University of Southampton 2015

FACULTY OF SOCIAL AND HUMAN SCIENCES

A Comparative Analysis of the Level of a State’s Economic Development with the Level of its Participation in Tertiary

Education.

By Mr James W Darnbrook

A dissertation submitted in partial fulfillment of the degree of MSC Education Practice and Innovation

By taught course

I confirm that the material contained in this dissertation is all my own work and where the work of others has been drawn upon, it has been properly acknowledged according to appropriate academic conventions. No portion of this work has been submitted or is currently being submitted in support of an application for another degree or qualification of this or any other university or institute of learning.

Signed:

AbstractKnowledge has become the key resource for the 21st century. The effect of

a tertiary educated workforce on the growth and development of both

Developing and Developed countries is now, more than ever, of critical

importance. Understanding the relationship between economic growth /

development and the application of a tertiary educated workforce into the

working environment will be a valuable policy tool for current and future

governments. This dissertation aims, through a comparative analysis of

several large data sets, to examine the relationship between tertiary

educated workers and the level of a country’s economic development. The

analysis is in three parts. The first part is a comparative snapshot of the

2012 data. The second part is a longitudinal correlational analysis from

2003-2012 and the third part is an abridged longitudinal correlational

analysis using data from 2003, 2007 and 2012.

This data analysis suggests a positive correlation between GNI per capita and school enrolment in tertiary education across all three income groups. It also demonstrates the positive correlation between Service sector growth and the level of labour force with tertiary education as well as the level of unemployed with tertiary education within the Service sector. The increased reliance on the Service sector for GNI growth across all three income groups may also indicate that a continued growth in enrolment in tertiary education is likely to occur.

Further cross-country research into the quality of the delivered tertiary education and its effective use as part of a company’s Intellectual Capital would help to support the results of this thesis.

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Acknowledgements

I would like to thank my supervisor Dr Chris Downey for his patience, guidance and support in ensuring that this thesis was submitted inline with the expectations of an MSc thesis and in a timely manner. I would also like to thank my wife Dr Cristina Costa Santini for her support and encouragement throughout the process of researching and writing this thesis.

Table of Contents

Abstract p. iAcknowledgements p. iiTable of Contents p. ii-iiiList of Figures p. iii-vList of Tables p. v-viList of Abbreviations and Acronyms p. v-vi

Chapter One Introduction1.1 Rationale pp. 1-41.2 Key Issues p. 41.3 Tertiary Education and the State pp. 4-51.4 Research Problem p. 51.5 Research Aims p. 61.6 Justification for the Research pp. 6-7

Chapter Two Literature Review2.1 Introduction p. 72.2 Human Capital Definition p. 72.3 Benefit to Society / Industry / Individual pp. 7-122.4 Global Skills Race pp. 12-142.5 Developing Systems to Compliment Human Capital pp. 14-162.6 Summary pp. 16-17

Chapter Three Research Methodology3.1 Introduction pp. 17-183.2 Description of Data Collection pp. 18-243.3 Data Analysis Methodology pp. 24-26

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3.4 Ethics / Risk Management p. 263.5 Summary pp. 26-27

Chapter Four Data Analysis4.1 Introduction p. 274.2 Analysis of the Relationship between Key Tertiary pp. 27

Educational Data / Value Added GDP Data and the respective World Bank Analytical Classifications.

4.3 Longitudinal Relationship and Correlation Analysis. pp. 34-364.4 Abridged longitudinal approach. Selected data pp. 37-

38from 2003, 2007 and 2012.

4.5 Summary p. 38

Chapter Five Discussion5.1 Introduction pp. 38-395.2 General discussion of results pp. 39-425.3 Limitations of the study p. 425.4 Implications for policy and practice p. 435.5 Implications for theory pp. 43-445.6 Recommendations for future research pp. 44-45

Chapter Six Conclusions6.1 Conclusions p. 456.2 Policy Recommendation 1 pp. 45-466.3 Policy Recommendation 2 p. 46

References p. 46-50

AppendicesAppendix A: Consolidated Data Tables p. A1- A12

List of FiguresFigure 1. The Average Earnings of Full-Time, Year-Round p. 11Workers as a Proportion of the Average Earnings of HighSchool Graduates by Educational Attainment: 1975 to 1999.

Figure 2. Contribution by Sector (Agriculture, Industry, p. 20Manufacturing and Services to Value Added % of GDP. Data Source World Bank.

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Figure 3. The Difference in Expenditure per Student- p. 26Tertiary (% GDP per capita) between Lower Middle Income, Upper Middle Income and High Income Countries (2012).

Figure 4. The Difference in School Enrolment - Tertiary p. 29(% Gross) between Lower Middle Income, Upper Middle Income and High Income Countries (2012).

Figure 5. The Difference in the Overall Level of Labour p. 30Force with Tertiary Education (% of total) between Lower Middle Income, Upper Middle Income and High IncomeCountries (2012).

Figure 6. The Difference in the Female Level of Labour p. 31 Force with Tertiary Education (% of total) between Lower Middle Income, Upper Middle Income and High IncomeCountries (2012)

Figure 7. The Difference in the Male Level of Labour Force p. 32with Tertiary Education (% of total) between Lower Middle-Income, Upper Middle-Income and High Income Countries (2012).

Figure 8. The Difference in the Level of Value Added % ofp. 33GDP (Agriculture, Industry, Manufacturing, Services) between Lower Middle Income, Upper Middle Income andHigh Income Countries (2012).

Figures 9 a,b,c. The Difference in the Level of Value Added pp. 34-35% of GDP (Agriculture, Industry, Manufacturing, Services)between Lower Middle Income, Upper Middle Income andHigh Income Countries (2012).

Figure 10. The Correlation between GNI per Capita, p. 36PPP (current international $) and School Enrolment in Tertiary Education (2003-2012).

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Figure 11. The Correlation between the Level of Value p. 37Added % of GDP (Agriculture, Industry, Manufacturing,Services) and the Expenditure per Student-Tertiary (% GDP per capita), the Unemployment with Tertiary Education and the Level of Labour Force with Tertiary Education (2003/2007/2012).

List of Tables

Table 1. Countries Represented in this Thesis. p. 17

Table 2. Extract from UN International Standard Industrial p. 19Classification of all Economic Activities Classifications.

Table 3. List of Data Sources Used. p. 21

Table 4. Country Classification Systems in Selected p. 23International Organisations. Nielson, (2011, p. 19).

Table 5. Difference in GNI per Capita 2003 – 2012, p. 24Data Source World Bank Analytical Classifications.

Table 6. Mean Average Scores in the Level of Value p. 33Added % of GDP (Agriculture, Industry, Manufacturing,Services) between Lower Middle-Income, Upper Middle-Income and High Income Countries (2012).

List of Abbreviations and AcronymsUnited Nations UNOrganisation for Economic Cooperation and Development OECDGross Domestic Product GDPUnited Nations Educational, Scientific and Cultural Organisation UNESCO International Standard Classification of Education ISCEDGross National income GNIWorld Economic Situations and Prospects WESPEuropean Union EUPurchasing Power Parity PPP

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International Monetary Fund IMFInternational Bank for Reconstruction and Development IBRD

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CHAPTER ONE INTRODUCTION

1.1 Rationale

In the 21st century the effective application of Human Capital within the work environment is commonly regarded as the most essential ingredient required for a company to grow and develop and in turn to encourage the economic growth of a country. According to Drucker (1993, p. 45) in his well-known work the Post-Capitalist Society, ‘That knowledge has become the resource, rather than a resource, is what makes our society post-capitalist. This shift from heavy industrial labour to a Service ‘knowledge based’ economy has been studied in some depth in the last 20 years. ‘The assertion that knowledge and brainpower supersede physical assets as the foremost source of competitive advantage is now commonly accepted in the management literature’ (Quinn, Anderson, & Frinkelstein, 1996, Stewart, 1997, cited Lee, 2012, p. 201). Indeed, according to a bibliometric study conducted by Timonen and Paloheimo (2008, p. 177), despite research on the subject of Knowledge work occurring across many scientific disciplines, most research on the subject occurs ‘inside the Management Domain’ examining the ways that ‘knowledge work is performed and managed as teams and project groups’, as well as the ‘implications and meaning of knowledge work in organizations’. This focus by Management Domain may serve to highlight the importance of the application of knowledge work to modern commercial productivity. Within the commercial sphere the application of Human Capital is referred to as part of a company’s Intellectual Capital, which is defined by Nahapiet and Ghoshal, (1998, p. 245) as ‘the knowledge and knowing capability of a social collectivity’. This Intellectual Capital is comprised of three distinct elements ‘Human, Organisational, and Social Capital. Lee, (2012, p. 202). A delicate interplay exists between these elements, however of these three elements Human capital can be considered as the most fundamental component of Intellectual capital (Bontis & Fitz-Enz, 2002, Edvinsson & Malone, 1997, Stewart, 1997, Sveiby, 1997, cited Lee, 2012, p. 202).

The importance of Human Capital to the knowledge economy in today’s society for both developing and developed countries cannot be understated. Businesses, Macro-Economists, International Development Agencies, National Education Authorities and Government Finance Ministries should all be focusing far more energy on the generation of appropriate Human Capital and its effective implementation within the workforce if they have a genuine desire

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to see economic growth and prosperity in an increasingly competitive environment. The current literature examines the role of Human Capital within specific work domains such as the biotech industry or within the borders of a particular country and much important research has been directed at measuring the effective application of Human Capital in a commercial environment. It is assumed that the aim of most countries is to move from being a Low Income country to a High Income country or to maintain their current position as a High Income country. It is also evidenced in this thesis that a definite move away from reliance on Agriculture and towards a growing Service sector occurs as a country develops. Many of the skills required to work effectively in the Service sector can only be achieved through tertiary education. This study is designed to examine at a gross international scale the relationship between tertiary education and the status of development of a country.

Education fulfills many roles for us as individuals and for society as a whole. At the individual level it may allow enlightenment or provide the individual with the skills required to pursue a rewarding and fulfilling life and career. The United Nations (UN) Universal Declaration of Human Rights Chapter 26 (2), 1948 clearly pronounces that education should be about more than just developing Human Capital. It states that;

Education shall be directed to the full development of the human personality and to the strengthening of respect for human rights and fundamental freedoms. It shall promote understanding, tolerance and friendship among all nations, racial or religious groups…

This UN article, adopted in 1948 alludes to the role education can play in developing greater harmony between nations. In today’s fractious societies, this lack of tolerance between nations applies equally between members of the same nation.

