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The Workforce of the FutureAn assessment of technology proficiency

© The Economist Intelligence Unit Limited 20151

The Workforce of the Future: An assessment of technology proficiency

Preface 2

Acknowledgements 3

Introduction 4

Index findings 6

Key findings 6

Overall Workforce of the Future rankings table 8

Technology and connectivity infrastructure 9

Technology and society 12

Labour market 15

Education and technology skills 18

Government environment 20

Business environment 23

EIU preferred weighting scheme 26

Conclusion 28

Appendix 29

Contents



The Workforce of the Future Index is an Economist Intelligence Unit publication, sponsored by AT&T. This report discusses the key findings and methodology of the index and the accompanying global benchmarking model.

We would like to thank the many researchers who lent their expertise to this project. A full list of acknowledgements follows.

The findings, interpretation and conclusions expressed herein are those of The Economist Intelligence Unit and do not necessarily reflect the views of the sponsor. While every effort has been made to verify the accuracy of this information, neither The Economist Intelligence Unit Ltd nor the sponsor of this report can accept any responsibility

or liability for reliance by any person on this report or any other information, opinions or conclusions set out herein. The boundaries, colours, denominations and other information shown on any map in this work or related materials do not imply any judgment on the part of the sponsor concerning the legal status of any territory or the endorsement or acceptance of such boundaries.

For further information, please contact:

David HumphreysProject director: [email protected]

Brad HoffmannProject manager: [email protected]

Preface



The following economists, researchers, consultants, business executives and country analysts contributed to the report. We thank them for their participation.

Economist Intelligence Unit specialists, researchers and contributors: Alice Nawfal, Myya McGregory, Nthabi Choma, Sara Constantino, Matt Zook and Katherine Stewart..

Model and report production: Marcus Krackowizer and Mike Kenny

Peer panel members The following human capital, technology and labour market experts contributed significantly to shaping the index methodology and vetting the indicators. Their diverse backgrounds and extensive experience ensured that a wide variety of views were considered. The panel met as a group in April 2015 in New York City to review an initial indicator list and provided ongoing support, as needed, throughout the project. They also advised on the selection of weights for the index.

Matt Barney, founder and CEO, LeaderAmp

James Bessen, lecturer, School of Law, Boston University

John Boudreau, professor of management and organisation, Marshall School of Business, University of Southern California

Rick Guzzo, consultant, Mercer

Samia Melhem, lead policy officer at the Transport and ICT Global Practice, World Bank

Haig Nalbantian, senior partner, Mercer

Rahel Schellenberg, global head of HR information services, Marsh & McLennan

Prasanna Tambe, associate professor of information, operations and management sciences, Stern School of Business, New York University

Acknowledgements



There is growing pressure on companies, governments and other employers to identify, attract and retain human talent capable of leveraging technology effectively and efficiently. The difficulties in engaging such workers stem from numerous factors, including globalisation, changing demographics and emerging technologies such as artificial intelligence and the Internet of Things. The challenges facing organisations from the technology and human resources perspective vary across countries and regions; however, most countries, regardless of their stages of development, are becoming more digitised. As countries increase their connectivity infrastructures, and as technology pervades society and production continues to be automatised, the workplace also becomes more technological. This change requires a new type of employee, a technologically savvy employee, one of the “Workforce of the Future”.

A brief examination of the challenges firms face reveals that the human capital strategy will become more complex. Globalisation has increased international competition among countries and firms. This competition emphasises the importance of developing a technologically capable labour force to attract business and foreign investment. Globally, demographic trends are diverging. In the United States, for example, two changes are altering the workplace environment: the ageing population and the rise of the millennial generation. The US Census Bureau estimates that

there are over 80m millennials—the generation born in the 1980s and 1990s that grew up with technology—in the United States. Creating a workplace that meets the shifting expectations of millennials while also compensating for the loss of the practical experience of the baby boomers is a growing challenge for firms. Meanwhile, in Europe, the percentage of the population aged 60 years or above is projected to grow from 24% in 2015 to 34% in 2050, while in Africa that group is only expected grow from 5% to 9%. Stagnant European economies with high youth unemployment will struggle. Growing youth populations in African countries, such as Nigeria, will be an asset, but one that requires cultivation.

