682

Tanzania 68 44841226

Ukraine 84 45870700

Spain 87 46070971

Colombia 80 46294841

Myanmar 74 47963012

Korea, RO 81 49410000

South Africa 93 49991300

Italy 89 60483385

France 87 65075569

Congo 65 65965795

Thailand 75 69122234

Turkey 78 72752325

Iran 79 73973630

Egypt 81 81121077

Germany 95 81776930

Ethiopia 75 82949541

Vietnam 73 86927700

Mexico 85 113423047

Japan 70 127450459

Russian Federation 84 141920000

Bangladesh 83 148692131

Nigeria 79 158423182

Pakistan 88 173593383

Brazil 85 194946470

4

Indonesia 78 239870937

India 92 1224614327

China 77 1337825000

Source: World Bank development indicator database http://databank.worldbank.org/ddp/home.do Accessed 20/7/2012

Test and Score Data Summary for TOEFL Internet-based and Paper-based Tests, January 2010-December 2010 Test Data. www.ets.org/Media/Research/pdf/TOEFL-SUM-2010.pdf Accessed 6/6/11

Table 12 presents a weak argument for hypothesis four. When I calculated the correlation

coefficient of the 2010 population figures and 2010 TOEFL scores, using the same

methodology as applied in Table 11: I attained the following figure 0.049887534. That figure

shows that must disregard hypothesis four, as the correlation coefficient is quite low.

2

Conclusion

The low variance levels and rounding of averages in the ELP scoring systems don’t

allow for controlled analysis, yet they are the best available samplings of ELP scores

currently available. In contrast, the economic development indicators show wide degrees of

variance between different countries’ levels of economic development. Until a more

comprehensive selection of international ELP scores are available, this is best available

method of finding out how well do people in some countries speak English on average.

Despite the limitations of the data, they demonstrate that for some countries ELP on average

is more difficult to obtain.

Chapter three has provided some evidence to support my hypothesis one:

1. Foreign direct investment, international trade, and globalization levels are higher in

states with higher levels of English language proficiency (ELP).

The parallels between high globalization rankings and ELP scores on Table 7 suggest a

significant relationship between trading openness and the use of English. However not all

aspects of this hypothesis were supported by my research, for instance on Table 9 the

correlation coefficient analysis of IELTS ELP scores and my selection of economic

development indicators, showed a weak relationship. Which could be accounted for with the

low (1-9) range of IELTS scores compared to the extensive (1-100) range of the TOEFL

scores. Nevertheless Table 11 supports hypothesis one by demonstrating notable coefficient

relationships between GDP per capita and GNI per capita matching TOEFL ELP scores. In

effect supporting the findings of Selmier & Oh (2012) of trade being ‘cheaper’ with certain

languages. However Tables 11 and 9 did not provide overwhelming evidence to support the

FDI and ELP relationship in hypothesis one.

3

Chapter 4

4

Thesis conclusions

In this thesis I have questioned whether economic development has a relationship on

ELP. I asked:

“Do economically strong, globalized states often speak more English than less strong

ones?”

The primary focus of this thesis has been to ascertain if economically developed

countries often have high ELP levels, to the best of the international data available. I found

that there is a degree of linkage that supports that proposition. As I addressed that proposition

and question of this thesis, I recognised ELP as a skill that creates the space for economic

growth and development through lowered communication costs. Furthermore, I found that

globalisation plays a role in the growth of English. Aside from the statistics I have presented

in this thesis, I have given you an extensive impression of how and why ELP matters has such

a central relationship with economic development. In this thesis I have also argued that by

and large, the growth of English is a positive thing for economic development.

No substantial previous study has attempted to link proficiency levels of a global

language to globalization, population, ease of business rankings, FDI, GDP per capita, GNI

per capita and export levels. Previous literature on this area has demonstrated that there is a

significant relationship between languages and FDI (such as Selmier & Oh 2012). Though

none have attempted to draw a wide representation of the impact of degrees of ELP on

economic development. It is interesting to note that Selmier & Oh’s (2012) research found a

stronger relationship between FDI and major trade languages in general: whereas my own

research did not find a substantial link between of ELP and FDI.