At the societal level, education allows for greater social mobility and offers a society a solid knowledge foundation from which to function. This foundation begins with the basics of education and the Center for Global Development, (2006, p. 1) cite ‘rapid and continuous growth can only be achieved when the adult literacy rate reaches at least 40%.’ Once this base level has been achieved within a society, then a greater focus and more resources can be

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placed on achieving universal primary and secondary education, and in time allow provision for greater segments of the society to enroll in tertiary education. This increased level of education leads inevitably to higher employment rates. Within the Organisation for Economic Cooperation and Development (OECD) 84% of those holding a tertiary qualification are employed, as opposed to only 57% of those with a secondary education qualification. According to Menne and Stein (2012, p. 2) Unemployment rates decrease as educational attainment increases with tertiary educated people earning over 50% more than those with lower educational qualifications.

This thesis will narrowly focus on the contribution of tertiary education to the economy of a functioning society. As previously discussed an educated population is one of the cornerstones required for an economy to develop and for the Gross Domestic Produce (GDP) of this society to grow. This economic growth then allows a society to develop or diversify its economy from primary, through secondary onto tertiary industries. This growth has the potential to bring greater stability to a country as well as offer an opportunity for a better standard of living. (Fisher, 1935 and Clark, 1940, cited in Schettkat and Yocarini, 2003, p. 7) both independently proposed the idea of a three-sector theory ‘which, in the course of economic progress, employment will first shift from agriculture to manufacturing, and then to services’. Accompanying this shift in focus through the different industrial sectors a society should also move from developing through transitional to developed nation status. In the 21st Century developing a strong knowledge economy is pivotal to achieving this shift. Powell and Snellman, (2004) define the Knowledge economy as:

Production and services based on knowledge-intensive activities that contribute to an accelerated pace of technical and scientific advance, as well as rapid obsolescence. The key component of a knowledge economy is a greater reliance on intellectual capabilities than on physical inputs or natural resources. Powell and Snellman, (2004, p. 199)

It must be recognized at this point that many other factors affect the growth of a society or country. Principally, the role of good governance, which based on sound democratic principles, can enable the provision of healthcare and education to all of its members, whilst supporting the growth of industry through non-corrupt institutions. This thesis aims to conduct a comparative analysis of a country’s economic development with the level of its participation

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in tertiary education only. Tertiary education as defined by the United Nations Educational, Scientific and Cultural Organisation (UNESCO), (2011, p. v); is the International Standard Classification of Education (ISCED) levels 5 – 8.

1.2 Key Issues

Two particular issues will be examined using the data; firstly, whether tertiary education can be correlated with the movement of a state upwards from being a primary economy to becoming a tertiary economy. Secondly, to see if the data shows any correlation between tertiary education and GDP / Gross National Income (GNI) growth.

1.3 Tertiary Education and the State

Higher Education Institutes contribute to the Knowledge Economy of a society by producing ‘future knowledge workers equipped with internationally competitive qualifications’. Jiang, (2008, p. 351). According to the World Bank (2000, p. 16) in order to make the shift ‘from subsistence farming, through an economy based on manufacturing, to participation in the global knowledge economy’ tertiary education is required to enhance the human capacity of a society’s members. The consensus is, that in order to be able to develop an economy fit for the 21st Century a high level of importance must be placed on developing the Human Capital of a country. However the matter is not so straightforward. Simply investing in more and more tertiary education may not of itself add value. Research by Zhang and Zhuang, (2001, p. 167) concludes that economic growth may require a specific value or level of Human Capital structure and that too much of the wrong level of Human Capital can be as deleterious as too little. Indeed they state that ‘the Human Capital structure may have an inverse-U-shape effect on economic growth’. In their study it was observed that the less developed areas of China benefited more from all levels of education whilst the most developed areas of China benefited most only from tertiary education. Research by Chi (2008, p. 422), suggests that it is the presence of, or rapid growth in, tertiary education that is a more important factor in determining decisions of capital investment. Despite being a major growth engine, this notion that capital investment is attracted to areas with a high level of tertiary education may be one of the factors that generate greater income inequalities within a country, as more investment will attract more tertiary educated workers, which in turn will attract even more investment.

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Simply generating more tertiary educated people within a country may not of itself achieve the move to a stronger knowledge economy. According to Drucker(1993, p. 166) the acquisition and distribution of knowledge is not of sole importance, rather it is the ‘application of knowledge once acquired’ which leads to productivity. In extremis the state can interfere with curricula to ensure it serves the needs of business. This may lead to an ‘agentic state which acts to subordinate the will of the individual to serve the economic needs of the state’. (Safstrom, 2005, cited in Kelly, 2009, p. 54). This involvement by the state in deciding the composition of the curricula can have a darker side when the state applies its will to deciding which of its citizens get to benefit from tertiary education. In some cases the state can use educational provision as a political tool. Brown (2010, p. 2) The unfair provision of education based along class, gender, ethnic or religious divisions has been recognized to lie at the root of many contemporary conflicts. Staying with the theme of educational provision and intra-state violence, Collier and Hoeffler (2011, p. 4) found econometric evidence that countries with lower rates of male secondary school enrolment are indeed more susceptible to violent conflict. Thus demonstrating that too little education can lead to violent conflict as particularly young males who have no other employment options turn to paid violence through membership of an armed gang as their principle source of revenue. Finally, it has been observed that many members of terror groups with a strong ideological background possess a high level of education. Rogers (2010, p. 149) notes that ‘many of those directly involved at the higher levels of Al-Qaida are well-educated. In one sense the attacks of 11 September really are an illustration of that uncomfortable revolution of frustrated expectations’.

1.4 Research Problem

This thesis will examine historical data sets on the provision of tertiary education throughout different selected countries. It will focus its analysis on the two principle areas discussed above; (i) encouraging a state to move through the different economic sectors, (ii) its correlation with GDP / GNI growth. This will be achieved through a comparative analysis of the level of a country’s economic development with the level of its participation in tertiary education.

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1.5 Research Aims

The principle aim of this primary research is comparative in nature. It aims to examine the relative amount of tertiary education conducted by members of each country and then to categorize these countries in order that they may be better compared. Research questions which should be established as a result of this research are:

a. Is there a correlation between a country’s level of participation in tertiary education and the ratio of primary, secondary and tertiary industries composing the State’s economy?b. How does participation in tertiary education differ between males and females amongst the different countries?

1.6 Justification for the Research

A truly integrated and global 21st Century society offers unprecedented opportunities and challenges to any country regardless of resource wealth to grow and develop. The role of Human Capital in this growth has also never been so acute. Once the world leader in producing graduates with at least an Associate Degree level qualification the USA has ‘now slipped to 12th place in the world rankings. Brynjolfsson, McAfee and Spence, (2014, pp. 51-52). This relative decline comes at a time when Human Capital is at the very essence of productivity and innovation.

Today, global wealth is concentrated less and less in factories, land, tools, and machinery. The knowledge, skills, and resourcefulness of people are increasingly critical to the world economy. Human Capital in the United States is now estimated to be at least three times more important than physical capital. A century ago, this would not have been the case. World Bank, (2000, p. 15)

This research is exploratory and may help to form a basis for deciding if further research in this area is warranted. Due to its exploratory nature any results shown may at best be labeled correlational and should not be mistaken for a causal relationship. The resulting analysis of this research may provide indicative forecasts for the effect of tertiary education in the support of the economic development of countries. It must be stressed that this research only

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examines the worldwide quantity and not the quality of tertiary education. As reported by Hanushek and Woessmann, 2007, p. 2) It is known that ‘Educational quality has a strong and robust influence on economic growth’ as well as educational quantity.

CHAPTER TWO Literature Review

2.1 Introduction

This review of the current literature will examine the notion of Human Capital and the importance of tertiary education in generating Human Capital. It will review the importance of tertiary education within a society, the economics of a country, as well as to the individual. It will also observe the global nature of developing Human Capital through participation in tertiary education examining some of the challenges that countries face in developing and using this Human Capital effectively.

2.2 Human Capital Definition

Human Capital, as defined by Blundell et al. (1999, p. 9) is the ‘early ability; qualifications and knowledge acquired through formal education; and skills, competencies and expertise acquired through training on the job.’ This definition highlights the duality of formal and informal, (on the job training), as both being important for the development of Human Capital as well as allowing for some innate or ‘early ability’. (Lucas, cited by Mgadmi and Rachdi 2014, p. 30) agrees that Human Capital is expressed by means of skills and competencies, however he stresses the social and economic benefits that the development of Human Capital facilitates. It is this generation or accumulation of Human Capital that is cited as being ‘the main reason of differences in standards of life among nations’. It is this connection between the development of Human Capital, specifically through tertiary education and the economic benefits that this thesis will examine.

2.3 Benefit to Society / Industry / Individual

Higher education has been viewed as pivotal to the development of young fledgling nations since at least as far back as the 2nd World War. After the dismantling of the major European Empires a new perspective was imposed on the world by the US. One of self-determination, which led to greater autonomy for many existing countries or the development of new countries. According to

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a report published by the World Bank (2000, p. 16) the newly independent countries ‘looked to their higher education systems to deliver support for national efforts to raise standards of living and alleviate poverty.’ The process of raising living standards and alleviating poverty were just two of the mechanisms that encouraging higher education were meant to use in order to achieve an even more fundamental, and some would argue, existential purpose for these countries. According to the same World Bank report (2000, p. 16) ‘in some cases, there was a belief that higher education could help make societies more democratic, while strengthening human rights’. It appears that in these cases higher education was viewed as a tool of the state that when wielded correctly would help the state to grow in a stable and democratic manner. Blundell et al (1999) support this notion explaining that.

There are obvious benefits to society from having an educated and literate population, including increased participation in democratic institutions and social cohesion. Blundell et al, (1999, pp. 14-15)

On the question of how tertiary education can have the most beneficial economic effects, the work by Blundell et al, (1999, pp. 17-18) correlates strongly with more contemporary work by Zhang and Zhuang, (2001, p. 167). Zhang and Zhuang noted that tertiary education had a greater impact in the developed areas of China and that more general primary and secondary education was needed in the less developed areas. Blundell et al, take a global view rather than the intra-state view of Zhang and Zhuang and state that ‘primary and secondary education skills are related to growth in developing countries’ whilst tertiary education skills are of most importance for ‘economic growth in the developed countries’. One of the drivers for the phenomenon that sees tertiary education contributing more to the economy of developed countries is related to the productivity of individual workers and the wage they receive. It is the high wages that already exist in developed countries that induces the development of more capital-intensive labour, which in turn economizes on the amount of individual labour required for any given task. This more capital intensive labour is realized through technological development which increases the work output of an individual worker, but places greater intellectual demands on that same worker. These technological developments and improvements in efficiency are referred to by Snellman, (2011, p. 209) as ‘multifactor productivity or the Solow residual.’ According to Allen, (2011, p. 47) this process can ultimately lead to ‘an ascending spiral of

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progress: high wages induce more capital intensive production that, in turn, lead to higher wages.’ Whereas in developed areas it is the high cost of Human Capital that encourages technological development which in turn leads to higher productivity for this expensive asset, Chani, Hassan and Shahid (2012, p. 12) in their paper on Human Capital Formation and Economic Development in Pakistan note that in a developing country such as Pakistan a ‘bi-directional relationship’ exists between developing Human Capital and economic development. They note that in Pakistan’s case either start point could be chosen, namely that more economic development would encourage more Human Capital development or vice versa.