The continued adoption of emerging technologies into the workforce further complicates the challenge of finding the right talent. In 2014, 39% of chief information officers surveyed by KPMG, a consultancy, felt that there was going to be a significant change in skills needed in the next 3-5 years compared with today.

The Workforce of the Future Index addresses the global human resource challenges facing business leaders. The index measures the technological aptitude of national workforces across 56 countries, which collectively represent approximately 85% of the global population and more than 90% of global GDP. The Workforce of the Future Index differs from other technology and innovation indices through its focus on the labour force’s preparedness to work in technology-rich

Introduction



environments. It evaluates the prevalence of information and communications technology (ICT) and the social and economic aspects of an increasingly technical world both as components of the supporting environment and as drivers of their technological proficiency.

This assessment of future technological readiness finds that rich-world countries lead the way. The United States ranks first, scoring significantly better than the other high-income OECD countries that make up the top performers: the United Kingdom, Denmark and the Netherlands. The United States scores well across all six categories in the index, while the Western European countries are hindered by low scores in the labour market category owing to unfavourable demographic trends. The poorest performers are Pakistan, Algeria and Nigeria. These countries see particular challenges in the quality of technology education, although growing youth populations bolster their scores in the labour market category.

This report outlines key trends and success drivers across each category and highlights areas for potential improvement. The index is comprised of six categories: Technology and connectivity infrastructure; Technology and society; Labour market; Education and technology skills; Government environment; and Business environment. Chart 1 lays out the index framework:

In developing this index, a team of analysts from The Economist Intelligence Unit (EIU) evaluated 32 separate criteria (indicators), both qualitative and quantitative, for each country. These indicators were scored relative to the other countries and were derived from primary and secondary research. All indicators were chosen for their significance, data coverage and comparability. The categories, and their component indicators, are weighted according to our assumptions of their relative importance in fostering a workforce’s technological aptitude. Details on the methodology can be found in the Appendix.

Workforceof thefuture

Technologyand

connectivityinfrastructure

Technologyand

society

Governmentenvironment

Educationand

technologyskills

Businessenvironment

Labourmarket

Chart 1:The Workforce of the Future index framework



Key findingsl Overall scores in the Workforce of the Future

Index indicate that OECD countries have the most favourable environments for technological proficiency, and the United States is best in class. Upper-middle-income and Central and Eastern European countries typically have moderate environments for developing tech-savvy populations. Several lower-middle-income and all the African countries studied perform poorly.

l In addition to the United States, the United Kingdom, Denmark, the Netherlands and Australia will have the most technologically capable workforces in coming years. A combination of sophisticated ICT infrastructures, frequent Internet usage in society, quality education opportunities relative to other countries and significant investment focus from governments and businesses put these countries at the top of the 56-nation index.

l Several African countries (Tunisia, Egypt, Algeria and Nigeria) rank at the bottom of the index. Pakistan rounds out the bottom five. Despite favourable demographics and stronger

economic growth relative to most high-income countries, these nations are hindered by weak education systems, meagre infrastructure, lower technology integration in everyday life and poor business and government environments.

l Unfavourable labour market conditions hamper scores in Western, Central and Eastern European countries. Ageing populations, labour market regulations and increasing youth unemployment drive low scores in the labour market category. Additionally, countries with stagnant economies, such as Greece and Spain, are experiencing “brain drain”. In Greece, for example, unemployment and other labour market-related issues are encouraging IT professionals to relocate to more stable European countries.

l The high-income countries of East Asia and Pacific—South Korea, Singapore, Taiwan, Japan and Hong Kong—will have technically capable workforces in the future. These countries record consistently strong performances across all categories. Singapore, Hong Kong and Taiwan score remarkably well in the business environment category. In addition to strong ICT goods trade, Singapore and Hong Kong do well in the overall business

Index findings



environment indicator and export a large volume of creative goods and services. In 2014 the World Bank cited Singapore as having the most business-friendly regulatory environment.

l Stable and reliable electric infrastructure is a crucial determinant of performance among upper-middle- and lower-middle-income countries, especially those in South Asia and Africa that perform weakly in the technology and connectivity infrastructure category. While having access to electric power is not enough to ensure a high score in technology and connectivity infrastructure, sufficient access to electricity is a requirement for scoring well.