5

Globalisation and economic development utilize numerous languages. Debate rages

over whether you could call English the global language or not. Yet the facts remain. English

is more influential and widespread than any other language, as my literature review has

demonstrated. One could argue that this thesis has merely examined the ELP scores of elite

sections of various countries and not the general population at large. To address my research

question I utilized all available ELP data and addressed numerous variables.

For example: the exposure of the population to English media, geographical and

linguistic distance between the non-English speaking country and an English speaking

country. I was able to draw a clear relationship between economic development and ELP

scores from my own research on Table 11 by demonstrating significant relationships between

GDP per capita and GNI per capita matching TOEFL ELP scores.

While some may still consider English a worthless endeavour or imperialist, the

popular perception exists that learning English is worth its salt. As if the average ELP scores

were completely meaningless statistically each ELP testing system would have little relation

to each other. However they do: when I calculated the correlation coefficient of the thirty-four

countries that match each other’s IELTS and TOEFL scores I obtained a score of

0.736502368 which indicates a strong relationship between them: which can mean that both

TOEFL and IELTS scoring systems illustrate national ELP levels not just individual scores.

While the variable of ELP may well perform different roles within economic

development, my research indicates the following: highly proficient non-native English

speaking countries are likely to be highly economically developed ones, that are globalised

and supportive of business.

6

Suggestions for future study

Further study on this subject would warrant an independent survey of ELP levels

within non-English speaking states. That could assess the average proficiency of a state with

greater statistical accuracy. It would calculate the mean scores of 1000 students of English

from controlled groups of professions, education and income in order to give a complete

demonstration of ELP scores. The averages of those figures would allow for a greater

accuracy in understanding the effects of English on globalization and international

development. However, since in all three ELP testing systems that I research, a slight

majority of the test takers do so for higher education entrance84. Therefore it is clear that these

scores I research cover a particular demographic already. Yet statistical accuracy could be

best provided through controlled demographic sampling. The ages at which different

countries begin learning English should be examined extensively with respect to the ELP

scores from the controlled ELP testing system that I propose.

The value of English

Despite the problems it may raise, I value the possibilities that English can grant us.

Why? Because it has become an economic necessity, a part of basic education like literacy

and arithmetic. As in a global economy, there is a global language. To have an open economy

is to accept the merits of the ‘open’ global language, English.

Globalization has arisen from many sources though above all, communication. The

debate around the relationship between languages and globalization has led us to wonder if

languages are intrinsically tied to the power of economies or states. It is a difficult question to

quantify. The ‘language variable’ plays different roles by being an essential part of

globalization for the reasons I have discussed and discovered.

84 Ibid (78.3% of test takers did the 2011 IELTS academic test)

7

Throughout the processes of development and change in the world today, English is

arguably a major factor. My objective has been to point out the effects and dominance of

English in economic development and how both globalization and economic development are

affected by the ease of exchange and communication. The existence of a global unity through

language I argue is a good thing, not without its setbacks, but a means of making

globalization occur with a seductive promise, a level playing field for the world when all

speak the same tongue.

In the words of Jay Walker:

“English has become the international language of problem solving, not because America is pushing it

but because the world is pulling it. English mania is a turning point, like the harnessing of electricity in

our cities or the fall of the Berlin wall. English represents hope, for a better future, a future where the

world has a common language to solve its common problems”85.

85 Jay Walker on the world’s English Mania” http://www.ted.com/talks/jay_walker_on_the_world_s_english_mania.html Accessed on 20/07/2011

8

Appendix

The definition of FDI that I used is that of the World Bank. It defines FDI as;

“Foreign direct investment (net) shows the net change in foreign investment in the reporting

country. Foreign direct investment is defined as investment that is made to acquire a lasting

management interest (usually of 10 percent of voting stock) in an enterprise operating in a

country other than that of the investor (defined according to residency), the investor's purpose

being an effective voice in the management of the enterprise. It is the sum of equity capital,

reinvestment of earnings, other long-term capital, and short-term capital as shown in the

balance of payments.”86

I selected two arrays of data at a time and calculated the correlation coefficient from the

“Correl” function from Microsoft Excel.

86 World bank indicators and IMF data 2011 www.worldbank.org

9

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