The main issue with this virtuous ascending spiral of progress is its area of influence. Ultimately any economic gain made within the borders of a country should contribute to that country’s GDP. However if that wealth and job creation is limited to one or two regions of the country then inequalities in standards of living and economic development will occur. This inequality may ultimately lead to the exclusion of a large quantity of human and other capital resources from contributing to the GDP growth of the country as well as potentially lead to discontent between the regions of a country. According to Chi (2008, p. 422) China’s regional growth inequality is expected to increase rather than decrease in the future. This is due to the strange phenomenon, which sees Human Capital apparently ‘attracted’ to existing stocks of Human Capital. Therefore those areas with a high potential stock of tertiary educated workers will have ‘a large and significant impact on the later fixed assets accumulation’ Chi (2008, p. 434). As fixed assets are a finite resource, this will of course be at the expense of the other areas.

China's regional growth inequality may increase rather than decrease in the future. This is because eastern areas traditionally have a large stock of college-educated workers and … they are more likely to attract physical capital investment than primary and secondary education. Thus, rather than flow to the west voluntarily, physical capital investment will continue to build up in the eastern area where the Human Capital stock is larger and cause the eastern area to grow faster than the central and western areas. Chi, (2008, p. 422).

More broadly, the development of Human Capital has also been seen to have a positive economic effect in other countries. In Tunisia, it was seen to ‘exert a significant influence on economic growth,’ over the period from 1974 to 2012.

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Mgadmi and Rachdi, (2014, p. 30). In the developed world, (Jorgenson and Fraumeni, 1992, cited by Mgadmi and Rachdi, (2014, p. 31) reported that an incredible 61% of economic growth of the USA is provided by Human Capital and from the same paper Hall and Jones, (1999), reported that ‘In 1988, 22% of economic growth in 127 countries is deserved by Human Capital.’ Mgadmi and Rachdi, (2014, p. 31).

The benefits of well-trained Human Capital to the private companies that exist within a country are varied. Firstly, by introducing new technologies a company can become more efficient and as such more productive. Indeed, ‘demand for skilled workers has been shaped by the kinds of technologies that are permeating into modern workplaces.’ Machin and McNally, (2007, p. 8). This demand for more highly skilled workers also benefits these highly-trained individuals, as they will be economically more valued than lower skilled workers. It was noted by Blundell et al, (1999, pp. 14-15) that well-educated individuals working in an environment with less well-educated individuals demonstrate a degree of informal skills transfer, in that they ‘may improve not only their own productivity but also those of the less well-educated individuals with whom they work’. The final benefit of employing well-trained Human Capital is particularly pertinent to larger multinational companies. These firms already have a footprint in many countries and as such have access to pools of highly-trained Human Capital in many countries. This Human Capital advantage is being further refined as the multinationals begin to morph into transnational companies able to effectively engage in the ‘development of global webs of high, medium and low-skilled work that straddle national borders’, Brown, Lauder and Ashton, (2008, p. 9). This access to these ‘global webs’ of workers of various skill levels allows such companies to reduce its capital expenditure on wages by basing high-value work in low wage countries.

As individuals, we are faced with a cost / benefit analysis. Firstly, should one engage in tertiary education and forego the benefits of receiving a wage immediately? Secondly, if one does engage in tertiary education which field should one study? It has been suggested by many commentators that the tertiary education market in the developed countries is saturated and that the value of a tertiary qualification and the expected financial remuneration thereof, have diminished. It is true that there has been an increase in the supply of tertiary educated people in the workforce, however this increase was driven by a supply-side demand, namely that the ‘introduction of new technologies leads to a bias towards the more highly skilled workers. Therefore

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‘employers will be willing to pay more to workers who are skilled enough to operate these new technologies’ Machin and McNally (2007, p. 9). In turn less skilled workers will have greater difficulty in finding employment and also receive less financial remuneration for their work. In short, they are a less valuable commodity. Therefore Machin and McNally (2007, p. 5) conclude that concerns about the ‘over-supply’ and/or ‘over-qualification’ of tertiary graduates are misplaced. In a competitive and highly skilled labour market the individual has to evaluate the value of investing in tertiary education. Blundell et al, (1999, pp. 2-3) suggest that an individual will be willing to pay for the extra tuition and receive an initial reduced wage ‘if the costs are compensated by sufficiently higher future earnings.’ For this model to work higher qualified workers must demonstrate a measurable increase in productivity over their less well-trained counterparts.

Blundell et al, (1999, p. 4) report that in developed western economies the ‘gross rate of return for a year’s additional education ranges between 5 and 10 per cent’. Figure 1 below from Powell and Snellman (2004) shows the trends in the USA between 1975 and 1999. It is interesting to note that differences in earnings not only differ between the levels of education but also more importantly to this discussion the differential between each level of education shows a marked increase for those with the higher levels of education from around 1986 until 1999.

Figure 1. The Average Earnings of Full-Time, Year-Round Workers as a Proportion of the Average Earnings of High School Graduates by Educational Attainment: 1975 to 1999. Powell and Snellman (2004, p. 213).

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Despite the clear differences in remuneration for those with tertiary education a closer analysis shows that these differences are not equal to all with a tertiary level of education. In a study conducted on behalf of the OECD by Machin and McNally, (2007) it was reported that graduates failed to get sufficient remuneration for their tertiary education if they studied a subject that did not correspond to the needs of the labour market. In general terms they reported that:

Science/Engineering/Technology is often among the category of subjects with a relatively high return along with … law and medicine whereas Arts and Humanities is often among the category of subjects with a relatively low return. Machin and McNally, (2007, p. 4)

They conclude by stating that in the Arts and Humanities there may be an oversupply of labour and that in the UK no increase in remuneration exists for those having studied these subjects.

2.4 Global Skills Race

If indeed, the development of Human Capital is the main economic impetus of this century then it stands to reason that with such a finite resource an

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element of competition exists for this resource. This competition, or race, exists at several different levels. As long ago as 1920, when examining the future of humanity, HG Wells in his book the Outline of History wrote that ‘ Human history becomes more and more a race between education and catastrophe’. At this existential level of humanity Wells was referring to our ability to employ our Human Capital in effective and innovative ways to ensure that we continue to solve the big problems such as how to deal with a growing world population with finite resources, Weigman (2013, p. 136). At the inter-state level, more recently, Gordon Brown, who at the time was Prime Minister of Great Britain, is cited by Brown, et al, (2008, p. 4) announcing that ‘the UK had entered a global skills race… within this race education, knowledge and skills assume ever-greater importance.’ The point he was making was that each country was engaged in a race to develop its Human Capital in order to ‘outsmart other national economies, whether established or emerging’. It is clear that all countries should take their economic development seriously and Brown was only articulating what remains unspoken by other countries’ leaders. His reference to established and emerging countries is important as it demonstrates that as a country we are keen to improve our market share in industries already competed for by developed countries as well as ensuring that we are not overtaken by developing countries. The question of timeliness is important here. Has the UK reacted too slowly to the requirement to invest in tertiary education? Chani, Hassan and Shahid (2012, p. 4) in their overview of Human Capital development in Pakistan postulated that generating the idea of Human Capital can be achieved in a relatively short time scale but ‘for the development of Human Capital it takes 10 to 15 years.’ If the recognition of the issue occurred in the UK with Gordon Brown’s speech in 2008 then according to the timescales stated by Chani et al (2004, p. 4) if the UK’s government educational policy branch reacted immediately, it will take until between 2018 and 2023 for the result of this Human Capital development to be realized.

International competition for Human Capital is fierce. Whilst there has been a relative decline in Europe and the USA, Asia has been developing its Human Capital at an enormous rate. It has achieved this by two separate means. Firstly, sending students to western educational institutes then in time educating them indigenously. Brown, et al, (2008, p. 6) cite an alarming statistic related to the development of Human Capital in Asia, as ‘producing twice as many engineers as America and Europe together.’ With over half of the doctoral students in the ‘business relevant’ subjects such as engineering,

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mathematics and computer science in the US being foreign subjects.

This dramatic and relatively quick expansion in Human Capital has allowed Asia to attract investment into more advanced economic areas that are typically associated with the West. Brown et al, (2008, p. 4) cite a recent OECD acknowledgement that emerging economies such as China and India were ‘moving up the value chain to compete with Western companies for high-tech products and R&D investment.’ The traditional concept of research and development and design occurring in the West and allowing subsequent manufacture to occur in Asia may be under threat. The famous logo found on nearly all Apple products which states ‘Designed by Apple in California. Assembled in China’. May soon become an outdated notion.

The availability of tertiary educated Human Capital in developing countries has not gone unnoticed by the large transnational companies. Where the West fail to supply the demand of highly trained workers required by large transnational companies the companies themselves are turning to Asia to fill this demand requirement. A large unnamed German transnational company cited by Brown et al, (2008, p. 15) expressed concern over the supply of engineers and scientists from the US and Britain but explained that ‘the company did not experience a shortage because it was employing more Chinese and Russian graduates.’ They went on to state that it was not only a factor of the reduction in quantity of trained employees from the USA and Britain but also that ‘it would take Britain and the United States a long time to catch up with the quality of engineers and scientists being trained in Asia and the Russian Federation’, Brown et al, (2008, p. 15).

2.5 Developing Systems to Compliment Human Capital

Increasing tertiary educated Human Capital in line with the needs of the labour market is only part of the answer to increasing efficiency and economic output. The World Economic Situations and Prospects (WESP) Report (2014, pp. v – vi) published by the United Nations outlines some of the varying issues in employment faced by different countries around the world. Despite the European Union (EU) area general unemployment being at around 27% in the worst hit countries such as Greece and Spain, the critical issue in both the EU as well as developing nations is youth unemployment, which stands at around 50%. A considerable gender gap exists particularly in the developing countries. The WESP (2014, pp. v – vi) report highlights the issue that ‘further public

14

investment in skills training and upgrading will be necessary to integrate those groups that have been excluded.’ Different approaches are being attempted around the world to increase employment and boost national GDP. In some cases the provision of education is being aligned with macro-economic policies in order to promote an increase in productivity and innovation.