l Mobile broadband penetration (3G and 4G access) is an area of weakness among lower-middle-income countries in the index. Mobile

broadband penetration per 100 people in the top ten countries in the technology and connectivity category is 94.5, compared with 19.9 in the bottom ten countries. Despite low mobile broadband penetration rates, many developing countries have mobile cellular subscription rates that exceed those of the United States.

l Unaffordable Internet is a major impediment to developing a society that consistently uses Internet technology. All five of the countries where Internet is least affordable—Nigeria, the Philippines, Argentina, South Africa and Peru—rank in the bottom ten countries in the technology and society category and in the bottom quarter of the overall index.



Overall Workforce of the Future rankings table

Weighted total of all category scores (0-100 where 100 = most favourable)

Rank Country Score/

100 Rank Country Score/


100

1 United States 78.1 20 Belgium 55.6 39 Thailand 39.8

2 United Kingdom 66.4 21 Ireland 54.6 40 Qatar 39.7

3 Denmark 64.1 22 Spain 53.8 41 Vietnam 38.6

4 Netherlands 64.0 23 Israel 51.8 42 Mexico 38.4

5 Australia 63.9 24 Czech Republic 49.1 43 Colombia 38.3

6 Switzerland 63.5 25 Hungary 47.5 44 Brazil 37.3

7 Germany 63.4 26 China 47.4 45 Saudi Arabia 36.1

8 Sweden 62.3 27 Slovakia 46.9 46 Argentina 35.8

9 South Korea 62.2 28 Poland 46.6 47 Philippines 35.1

10 Canada 61.9 29 Italy 46.1 48 South Africa 34.6

11 Finland 61.6 30 Chile 45.8 49 Peru 33.5

12 Norway 61.4 31 Croatia 44.8 50 Indonesia 32.8

13 Singapore 60.1 32 Malaysia 44.8 51 India 32.2

14 Taiwan 60.0 33 United Arab Emirates 44.6 52 Tunisia 31.6

15 Japan 59.1 34 Greece 44.4 53 Egypt 29.3

16 Hong Kong 57.9 35 Romania 43.7 54 Pakistan 21.5

17 France 57.7 36 Russia 41.4 55 Algeria 21.2

18 Austria 57.7 37 Turkey 41.3 56 Nigeria 20.0

19 New Zealand 56.8 38 Costa Rica 40.6

Overall score, by region

Rank Region Score/100

1 North America 70.1

2 Western Europe 58.6

3 East Asia and Pacific 52.3

4 Central and Eastern Europe 45.9

5 Latin America 38.7

6 Africa and Middle East 35.1

7 South Asia 28.9

Overall score, by income classification

Rank Income group Score/100

1 High income – OECD 57.8

2 High income – nonOECD 48.2

3 Upper-middle income 38.5

4 Lower-middle income 30.0



Technology and connectivity infrastructureThe technology and connectivity infrastructure category measures not only the extent to which individuals and businesses can readily access the Internet and mobile networks, but also the quality, security and reliability of those networks. Faster and more secure technology infrastructure creates an environment that is conducive to, and a society that is familiar with, using technology and the Internet in day-to-day life. This ease of dealing with technology transfers to the workplace, where familiarity and access make workers better able and more willing to utilise technology.

Technology and connectivity infrastructure rankings

Rank Country Score/



100

1 South Korea 86.0 20 France 61.5 39 Qatar 41.5

2 Netherlands 80.2 21 Ireland 57.1 40 Turkey 41.3

3 Switzerland 78.7 22 Czech Republic 56.7 41 Costa Rica 39.7

4 Denmark 78.1 23 Israel 55.5 42 Colombia 37.9

5 Norway 74.5 24 Spain 54.3 43 Vietnam 37.7

6 Finland 74.0 25 Greece 54.0 44 Brazil 37.4

7 Sweden 73.4 26 Slovakia 52.3 45 Mexico 37.3

8 Japan 70.9 27 Romania 51.3 46 Indonesia 35.7

9 United States 69.5 28 Poland 50.3 47 Egypt 35.4

10 Australia 69.3 29 Hungary 50.3 48 Philippines 35.3

11 Singapore 68.2 30 Italy 49.2 49 Argentina 34.4

12 Taiwan 67.4 31 Chile 47.7 50 Peru 32.6

13 Germany 66.9 32 United Arab Emirates 46.3 51 South Africa 30.2

14 United Kingdom 65.2 33 Croatia 45.2 52 Nigeria 28.8

15 Canada 64.5 34 Thailand 44.5 53 India 26.5

16 Belgium 64.2 35 China 44.2 54 Tunisia 26.3

17 Hong Kong 63.6 36 Russia 44.0 55 Algeria 23.6

18 Austria 63.3 37 Saudi Arabia 43.1 56 Pakistan 23.1

19 New Zealand 62.3 38 Malaysia 42.6

Technology and connectivity is comprised of five indicators that collectively measure the prevalence of high-quality Internet and mobile networks within a country. These indicators are:

l Prevalence of high-speed Internetl Electric powerl Secure Internet serversl Quality of high-speed Internetl Competition in ITC

Availability and access to the network is measured through two indicators: electric power and prevalence of high-speed Internet. Electric power is comprised of both electric power transmission and distribution losses and rural access to electricity; these sub-indicators collectively assess whether or not a country has the basic electric power infrastructure that allows people to access the Internet. The prevalence of high-speed Internet indicator considers whether or not people within a country who have access to



the Internet have high-speed Internet. This indicator is a composite of fixed (wired) broadband subscriptions—subscriptions to high-speed access to the Internet at 256 kbit/s or more through cable modem, DSL and other fixed broadband subscriptions—and mobile broadband penetration per 100 people.

Internet usage tends to be higher in societies where the network is reliable and efficient. Two indicators, quality of high-speed Internet and competition in ICT, evaluate the quality and reliability of the Internet in a country. The competition in ICT indicator assesses the level of competition in the Internet and telephony sectors. In countries where the market is more competitive, providers will be required to provide faster, more reliable Internet services to prevent users from switching to the competition. There is less incentive to provide high-quality service in countries where the ICT sector is monopolised. Quality of high-speed Internet is comprised of six unique sub-indicators that look at download and upload speed and latency (delays in delivering packets of data from one point to another) for both fixed Internet and mobile.

The final technology and connectivity infrastructure indicator looks at the number of secure internet servers within a country. This indicator reflects the degree of Internet security infrastructure. Although individuals may not be discouraged from using an unsecured Internet server, most businesses are unwilling to do so. Thus, a low presence of secure servers inhibits Internet use in the workplace.

Unsurprisingly, the high-income countries of East Asia and Pacific and Europe, led by South Korea, the Netherlands and Switzerland, dominate technology and connectivity infrastructure. Relatively high scores across all indicators, particularly in the presence of secure Internet servers—Switzerland has 2,829 secure servers per 1m people, while the United Kingdom has under half that number (1,292 per 1m)—drive these countries’ performance, although the European countries tend to outperform the other countries in this indicator (with the exception of South Korea).

Scandinavia does particularly well. As the index looks primarily at high-income countries that already have established technology infrastructures with quality high-speed Internet access and a diversified ICT sector, a comprehensive presence of secure Internet servers is the primary differential between those high-income countries that rank at the top of the category and those that score less well.

Among upper-middle- and lower-middle-income countries, especially those in South Asia and Africa that rank in the bottom third in this category, electric power is a crucial determinant of performance. Although having access to electric power is not enough to ensure a high score in the technology and connectivity infrastructure category, sufficient access to electricity is necessary to score well. In other words, without electricity, there is no Internet. India, Nigeria, Algeria and Pakistan have the lowest scores in the electric power indicator and are four of the five poorest performers in the category overall.