It has been shown that any tertiary education provides a benefit to the workforce this benefit comes in the form of a worker who is according to Blundell et al, (1999, p. 14) able to ‘adapt more rapidly and efficiently to new tasks and technologies’ as well as having ‘an increased ability to innovate on the job.’ Obviously a balance must be sought between not just educating as many people as possible to a tertiary level but also in ensuring that the correct balance of subjects is being delivered to try and minimize both over and under education. In a recent Chinese study by Zhang and Zhuang, (2011, p. 167) they noted that ‘Human Capital structure may have an inverse-U-shape effect on economic growth’. Meaning that too much Human Capital can be as deleterious as too little. This critical value of Human Capital is a constantly changing variable as it is shaped by the economic landscape of a specific country. The relationship between the economic landscape and the degree of tertiary educated people in the workforce can be seen in the UK as reported by Green and McIntosh, (2002, p. 25) They report that one third of those surveyed in 2001 were over-qualified and one fifth of those surveyed were under-qualified. Their work examined the reasons for this over and under qualification. It showed that there was a mismatch between a formal qualification and the skills required for a job. Therefore they could be construed as being ‘over-qualified in terms of formal qualifications, even though their skills or abilities are appropriate for the jobs that they do’, Green and McIntosh, (2002, foreword). They also found that a ‘skills mismatch’ existed whereby due to the structure of the labour market they were unable to find the most suitable jobs for their skills. As such it is suggested that the issue of too much tertiary educated Human Capital in the workforce could be a misnomer and closer correlation between skills required and qualifications held, along with better skill matching by employers and employees would see a large reduction in the figures cited by Green and McIntosh. Another factor supporting the notion that there are not too few tertiary educated people in the workforce is a simple supply / demand economical one. As postulated by Machin and McNally, (2007, p. 3), If there were an over-supply of tertiary educated graduates then, ‘relative wages and employment probabilities would fall to the level of their closest substitutes’. This is not occurring and it is not a

15

system that can be artificially put in place by policy makers. It must be left to market forces to dictate.

The final system that can help control the supply of tertiary educated capital in the labour market is that of the government being able to discretely target or attract the correct proportion of trained Human Capital to a particular region or economic sector. As is the case in China, where Zhang and Zhuang, (2011) note the policy implications for the Chinese government.

The more developed provinces benefit more from tertiary education, while underdeveloped ones depend more on primary and secondary education. As far as policy implications are concerned, this study suggests that China should raise the percentage of workers with tertiary educational attainment to promote economic growth. Moreover, in order to decrease regional disparities, it is better to invest more in all educational levels of the poor provinces. Zhang and Zhuang, (2011, p. 171)

Apart from aligning government macroeconomic policies with educational needs and achieving a more integrated system of skills and qualification matching between employers and employees, other more immediate and tangible systems can be employed to be able to better use Human Capital. Structural Capital innovations are being employed. New technologies, both software as well as hardware based and smarter work systems that can capitalize on this higher degree of Human Capital are being developed. Brown et al, (2008, p. 11) write of a ‘Digital Taylorism’ akin to the Fordist Mechanical Taylorism that so fundamentally changed industrial output in the last century. In principle Digital Taylorism aims to streamline the digital rather than mechanical working practices currently undertaken by our tertiary educated Human Capital and convert them into ‘codified packages’ that can be manipulated and utilized by any skilled worker regardless of location. The effect of this Digital Taylorism on the world of education may see a shift from specific and focused education and training in courses such as engineering to more general education that focuses on core skills required to operate the new systems brought into the workforce through Digital Taylorism. It may be that the company or country that adapts quickest to this ‘revolution’ in highly skilled working practices will gain a significant advantage over their competitors.

2.6 Summary16

The relevance to the individual and to society with regard to the economic value of tertiary educated Human Capital has been reviewed in this section. Some of the issues surrounding the development of tertiary educated Human Capital have also been expounded, such as the relationship between capital investment and the geographic displacement of the tertiary trained workforces and the feedback mechanism which sees tertiary educated workers becoming ever more valuable as a commodity within a limited geographical area. This section also highlights the global struggle to access this tertiary educated workforce and the efforts that some transnational companies as well as nations are making to develop and exploit this Human Capital. Despite these issues surrounding tertiary education it is still the case that tertiary educated workers are more productive and add greater value either in a role specific manner or in a more general manner through the imparting of knowledge to less well-educated workers n the work force. This issue coupled with the notion of Digital Taylorism indicates that tertiary educated workers may become exponentially more valuable as the 21st Century progresses. This dissertation aims through a comparative analysis of several large data sets to examine the relationship between tertiary educated workers and the level of a country’s economic development. The analysis is in three parts. The first part is a comparative snap shot of the 2012 data. The second part is a longitudinal correlational analysis from 2003-2012 and the third part is an abridged longitudinal correlational analysis using data from 2003, 2007 and 2012.

CHAPTER THREE Methodology

3.1 Introduction

This post-positivist research was principally reliant upon secondary quantitative data sets. Quantitative data analysis was chosen because there is a lack of primary data and individual subject input, coupled with the fact that there is no ‘real life’ experimental contextual setting.

Table 1. Countries Represented in this Thesis. UN WESP publication (2014, pp. 145-146).

Serial Country World Bank Classification1 Armenia Low Middle Income2 India Low Middle Income3 Indonesia Low Middle Income4 Moldova Low Middle Income5 Morocco Low Middle Income

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6 Philippines Low Middle Income7 Argentina Upper Middle Income8 Bulgaria Upper Middle Income9 Colombia Upper Middle Income10 Hungary Upper Middle Income11 Malaysia Upper Middle Income12 Mauritius Upper Middle Income13 Mexico Upper Middle Income14 Tunisia Upper Middle Income15 Turkey Upper Middle Income16 Australia High Income17 Austria High Income18 Belgium High Income19 Cyprus High Income20 Czech Republic High Income21 Denmark High Income22 Estonia High Income23 Finland High Income24 France High Income25 Hong Kong High Income26 Iceland High Income27 Ireland High Income28 Italy High Income29 Japan High Income30 Latvia High Income31 Lithuania High Income32 Malta High Income33 Netherlands High Income34 New Zealand High Income35 Norway High Income36 Poland High Income37 Portugal High Income38 Russia High Income39 Slovakia High Income40 Slovenia High Income41 Spain High Income42 Sweden High Income43 Switzerland High Income44 United Kingdom High Income

Table 1 above describes the countries represented in this thesis. For analysis the countries were further organized using the World Bank income categories of lower Middle Income, Upper Middle Income and High Income countries. A limitation of this study is that no countries from the Low Income category were used for analysis. This category was excluded from analysis due to lack of sufficient data proxies from the World Bank data series.

This was a preliminary study, therefore to aid validity, the author intentionally set out to include as many countries as possible for analysis. The countries were originally chosen from the list of Developing, Transitional and Developed countries listed in the UN WESP publication (2014, pp. 145-146). In total 44 countries were selected for inclusion in this study from a total of 160 countries which are listed in the 2014 WESP.

The data sets used for analysis were from the World Bank and the UN open resource databases. It is accepted that only being able to examine a limited number of countries may weaken the validity of this research. The principle-limiting factor for the non-inclusion of certain countries in this study was the

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non-availability of longitudinal data from the above-mentioned data sources. The data sources used can be defined into three distinct groups; the first is tertiary education data, the second is economic activity data and the third data set is related to development: The research design consisted of a cross-country survey of available data coupled with a comparative / correlational analysis between multiple data sets both at specific time points as well as longitudinally. This design was considered appropriate, as it was deemed to be the most accurate way to describe the data. This research did not consider the effect of extraneous variables such as education quality or cultural, political or religious factors that could definitely have an effect on how and why a country invests in tertiary education or why differences exist between countries’ level of economic development.

3.2 Description of Data Collection

The approach to data collection was chosen as this thesis aims to capture an ‘initial look’ at the relationship between tertiary education and development. It was felt that an initial broad analysis might provide sufficient evidence to warrant a more in depth study of the issue.

The purpose of this thesis is to begin to develop an idea about the level and scale of tertiary education in countries of differing levels of development and to examine the relationship and correlation if it exists, between a country’s level of development and its level of tertiary education. The assumption is that as a country becomes more developed it should see a decline in the proportion of GDP achieved through ‘relatively’ unskilled work such as agriculture and see an upshift in the contribution of the service sector to the accrual of its GDP. Service sector activities are defined by the UN as numbers 50 – 99 using the International Standard Industry Classification (ISIC) economic classification system. Those activities classified as Service activities are outlined in Table 2 below.

Table 2. Extract from UN International Standard Industrial Classification of all Economic Activities Classifications (2008, p. 43).Serial Classificatio

n NumberEconomic Activity

1 49-53 Transportation and Storage2 55-56 Accommodation and Food Service activities3 58-63 Information and Communication4 64-66 Financial and Insurance activities

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5 68 Real Estate activities6 69-75 Professional, Scientific and technical activities7 77-82 Administrative and Support Service activities8 84 Public Administration, Defence, compulsory social security9 85 Education10 86-88 Human Health and Social Work activities11 90-93 Arts, Entertainment and Recreation12 94-96 Other Service activities13 97-98 Activities of households s employers14 99 Activities of extraterritorial organisations and bodies

The reasons for the shift to Service sector activities in High Income economies are disputed. According to a review of this issue by Shetkatt and Yokarini (2003, p. 36) the shift involves a ‘complex mix of supply and demand factors’.

Amongst these factors two stand out. The first is structural and relates to the ‘modest rate of increase in outsourcing from manufacturing to services’ that is taking place. (Greenhalgh and Gregory, 2001, cited Shetkatt and Yokarini, 2003, p. 26). The second and probably more pertinent factor for this thesis is based on work by Fuchs, 1968, cited Shetkatt and Yokarini 2003, p. 9) estimating that ‘service sector productivity growth lags behind manufacturing productivity growth’. The principle reason for this is because skill upgrading or the knowledge required to obtain efficient work outputs has been less pronounced in these Service activities.

Figure 2 using World Bank data for the countries selected for this study clearly shows a marked decrease in the proportion of agriculture and an increase in the proportion of Services as they contribute to the country’s GDP for the High Income countries. Figure 2. Contribution by Sector (Agriculture, Industry, Manufacturing and Services to Value Added % of GDP. Data Source World Bank.

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Armenia

Indonesia

Moro

cco

Bulgaria

Hungary

Maurit

ius

Tunisia

Austria

Cyprus

Denmark

Finland

Hong kong

Ireland

Japan

Lithuania

Netherla

nds

Norway

Portugal

SlovakiaSpain

Switzerla

nd

0

20

40

60

80

100

120

140

160

180Agriculture

Industry

Services

Manufacturing

The complete list of data sets used in this thesis is listed below in Table 3. Tertiary education data and GDP/GNI data sets used were from the Online World Bank Data catalogue. The Development data sets came from both the World Bank and the UN WESP reports.

Table 3. List of Data Sources Used. World Bank.

Serial Title Description Source

1-Tertiary Education

Labour force with tertiary education (% of total)

Labour force with tertiary education is the proportion of labour force that has a tertiary education, as a percentage of the total labor force.

World Bank

2-Tertiary Education

Labour force with tertiary education, female (% of female labour force)

Labour force with tertiary education is the proportion of labour force that has a tertiary education, as a percentage of the total labour force.

World Bank

3-Tertiary Education

Labour force with tertiary education, male (% of male labour force)

Labour force with tertiary education is the proportion of labour force that has a tertiary education, as a percentage of the total labour force.

World Bank

4-Tertiary Educatio

Expenditure per student, tertiary (% of GDP per capita)

Public expenditure per pupil as a % of GDP per capita. Tertiary is the total public expenditure per student in tertiary education as a percentage of GDP per capita. Public

World Bank

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n expenditure (current and capital) includes government spending on educational institutions (both public and private), education administration as well as subsidies for private entities (students/households and other privates entities).

5-Tertiary Education

School enrolment, tertiary (% gross)

Gross enrolment ratio. Tertiary (ISCED 5 and 6). Total is the total enrolment in tertiary education (ISCED 5 and 6), regardless of age, expressed as a percentage of the total population of the five-year age group following on from secondary school leaving.

World Bank

6-Tertiary Education

Unemployment with tertiary education (% of total unemployment)

Unemployment by level of educational attainment shows the unemployed by level of educational attainment, as a percentage of the unemployed. The levels of educational attainment accord with the International Standard Classification of Education 1997 of the United Nations Educational, Cultural, and Scientific Organization (UNESCO).

World Bank

7-GDP Data

Value Added Data % of GDP

Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production.

Industry corresponds to ISIC divisions 10-45 and includes manufacturing (ISIC divisions 15-37). It comprises value added in mining, manufacturing (also reported as a separate subgroup), construction, electricity, water, and gas.

Manufacturing refers to industries belonging to ISIC divisions 15-37.

Services correspond to ISIC divisions 50-99 and they include value added in wholesale and retail trade (including hotels and restaurants), transport, and government, financial, professional, and personal services such as education, health care, and real estate services. Also included are imputed bank service charges, import duties, and any statistical discrepancies noted by national compilers as well as discrepancies arising from rescaling.

For each sector value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3.

World Bank

7-GNI (PPP) data

GNI per capita, Purchasing Power Parity (PPP), (current international $)

GNI per capita based on purchasing power parity (PPP). PPP GNI is gross national income (GNI) converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GNI as a U.S. dollar has in the United States. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in current international dollars based on the 2011 ICP round.

World Bank

8-Development Data

World Bank Analytical Classifications

Presented in World Development Indicators, GNI per capita in US$ (Atlas methodology. The Atlas methodology is used to reduce the impact of exchange rate fluctuations in the cross-country comparison of national incomes. The Atlas conversion factor for any year is the average of a country's exchange rate for that year and its exchange rates for the two preceding years, adjusted for the differences between the rate of inflation in the country and that in Japan, the United Kingdom, the United States, and the Euro area).

World Bank

The tertiary education data sets reflect not only participation in tertiary education, divided by gender but they also aid the description of the relationship between tertiary education / individual country investment in education and involvement of tertiary educated workers in the general workforce. The data is purely quantitative and does not reflect the quality or actual level of tertiary education that was undertaken, it also does not report which areas of the Economic Activity sections within the Service sector (see Table 2) the education was applied to.

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The GDP data sets were chosen because they best describe the proportion of a country’s economy that is generated by each sector; Agriculture, Industry, Manufacturing, and Services. Again, due to the International level of analysis this World Bank data lacks the fine detail that may be found at a national level, such as the proportion of GDP generated delineated further by employment category within each sector.

When describing a country or classifying it, according to its level of development according to the International Monetary Fund (IMF) ‘there is no criterion (either grounded in theory or based on an objective benchmark) that is generally accepted.’ Nielson, (2011, p. 3). Despite the UN not having a taxonomy for all of its member states, three recognized and widely used taxonomies exist. They belong to the UN, IMF and the World Bank. Each of these taxonomies has undergone several iterations in their lifetimes and each has its limitations.

The UN taxonomy relies on three elements; GNI Per capita (PPP), longevity and education. These three proxies are modulated and ‘do not enter directly into the sub-indices, but undergo a transformation’ Nielson, (2011, p. 8) prior to appearing in the database. The UN does not disclose why it uses relative thresholds rather than absolute ones and whilst ‘15 % of the world’s population live in designated developed countries’, according to Nielson, (2011, p. 9) they fail to explain the weighting mechanism for this categorization.

The IMF uses the categories of Advanced, Emerging and Developing countries. Its taxonomy has three different elements to that of the UN. It calculates its categories using GNI per capita (PPP), export diversification and the degree of integration into the global financial system. When making its final classifications the IMF uses either sums or weighted averages of these data for individual countries.

Table 4 below displays a comparison between the UN, IMF and World Bank taxonomies.

Table 4. Country Classification Systems in Selected InternationalOrganisations. Nielson, (2011, p. 19).

23

The World Bank taxonomy relies solely on econometric data. The World Bank considers GNI per capita to be ‘the best single indicator of economic capacity and progress.’ Nielson, (2011, p. 11). To improve compatibility between the three taxonomies the World Bank GNI per capita Atlas method is this same measure that is used in both the IMF and the UN’s own taxonomies. As with all three taxonomies, inclusion in each of the World Bank’s categories also uses arbitrary cut-off points. Its classification system was overhauled in 1989 the old industrial and capital surplus oil exporting categories were combined to form the high-income category whilst no reason was provided by the World Bank the cut-off level for inclusion in this group was ‘set at 12ó times the low-income threshold or about double that of average world income level.’ Nielson, (2011, p. 13). The threshold to distinguish between lower and upper middle-income categories was set at the income cut-off between softer and harder International Bank for reconstruction and Development (IBRD) borrowing terms. Nielson, (2011, p. 13). The level of GNI per grouping is changed annually and is linked to inflation. Table 5 below demonstrates the difference in these levels between 2003 and 2012.

Table 5. Difference in GNI per capita 2003 – 2012, Data source World Bank Analytical Classifications.

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Serial

Classification 2003 ($) 2012 ($)

1 Low Income <= 765 <= 1,0352 Low Middle-Income 766 – 3,035 1,036 – 4,0853 Upper Middle-Income 3,036 – 9,385 4,086 – 12,6154 High Income > 9,385 > 12,615

The World Bank analytical classifications have been used in this thesis because the World Bank GNI per capita data is used in all three classifications and is the simplest classification, as it does not include other proxies such as longevity.

3.3 Data Analysis Methodology

The analysis used in this thesis used data taken solely from the data sources outlined in Table 3. The first concern was to ensure that a full complement of data points was available for each of the participating countries. In order to ensure a balance between retaining a large enough population of countries to make the study valid and having access to 100% original data, areas in the data sets where no data had been recorded for a specific year were estimated using a Linear Regression function. Appendix A shows a list of all data sets clearly annotating in red font those data points were estimated using the Linear Regression function. It was felt that the same Linear Regression function should be applied to all data sets. It can be observed that in a small number of cases the linear regression function may exaggerate the actual value. These few cases can be clearly seen in Appendix A.

The countries were then grouped according to the World Bank analytical classifications into Low Middle Income, Upper Middle Income and High Income country groups. All tables and Figures produced for this thesis show the countries clustered according to these classifications.

Three different series of analysis were conducted. The first series of analysis aims to give a snapshot of the current position and involved examining the relationship between key tertiary educational data / Value added GDP data and the respective World Bank analytical classifications. This was conducted using the most recent data from 2012 only. Descriptions of each of these analyses can be found below.

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a. The difference in expenditure per student-tertiary (% GDP per capita) between Lower Middle-Income, Upper Middle-Income and High Income Countries (2012).

b. The difference in School Enrolment - tertiary (% Gross) between Lower Middle-Income, Upper Middle-Income and High Income Countries (2012).

c. The difference in the level of Labour Force with tertiary Education (% of total) between Lower Middle-Income, Upper Middle-Income and High Income Countries (2012).

d. The difference in the Level of Value Added % of GDP (Agriculture, Industry, Manufacturing, Services) between Lower Middle-Income, Upper Middle-Income and High Income Countries (2012).

The next stage of analysis involved a longitudinal approach using all data points from 2003 – 2012 and examined the relationship and the correlation between the following:

a. The difference in GNI per capita, PPP (current international $) between Lower Middle-Income, Upper Middle-Income and High Income Countries (2003-2012).

b. The correlation between GNI per capita, PPP (current international $) and school enrolment in tertiary education (2003-2012).

The final stage of analysis involved an abridged longitudinal approach. Analysing data from 2003, 2007 and 2012. These years were selected because they identify conditions at the start and end of the study period and also include a median data year that occurred prior to the financial crisis, which severely affected growth trends in most countries. This analysis focuses on the level of value added per % of GDP and its correlation with three tertiary education proxies.

a. The correlation between the level of Value Added % of GDP (Agriculture, Manufacturing, Services) and the expenditure per student-tertiary (% GDP per capita) (2003/2007/2012).

b. The correlation between the level of Value Added % of GDP (Agriculture, Manufacturing, Services) and the unemployment with tertiary education (2003/2007/2012).

26

c. The correlation between the level of Value Added % of GDP (Agriculture, Manufacturing, Services) and the level of labour force with tertiary education (% of total) (2003/2007/2012).

3.4 Ethics / Risk ManagementThe data sets used in this thesis were taken entirely from open source data from the World Bank and United Nations. No data from identifiable individuals was used. Consequently, the author delivered no questionnaires. As such, there was no requirement to request specific individual consent from any subjects or to have concern for the distress or the confidentiality of individual subjects. The templated University of Southampton Risk Assessment form and Ethics Review Check List were completed as part of this thesis proposal. At the time of data collection and analysis there was no requirement to complete an Ethics Sub-Committee Application form. Hence no completed form is attached to this thesis.

3.5 Summary

Reliability was a key issue and the choice of using World Bank and United Nations Data sets served to allow subsequent research to be assured of having access to the same quality of data. In terms of validity a compromise was sought. In order to include as many countries as possible in this thesis and to be able to examine longitudinal data, linear regression was used to piece together data sets, which were incomplete.