The electrical power problems facing India, which ranks last in the electric power indicator, epitomise the constraints of insufficient electricity. Although the country has made huge investments in increasing electric power over the past decade, demand for electricity still exceeds supply: Central Electricity Authority (CEA) data show that the total demand for electricity in India in April 2014 was 140,998 mw, while supply was just 133,442 mw. In August 2014 the International Energy Agency (IEA) estimated that 300m people in India (one-quarter of the population) did not have access to electricity.1 Electric power deficits are also impacting economic growth in India. The Federation of Indian Chambers of Commerce and Industry (FICCI) estimates that in 2013 electricity shortages cost the country approximately US$68bn of GDP. These shortages are primarily the result of transmission bottlenecks, according to the FICCI. Insufficient amounts of power generation investment are allocated to transmissions, which

1 World Energy Outlook 2014, International Energy Agency. Available at: http://www.worldenergyoutlook.org/resources/energydevelopment/energyaccessdatabase/



inhibits the country’s ability to improve capacity.2 As such, India is faced with an enormous challenge as it attempts to maintain strong economic growth, which requires huge amounts of power generation, and to provide access to the one-quarter of its citizens who live without electric power.3 Until these issues are addressed, access to technology and connectivity infrastructure for individuals and business will be impeded.

Mobile broadband penetration (3G and 4G access) is another area of weakness among developing countries in the index. Mobile broadband penetration per 100 people in the top ten countries in the technology and connectivity category is 94.5, compared with just 19.9 in the bottom ten countries. However, despite low mobile broadband penetration rates, many of the developing countries studied have mobile cellular subscription rates that exceed those of the United States. More people in the world now have access to mobile phones than to toilets,4 indicating that mobile technology is no longer just an advanced-economy phenomenon. For example, while Colombia has a mobile broadband penetration rate of just 7.9%, according to the World Bank it had 113 mobile cellular subscriptions per 100 people in 2014, while the United States had only 98.5 Increasingly pervasive in everyday affairs, mobile use is becoming progressively more sophisticated and efficient in lower- and middle-income countries. For example, farmers in Chile and India have been able to leverage their mobile phones to gain market information and, in turn, receive

2 “Power shortages cost India $68 billion in GDP: Ficci”, The Economic Times, September 27th 2013. Available at http://articles.economictimes.indiatimes.com/2013-09-27/news/42463871_1_transmission-sector-power-transmission-power-generation

3 “Statutory nods barrier for Indian power sector”, The Hindu, May 12th 2014. Available at http://www.thehindubusinessline.com/news/statutory-nods-barrier-for-indian-power-sector/article6001630.ece

4 “Deputy UN chief calls for urgent action to tackle global sanitation crisis,” UN News Centre, March 21st 2013. Available at http://www.un.org/apps/news/story.asp?NewsID=44452&Cr=sanitation&Cr1=#.VbkV5flVikr

5 “Mobile cellular subscriptions (per 100 people),” The World Bank. Available at http://data.worldbank.org/indicator/IT.CEL.SETS.P2

higher prices for their products.6 Evidently, the low mobile broadband

penetration rates in developing countries speak more to the lack of a proper infrastructure to support mobile broadband than to the lack of access to a mobile phone or low phone usage. Again, electric power availability is a prominent issue: in Indonesia, Colombia and South Africa cell subscription rates are high, but electricity is limited. This trend of high cellular subscription rates in countries where there are electric power gaps suggests that investing substantial effort in establishing a solid infrastructure—including widening access to basic necessities such as electricity and increasing the quality of Internet speed—would also see expanding mobile broadband penetration rates.

Poor infrastructure in developing countries represents an opportunity, rather than a hindrance, for companies seeking to establish themselves at the forefront of technological innovation. In California’s Silicon Valley, companies that assert their presence early attract consumers more easily than their late-arriving rivals and in turn enjoy more investment, higher valuation, more interest from the press and increased ease in attracting better employees, with self-reinforcing effects.7 This theory also applies in the developing world. The Economist Intelligence Unit’s App Gap Index, which explores mobile broadband in healthcare, financial services and education, highlights that service deficits provide an opportunity for service provision and present significant opportunity for early-mover businesses in developing markets.8

6 “2012 Information and Communications for Development: Maximising Mobile”, World Bank, 2012. Available at http://siteresources.worldbank.org/EXTINFORMATIONANDCOMMUNICATION ANDTECHNOLOGIES/Resources/IC4D-2012-Report.pdf

7 “To fly, to fall, to fly again”, The Economist, July 25th 2015.

8 “The App Gap Index: Mobile broadband in healthcare, financial services and education,” The Economist Intelligence Unit, 2013. Available at http://www.eiu.com/public/thankyou_download.aspx?activity=download&campaignid=AppGap.