It was felt that the proxies chosen to represent the available tertiary education data were the correct ones and the choice of using the World Bank Country Development classification has been discussed. It was principally chosen because it is used as a measure in all three classification systems (International Monetary Fund, United Nations and World Bank) and limits itself to purely econometric data.

Finally, this is an exploratory study designed to ascertain whether a purposeful benefit exists in examining further this type of relationship between tertiary education and the economic functioning of a society. As such it is felt that the three types of analysis chosen, which examine the position today (2012 data), a longitudinal view and an abridged longitudinal view examining both the relationship and correlation between the data sets were appropriate.

CHAPTER FOUR Data Analysis

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4.1 Introduction

A complete list of all raw data tables used can be found at Appendix A. All data points used in this study that were established using linear regression have been highlighted in red.

This section reports the results of the analysis undertaken using both written interpretations and Figures and Tables. The results are displayed sequentially according to the three categories of analysis discussed in the data analysis methodology section above. Figures 1-8 list the country groupings beginning on the left with the Lower Middle income Group. The middle set is the Upper Middle Income group and the large group on the right of the figure is the High Income Group. The order of the countries varies throughout the tables as they have been ranked according to their scores in each table. The purpose of this dissertation is a Comparative Analysis of the Level of a State’s Economic Development with the Level of its Participation in Tertiary Education. As such all Standard Deviation, Mean average and Range information relates in all cases to each country group and not to individual countries.

4.2 Analysis of the Relationship between Key Tertiary Educational Data / Value Added GDP Data and the Respective World Bank Analytical Classifications.

As discussed in the Data Analysis Methodology section the analysis was conducted in three phases. The first phase included analysis of:

a. The difference in expenditure per student-tertiary (% GDP per capita) between Lower Middle-Income, Upper Middle-Income and High Income Countries (2012).

b. The difference in School Enrolment - tertiary (% Gross) between Lower Middle-Income, Upper Middle-Income and High Income Countries (2012).

c. The difference in the level of Labour Force with tertiary Education (% of total) between Lower Middle-Income, Upper Middle-Income and High Income Countries (2012).

d. The difference in the Level of Value Added % of GDP (Agriculture, Industry, Manufacturing, Services) between Lower Middle-Income, Upper Middle-Income and High Income Countries (2012).

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Figure 3. The Difference in Expenditure per Student-Tertiary (% GDP per capita) between Lower Middle-Income, Upper Middle-Income and High Income Countries (2012).

This proxy was used in the study to demonstrate the level of investment within each country for tertiary education as a percentage of the per capita figure. Its limitations are that it does not express the overall or absolute expenditure figures. Data for Turkey did not exist for the analysis in Figure 3. The Mean scores for Expenditure per Student-Tertiary (% GDP per Capita) for 2012 were as follows: Lower Middle Income 33.21%, Upper Middle Income 29.67% and High Income 31.72%. The data for Malta appears to skew the score for the High Income group. The variation in expenditure between the three groups was found to be minimal. This data demonstrates that there is no real difference between the amounts of money expended per Tertiary student as a percentage of GDP per capita in 2012.

A large range around the mean exists in all three groups. In the Lower Middle Income Group the range around the mean is 60.61 and the Standard Deviation is 24.94. This group shows the greatest range by some considerable margin of all three groups suggesting that a greater sample population is required for greater validity. In the Upper Middle Income Group range around the mean is 36.84 and the Standard Deviation is 14.96. In the High Income Group the range around the mean is 49.93 and the Standard Deviation is 11.09. The range around the mean for the High Income group would be considerably reduced if the data for Malta was excluded.

Lower Middle Upper Middle High

29

Figure 4. The Difference in School Enrolment - Tertiary (% Gross) between Lower Middle-Income, Upper Middle-Income and High Income Countries (2012).

Moro

cco

Phillipines

Moldova

Tunisia

Maurit

ius

Hungary

Turkey

Cyprus

Switzerla

nd

Hong kong UK

Czech Republic

Portugal

Belgium

Austria

Lithuania

Russia

Netherla

nds

New ZealandSpain

Australia

0

10

20

30

40

50

60

70

80

90

100

This proxy was selected for inclusion in the thesis as it demonstrates the inter-group differences in the amount of students that are actively enrolled in the process of tertiary education. The Mean scores for Figure 4, the Difference in School Enrolment - tertiary (% Gross) for 2012 were: Lower Middle Income 31.04%, Upper Middle Income 50.68% and High Income 69.93%. No obvious outlying data appears to be skewing this Mean score. This data shows a clear trend in ascending tertiary school enrolment between the Low Middle Income group at the bottom end through to the High Income group at the higher end. It is important to note that this data only represents a snap shot from 2012 and does portray any longer term trends.

A large range around the mean exists in all three groups. In the Lower Middle Income group the range around the mean is 30.34 and the Standard Deviation is 10.87. In the Upper Middle Income group range around the mean is 48.35 and the Standard Deviation is 16.99. In the High Income group the range around the mean is 52.51 and the Standard Deviation is 12.19.

Figure 5. The Difference in the Overall Level of Labour Force with Tertiary Education (% of total) between Lower Middle-Income, Upper Middle-Income and High Income Countries (2012).

30

0

10

20

30

40

50

60

70

80

Again only a snap shot from 2012, this proxy was selected for inclusion as it demonstrates how many of the tertiary educated members of a country are integrated into the workforce. The Mean scores for Figure 5, the Difference in the Overall Level of Labour Force with Tertiary Education (% of total) for 2012 were: Lower Middle Income 18.02%, Upper Middle Income 21.02% and High Income 33.92%. A considerable difference exists in the percentage of labour force with tertiary education between both the Lower / Upper Middle Income groups and the High Income group. With the High Income group having nearly twice as many tertiary educated people in the labour force.

In the Lower Middle Income group the range around the mean is 20.52 and the Standard Deviation is 9.42. In the Upper Middle Income group range around the mean is 13.22 and the Standard Deviation is 4.22. In the High Income group the range around the mean is 57.74 and the Standard Deviation is 11.70. The scores for Russia in particular and also for New Zealand to some extent skew the range and Standard Deviation for the High Income group. This skewing effect is a function of the linear regression.

Figure 6. The Difference in the Female Level of Labour Force with Tertiary

Education (% of total) between Lower Middle-Income, Upper Middle-Income

and High Income Countries (2012).31

Indonesia

India

Armenia

Mexico

Tunisia

Colombia

Hungary

Portugal

Austria

Malta

Poland

Iceland

Denmark

France

Switzerla

nd

Australia UK

Norway

Belgium

Ireland

New Zealand

0

10

20

30

40

50

60

70

80

90

100

This proxy was selected for inclusion as it was deemed important even at this initial exploratory level of analysis to look at potential intra-gender differences in the tertiary educated workforce. No data for Tunisia existed for the analysis in Figure 6. The Mean scores for the Difference in the Female Level of Labour Force with Tertiary Education (% of total) for 2012 were: Lower Middle Income 19.91%, Upper Middle Income 26.56% and High Income 38.70%.

A large range around the mean exists in all three groups. In the Lower Middle Income group the range around the mean is 29.19 and the Standard Deviation is 11.40. In the Upper Middle Income group range around the mean is 17.81 and the Standard Deviation is 5.97. In the High Income group the range around the mean is 69.18 and the Standard Deviation is 13.63. The score for Russia (achieved using linear regression) skews the High Income group data and a lower range and Standard Deviation would be recorded if this score were ignored. The large range and high Standard Deviation score indicate that a larger population size would add greater validity to these results.

Figure 7. The Difference in the Male level of Labour Force with Tertiary Education (% of total) between Lower Middle-Income, Upper Middle-Income and High Income Countries (2012).

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Indonesia

India

Armenia

Mexico

Tunisia

Colombia

Hungary

Portugal

Austria

Malta

Poland

Iceland

Denmark

France

Switzerla

nd

Australia UK

Norway

Belgium

Ireland

New Zealand

0

10

20

30

40

50

60

70Lower Middle HighUpper Middle

Again, as with Figure 6 no data for Tunisia existed for this analysis in Figure 7. The Mean scores for the Difference in the Male Level of Labour Force with Tertiary Education (% of total) for 2012 were: Lower Middle Income 15.85%, Upper Middle Income 17.08% and High Income 29.71%. As with the female scores this result shows the High Income group having almost twice the level of tertiary educated males as the Lower Middle income group.

A large range around the mean exists in all three groups. In the Lower Middle Income group the range around the mean is 17.72 and the Standard Deviation is 7.42. In the Upper Middle Income group range around the mean is 8.84 and the Standard Deviation is 3.48. In the High Income group the range around the mean is 49.14 and the Standard Deviation is 10.53.

No data on the absolute numbers of males and females with tertiary education was collected. However when viewed as in Figures 6 and 7 as a % of the total, the mean scores for females with tertiary education in the workforce are consistently higher across all three groups than their male counterparts. By group the percentage differences are: 25.6% more females in the Lower Middle Income group, 55.50% in the Upper Middle Income group and 30.26% in the High Income group.

Figure 8. The Difference in the Level of Value Added % of GDP (Agriculture, Industry, Manufacturing, Services) between Lower

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Middle-Income, Upper Middle-Income and High Income Countries (2012).

Armenia

Indonesia

Moro

cco

Bulgaria

Hungary

Maurit

ius

Tunisia

Austria

Cyprus

Denmark

Finland

Hong kong

Ireland

Japan

Lithuania

Netherla

nds

Norway

Portugal

SlovakiaSpain

Switzerla

nd

0

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60

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120

140

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180Agriculture

Industry

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Manufacturing

This proxy was selected for study as it describes the move away from the Agricultural sector and a development or growth in the Service sector as theorized in the Three-sector theory (Fisher, 1935 and Clark, 1940, cited in Schettkat and Yocarini, 2003, p. 7). It relates purely to economic GDP data and its composition is not reflective of any contribution by tertiary educated workers. The Mean scores for the Difference in the Level of Value Added % of GDP for 2012 are reported in Table 6.

Table 6. Mean Average Scores in the Level of Value Added % of GDP (Agriculture, Industry, Manufacturing, Services) between Lower

Middle-Income, Upper Middle-Income and High Income Countries (2012).

Serial Agriculture Industry Manufacturing

Services

Low Middle 15.78 30.59 16.54 53.63Upper Middle

6.35 31.98 18.03 61.67

High 3.17 26.12 15.78 70.47

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4.3 Longitudinal Relationship and Correlation Analysis.

It is this series of analyses, which begin to show trends in activity and not just a snapshot of the current situation. Due to the amount of countries represented in the High Income category it was deemed useful to improve the clarity of the results to separate the Income categories into three separate Figures. This second phase included analysis of:

a. The difference in GNI per capita, PPP (current international $) between Lower Middle-Income, Upper Middle-Income and High Income Countries (2003-2012).

b. The correlation between GNI per capita, PPP (current international $) and school enrolment in tertiary education (2003-2012).

Figures 9 a, b, c. The Difference in GNI per Capita, PPP (current international $) between Lower Middle-Income, Upper Middle-Income and High Income Countries (2003-2012).

9a – High Income

2003 2004 2005 2006 2007 2008 2009 2010 2011 20120

10000

20000

30000

40000

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Australia Austria

Belgium Cyprus

Czech Republic Denmark

Estonia Finland

France Hong kong

Iceland Ireland

italy Japan

Latvia Lithuania

Malta Netherlands

New Zealand Norway

Poland Portugal

Russia Slovakia

Slovenia Spain

Sweden Switzerland

UK

9b – Upper Middle Income35

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

0

5000

10000

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Argentina

Bulgaria

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9c – Lower Middle Income

2003 2004 2005 2006 2007 2008 2009 2010 2011 20120

1000

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9000

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Armenia

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Phillipines

A clear distinction in the overall amount of GNI per capita exists between the three groups. The Low Middle Income group as seen in Figure 9c has a starting range of between $2,000 - $5,000 The Upper Middle Income group as seen in Figure 9b has a starting range of approximately $6,000 - $15,000 and the High Income group as identified in Figure 9a has a starting range of between $10,000 – 40,000. In contrast the mean average growth in GNI per capita PPP over the 10-year period of data sampled shows that the Low Middle Income group has a mean growth of 80%, with the Upper Middle Income group growing slower with a mean of 65.1% and the High Income group growing slowest of all at nearly half the rate of the Low Middle Income group with a mean score of 43.96%. No data for Argentina existed for this calculation.

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This longitudinal view of the data highlights quite clearly the effects made by the recent global financial crisis on growth in GNI per capita. Occurring around 2008-2009 this retardation in growth is most pronounced in the High Income group and diminishes through the Upper Middle Income to Lower Middle Income groups. A possible reason for this difference in growth rates could be as illustrated in Figure 8 that the High Income group relies less on agriculture and places a greater emphasis on the Service Sector, which is more reliant on tertiary educated workers.

Figure 10. The Correlation between GNI per Capita, PPP (current international $) and School Enrolment in Tertiary Education (2003-2012).

-1

-0.5

0

0.5

1

1.5

Lower Middle Upper Middle High

This chart at Figure 10 shows the majority of countries have a strong positive correlation between GNI per capita and school enrolment in tertiary education. A score of 1 indicates the strongest positive correlation and a score of -1 indicates the strongest negative correlation. This positive correlation occurs across all three development categories. Outliers do exist and again a larger sample population would help to support the trend indicated in Figure 10. Although not visible in the correlation above, the data also indicated differences in the rate of growth of enrolment in tertiary education between the three groups. The mean average growth in School Enrolment in Tertiary Education over the 10-year period of data sampled is 36.13% for the Low Middle Income group, 48.05% for the Upper Middle Income group and 20.52% for the High Income group. The Low Middle Income group moved from an overall average of 22.75 – 30.97%, the High Middle Income group moved from 34.23 – 50.68% and the High Income group moved from 58.15 – 69.93%.

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4.4 Abridged Longitudinal Approach. Selected Data 2003, 2007, 2012.

This third and final phase of analysis looked at correlations and included the analysis of:

a. The correlation between the level of Value Added % of GDP (Agriculture, Manufacturing, Services) and the expenditure per student-tertiary (% GDP per capita) (2003/2007/2012).

b. The correlation between the level of Value Added % of GDP (Agriculture, Manufacturing, Services) and the unemployment with tertiary education (2003/2007/2012).

c. The correlation between the level of Value Added % of GDP (Agriculture, Manufacturing, Services) and the level of labour force with tertiary education (% of total) (2003/2007/2012).

Figure 11. The Correlation between the Level of Value Added % GDP and the Expenditure per Student-Tertiary (% GDP per capita), the Unemployment with Tertiary Education and the Level of Labour Force with Tertiary Education (% of total) (2003/2007/2012).

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The results presented in Figure 11 are three separate correlations with one common denominator. Three tertiary education proxies have been individually correlated with the Value Added percentage of GDP, which is separated into three sectors; Agriculture, manufacturing and Services. In Figure 11 a deep red colour relates to a strong positive correlation. A deep blue colour is associated with a strong negative correlation. The colour yellow represents no correlation.

As expected there is a greater positive correlation between the amounts of unemployed tertiary educated personnel and the value added to the Service sector. No obvious difference exists between the Lower middle, Upper middle and High Income groups. Generally, all three groups demonstrate a high degree of negative correlation in both the Agriculture and Manufacturing sectors. The exception is the Lower Middle income group, which is less negatively correlated in the Agriculture sector. Potentially analysing a larger sample population would confirm this negative correlation and add greater reliability.

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All three groups demonstrate a strong positive correlation between the level of the labour force with tertiary education and the value added by the Service sector.

4.5 Summary

All analyses conducted in this thesis have been presented in this section. The large Range and high Standard Deviation reported in all of the analysis indicate that a larger population sample would be useful in validating the trends and correlations observed in this study.

Pronounced differences in the level of GNI per capita PPP and value added percentage of GDP existed between the three development categories (Figures 8, 9 a, b, c and Table 6). When these two proxies were correlated with the tertiary educational proxies (Figures 10 and 11) it was found that where positive correlations existed they existed across the Development categories and no intra - development category differences were noted.

CHAPTER FIVE Discussion

5.1 IntroductionThis thesis is exploratory and can only offer a broad comparative analysis. Two particular issues were examined, firstly whether tertiary education is related to a country’s move upwards from being a primary economy to becoming a tertiary economy. Secondly, to see if the data shows any correlation between tertiary education and GDP / GNI growth. These issues will be discussed in this section as well as the specific research questions to be examined as a result of this thesis that were outlined in the research aims section.

a. Is there a correlation between a country’s level of participation in tertiary education and the ratio of primary, secondary and tertiary industries composing the State’s economy?b. How does participation in tertiary education differ between males and females amongst the different countries?

5.2 General discussion of results

1. Is tertiary education related to country’s move upwards from being a

primary economy to becoming a tertiary economy?

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The first research aim was to see if tertiary education is related to country’s move

upwards from being a primary economy to becoming a tertiary economy. (Fisher,

1935 and Clark, 1940, cited in Schettkat and Yocarini, 2003, p. 7) predicted that

as an economy moved from primary through to tertiary then it would see a greater reliance on the Service sector. Figure 8 which graphically describes the difference in the level of value added % of GDP by sector showed no real pattern of increase or decrease for the Industry or manufacturing sectors however it showed a marked difference in line with Fisher and Clark’s prediction for the Agriculture and Service sectors. The percentage of value added GDP was lowest in the High Income group with a mean score of 3.17% and highest in the Lower Middle income group with a mean of 15.78%. Likewise the contribution of the Service sector was highest in the High Income group with a mean score of 70.47% and lowest in the Lower middle income group with a mean of 53.63%.

Despite the focus of their work being internal to China, Zhang and Zhuang, (2001, p. 167) took the view that ‘primary and secondary education skills are related to growth in developing countries’ whilst tertiary education skills are of most importance for ‘economic growth in the developed countries’. The results from this thesis demonstrated in Figure 4, School Enrolment in Tertiary Education, that a clear relationship between the amounts of students enrolled in tertiary education and the development level of the country. The High Income countries had a mean average of 69.93% and the Lower Middle income countries had a mean score of 31.04%. The Upper Middle Income group sat between both Lower and High Income groups. Figure 5 which portrays the amount of the overall level of Labour Force with tertiary Education also showed a similar trend with the High Income countries having an average of 33.92% of the workforce tertiary educated whilst the Lower Middle income group had only 18.02%. The Upper Middle Income group sat between both Lower and High Income groups.

2. Does a correlation exist between tertiary education and GDP / GNI growth?

The first piece of evidence from this study to support the notion that a positive correlation exists is, that even accepting that the Industrial sector will attract a

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degree of tertiary educated workers it is predominantly the Service sector that should attract the most tertiary educated Human Capital. Table 2 describes the types of employment included in the Service sector. Figure 8, which describes the contribution to % GDP by sector, shows quite clearly that the High Income countries are more reliant on the Service sector.

The chart at Figure 10 using data from 2003 - 2012 which shows the majority of countries have a strong positive correlation between GNI per capita and school enrolment in tertiary education does offer some insight into the notion that a correlation exists between tertiary education and GDP / GNI growth. Although all three income groups demonstrated strong positive correlations between school enrolment and GNI per capita it is interesting to note that the rate of growth in enrolment in tertiary education is lowest in the High Income group and highest in the Upper Middle Income group. This slow growth rate in the High Income countries may be reflected by Brown et al, (2008, p. 15) when they observe that a noticeable reduction in quantity and quality of engineers and scientists exists from the USA and Britain and it may take time to catch up with Asia and Russia.

Figure 11, which is an abridged longitudinal analysis using data from 2003, 2008 and 2012, contains three separate series of data analysis. The first, Expenditure per Student Tertiary, shows no obvious positive or negative correlation by development group or sector. The second and third series; Unemployment with Tertiary Education and the Level of Labour Force with Tertiary Education show a clear positive correlation with the level of value added GDP in the Service sector. This analysis supports the notion that a high degree of tertiary educated people work, or are unemployed in the Service sector in all three income categories. Given that the Service sector is by far the biggest contributor to the percentage of value added GDP across all three income groups (Lower Middle Income 53.63%, Upper Middle Income 61.67% and High Income 70.47%) it is suggested that a correlation may exist between tertiary education and GDP / GNI growth.

This next section of the discussion examines two questions and discusses whether there is enough evidence from this study to suggest further study.

1. Specific Questions relevant for further study: Is there a correlation between a country’s level of participation in tertiary education and

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the ratio of primary, secondary and tertiary industries composing the State’s economy?

The data from this thesis (Figure 5) demonstrates that the High Income countries have the highest percentage of workers with tertiary education. The data from Figure 8 also shows that the High Income countries also extract the most percentage value added GDP from their Service sector. In agreement with suggestions in the literature, Figure 11 provides no evidence of a positive correlation by income category for the Agriculture or Manufacturing sectors. Evidence of a positive correlation between tertiary educated workers and the Service sector across all three income categories however is shown. Considering the rapid GNI per capita growth rate in the Lower Middle and Upper Middle Income categories and with the emphasis firmly on the Service sector further study could be undertaken to examine the relationship between the growth of the Service sector specifically in the Lower and Upper Middle Income categories and the growth in the tertiary educated workforce.

2. Specific Questions relevant for further study: How does participation in tertiary education differ between males and females amongst the different countries?

Evidence from the WESP (2014, pp. v – vi) stated that a gender gap exists particularly in developing countries. This thesis did not investigate the absolute numbers of males and females within the workforce. Instead it examined the percentage of each workforce with tertiary education. The data showed that female employment with tertiary education as a percentage of the overall female labour force was considerably greater than its male equivalent. The mean values were 25.6% more females in the Lower Middle Income group, 55.50% in the Upper Middle Income group and 30.26% in the High Income group. Further study in this area could be undertaken to examine the growth or decline in the overall numbers of females in the total labour force with tertiary education in comparison to their male equivalents.

5.3 Limitations of the study

This thesis has not found any causal evidence for the link between tertiary education and the development of a country’s economy. Also, it has not attempted to examine the quality of tertiary education either. Hanushek and Woessmann, (2007, p. P2) found strong evidence of causal relationships that

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‘Educational quality, measured by cognitive skills, has a strong impact on individual earnings’. In addition, educational quality also has a ‘strong and robust influence on economic growth.’ Their Cross-country analysis, although quite general in its nature, did highlight this qualitative problem being particularly of concern for the developing countries.

It must be remembered that many different factors affect the development of a country, such as good governance and the ability to effectively apply the tertiary knowledge to the workplace. The latter factor requires effective use of the other two elements of Intellectual Capital; Organisational and Social Capital. Financial investment is also needed and even with sufficient investment it may not be clear whether due to the potential for a bi-directional relationship to exist, Chani, Hassan and Shahid (2012, p. 12) between Human Capital and economic development it is the quantity of developed Human Capital that is driving development or if it is the economic investment that is attracting the Human Capital.

A lack of complete longitudinal data sets existed to be able to include a greater number of subject countries in this study, particularly the Low Income countries. Including more countries would have meant reducing the data sets available for use as proxies. Being able to Include more subject countries would be important in understanding how valid these results were. In addition, increasing the range of the longitudinal study may contribute to the reliability of the results in proving an effective correlation between growth in GNI per capita and tertiary education enrolment, and Service sector growth and a tertiary educated workforce.

5.4 Implications for policy and practice

The data analysed in this thesis showed that the more developed countries as a group displayed a greater reliance on the Service sector and a decrease in their reliance on the Agricultural sector. It did not confirm the mechanism by which this occurs, only that the data for 2012 appears to support the three-sector theory’s idea that as a country develops it will place greater economic emphasis on its Service sector. The implications for UK policy are threefold: Firstly, that active measures should be taken to ensure appropriate quality and

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quantity of tertiary educated students with the skills to work in this sector, secondly investment in the idea of Digital Taylorism should be encouraged. It is acknowledged that Organisational Capital can be a positive force for developing and capturing Human Capital, however as noted by Kumar (2013 p. 61) in reference to workers in the Information Technology Service sector, it can also be a restraining force which can ‘also limit its ability to retain creative talent, build human capital and move up the knowledge pyramid. It puts limits on innovative capabilities.’ The development of Digital Taylorism / Organisational Capital will be the key enabler in the effective long-term employment of tertiary educated Human Capital. The result could be a tertiary educated workforce with the knowledge and tools to adapt to an ever-changing working environment. Most important of all is to be able to put this Human Capital to work efficiently. Financial investment is required to attract the Human Capital and to grow this talent base. It must be accepted that an element of competition will exist between the individual and the provider of the tertiary education, as the individual seeks the best return for their educational investment. It is incumbent on tertiary education providers and companies to work to find solutions to limit this ‘brain drain’ and thereby, as postulated by Fuente (2011, p. 3) prevent a ‘wedge’ being driven ‘between the private and social returns to education’.

5.5 Implications for theory

This study on its own should not be used as evidence for suggestions to changes in educational theory. Issues which it highlights, but would need further evidence to support are; that if the Service sector is set to continue to grow as a proportion of our Value Added GDP and if indeed the contribution of tertiary educated Human Capital is positively correlated with this Sector then work will have to be undertaken to analyse how the UK can maintain its place or slow down the decline in its ability to supply ‘home grown’ tertiary educated students into the workforce. Although, as discussed in the introduction to this thesis, gaining an economic advantage for the country is not and should not be the sole consideration for an individual considering tertiary education, it certainly remains an important one for the country. An academic discussion needs to consider the structure behind how we deliver tertiary education. Should, using the construction of Digital Taylorism, platforms both hardware and software based be developed that would enhance the flexibility of employment for tertiary educated students who would be able to apply a

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common set of core skills across a wider variety of employment categories within the Service Sector?

5.6 Recommendations for future research

Recommendation 1. This research viewed Human Capital development at a country level and not at an individual level. The role of multinationals in the 21st Century is muddying the water. The increase in free movement of Human Capital across borders allows Human Capital educated in one country to improve the development and economic growth of another country. It is recommended that further research be conducted at an individual level to examine the relationship between the country of education and the country in which the economic benefit of that tertiary education is applied.

Recommendation 2. The longitudinal view of the difference in GNI per capita, shown in Figures 9 a, b and c highlights quite clearly the effects made by the recent global financial crisis on growth in GNI per capita. The retarded growth which was most pronounced in the High Income group may be related to the reduced Agricultural sector and over-reliance on the Service Sector. It is recommended that further research be conducted at a country level to examine the years before and after the 2008 financial crisis to determine if there is a strong correlation between a highly developed Service sector supported by tertiary educated workers and a degree of economic fragility. This work should consider the quality as well as quantity of tertiary educated workers.

Recommendation 3. Brown et al, (2008, p. 15) noted the recent reduction in quantity and quality of developed nations’ scientists and engineers. In view of the statement by Chani, Hassan and Shahid (2012, p. 4) that it takes 10-15 years to develop Human Capital, it is recommended that further research be conducted to examine the future correlation between GDP/GNI growth and enrolment in tertiary education from 2012 to 2020. This analysis should be conducted on an annual basis.

CHAPTER SIX Conclusions / Policy Recommendations

6.1 Conclusions

This preliminary data analysis suggests a positive correlation between GNI per capita and school enrolment in tertiary education. Figure 10 demonstrates this positive correlation across all three income groups. Figure 11 demonstrates

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the positive correlation between Service sector growth and the level of labour force with tertiary education as well as the level of unemployed with tertiary education within the Service sector. The increased reliance on the Service sector for GNI growth across all three income groups as evidenced in Figure 8 indicates that a continued growth in enrolment in tertiary education is likely to occur. It also suggests that due to the heavy reliance on the Service sector for growth in GNI as reported in Figures 9 a, b and c, if the growth in GNI is affected as it was after the 2008 financial crash then the effect on unemployment is likely to be concentrated in the Service sector affecting those with tertiary education the most, with the High Income countries having the most to lose. This systemic frailty may be partially obviated through measures to improve the quality of the tertiary education and its employment within the sphere of the intellectual Capital of individual companies. Hanushek and Woessmann, (2007, p. 26) report that between 1960-1990 a ‘statistically and economically significant positive effect of the quality of education on economic growth … that dwarfs the association between quantity of education and growth’ can be seen. Companies which have a workforce with a higher quality of education and which have well developed social and organisational capital will be more able to weather future economic crises.

6.2 Policy Recommendation 1- Reducing Economic Fragility.

This thesis may only serve as a preliminary awareness of the relationship between tertiary education levels and economic growth within a country. The analysis conducted herein suggests an increasing reliance on tertiary educated workers as the role of the Service sector in growing the GNI of a country increases. This may lead to an economic fragility as the economic base for growth becomes unbalanced and less robust. It is recommended that governments investigate the potential offered by ‘Digital Taylorism’ Brown et al, (2008, p. 11). Allowing a shift from a narrowly focused tertiary education to a broader more general focus on core skills and principles may allow tertiary educated workers to adapt more quickly and be able to operate new systems brought into the workforce through Digital Taylorism. The ability to operate these systems will allow greater flexibility to be able to respond to sudden changes in Service sector employment and may ultimately give an economic advantage to the country that adopts these practices first.

6.3 Policy Recommendation 2 – Monitoring the Tertiary output.

The competition for GNI growth is showing signs of weakness particularly amongst the High Income countries. In comparison with the Lower Middle

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Income group the High Income group is growing at almost half the rate, albeit from a much higher overall level. This factor coupled with the relative dearth of high quality tertiary educated workers being produced by the Western High Income countries contrasts starkly with the increase in high quality tertiary educated workers being produced in Russia and Asia. When one considers the timescales required to develop effective Human Capital In order to maintain a competitive advantage at a national level it is recommended that the type, quantity and quality of tertiary educated workers should be monitored by governments and efforts made to stimulate the growth of Human Capital in the more productive tertiary education sectors.

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World Bank. (2015) Labour Force with Tertiary Education (% of total). Available at: http://data.worldbank.org/indicator/SL.TLF.TERT.ZS (Accessed 08 August 2015).

World Bank. (2015) Labour Force with Tertiary Education, Female (% of Female Labour Force). Available at: http://data.worldbank.org/indicator/SL.TLF.TERT.FE.ZS (Accessed 08 August 2015).

World Bank. (2015) Labour Force with Tertiary Education, Male (% of Male Labour Force). Available at: http://data.worldbank.org/indicator/SL.TLF.TERT.MA.ZS. (Accessed 08 August 2015).

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World Bank. (2015) Unemployment with Tertiary Education (% of total Unemployment). Available at: http://data.worldbank.org/indicator/SL.UEM.TERT.ZS (Accessed 08 August 2015).World Bank. (2015) GNI per capita, Purchasing Power Parity (PPP), (Current International $). http://data.worldbank.org/indicator/NY.GNP.PCAP.PP.CD (Accessed 08 August 2015).

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Appendix A: Consolidated Data Tables Labour Force with Tertiary Education (% of total) 2003-2012

Labour Force with Tertiary Education Female (% of Female Labour Force) 2003-2012

1

Labour Force with Tertiary Education Male (% of Male Labour Force) 2003-2012

2

Expenditure per Student, Tertiary (% of GDP per Capita) 2003-2012

3

School Enrolment Tertiary (% of Gross) 2003-2012

4

Unemployment with Tertiary Education (% of total unemployment) 2003-2012

5

Value Added Data (% of GDP) 2003-2012Agriculture, value added (% of GDP)

6

Industry, value added (% of GDP)

7

Manufacturing, value added (% of GDP)

8

Services, etc., value added (% of GDP)

9

GNI per Capita, Purchasing Power Parity (PPP), (current international $) 2003-2012

10

World Bank Analytical Classifications 2003-2012

11

12