Technology and societyTechnology and society describes the technology environment surrounding consumers and the general population. In the Workforce of the Future Index, this category measures the prevalence of technology in the daily lives of the general population and this population’s capacity for adopting technology. As seen in the technology and connectivity infrastructure category, the presence of and familiarity with technology in daily life results in more tech-savvy employees, with high capabilities and a greater propensity to use technology in the workplace.

Technology and society rankings

Rank Country Score/



100

1 United States 77.5 20 South Korea 46.5 39 Romania 37.4

2 United Kingdom 62.7 21 New Zealand 46.4 40 Chile 36.9

3 Norway 61.6 22 Hong Kong 45.9 41 Malaysia 36.6

4 Denmark 59.8 23 Italy 45.3 42 Costa Rica 36.3

5 Germany 57.4 24 Singapore 45.2 43 Egypt 33.7

6 Sweden 56.7 25 Croatia 42.4 44 Thailand 33.2

7 Australia 55.8 26 Poland 41.7 45 Colombia 32.4

8 Switzerland 55.7 27 Slovakia 41.7 46 India 31.9

9 Netherlands 55.4 28 United Arab Emirates 41.1 47 South Africa 31.7

10 Austria 54.1 29 Turkey 40.8 48 Argentina 31.3

11 Finland 53.1 30 Czech Republic 40.7 49 Tunisia 30.3

12 Canada 51.5 31 Greece 40.2 50 Saudi Arabia 30.2

13 China 50.5 32 Hungary 39.8 51 Peru 29.8

14 Japan 50.3 33 Russia 39.8 52 Indonesia 29.2

15 Ireland 48.1 34 Israel 39.8 53 Philippines 27.9

16 Taiwan 47.8 35 Qatar 39.5 54 Pakistan 27.1

17 Belgium 47.5 36 Vietnam 39.4 55 Algeria 22.9

18 France 47.0 37 Brazil 39.0 56 Nigeria 18.1

19 Spain 46.5 38 Mexico 38.6

The technology and society category is measured across five indicators that look at the affordability of the Internet and Internet usage for a multitude of purposes. The indicators included are:

l Affordabilityl Internet usagel Advanced usage of Internetl B2C (business-to-consumer) e-commerce salesl Locally hosted web content generation

The first two indicators—affordability and Internet usage—assess whether or not the Internet is being used at a basic level, assuming that Internet technology is available. The proportion of individuals using the Internet and the proportion of active Internet users managing a social media account, which is measured as the percentage of active Internet users aged 16-54 who have managed a social media account (such as Facebook or Twitter) in the past six months, are used to gauge basic Internet usage.



Even if connectivity infrastructure exists, it is not guaranteed that basic Internet is being used. Expensive Internet access inhibits use. Therefore, the index also considers the affordability of Internet access to determine the prevalence of Internet usage in society. This indicator is comprised of five sub-indicators that measure fixed and mobile broadband prices as a percentage of income per capita and fixed broadband and prepaid mobile cellular tariffs, calculated as monthly subscription charges and average per-minute costs of mobile cellular calls, respectively. The final sub-indicator in affordability is the Gini index, which measures countries’ income distribution. In countries with high Gini coefficients (reflecting large income distribution disparities), lower-income members of the population are less likely to be able to afford access to the Internet.

The remaining indicators look at how much and in what way people are using the Internet beyond basic usage. The advanced usage of Internet indicator includes four sub-indicators that centre on gaming revenues and hacking activities. These are included on both an economy-wide and a per-capita basis. The per capita and the economy-wide US dollar amount of B2C e-commerce sales collectively form the B2C e-commerce indicator. Finally, locally hosted web-content generation looks at the prevalence of generic and country-code top-level domains to assess which countries are generating the most Internet content.

As the birthplace of many popular social media platforms, including Twitter, Facebook and Instagram, the United States, as expected, dominates the technology and society category with a lead of almost 15 points; it is followed by the United Kingdom, Norway, Denmark and Germany. In the highest-scoring countries Internet usage is significantly advanced, and they all generate large amounts of locally hosted web content. Additionally, revenues from B2C e-commerce sales, both overall and in per-capita terms, are large and growing across the top-performing economies.

There appears to be a strong connection between the prevalence of gaming among

adolescents and adults in a country and the growing interest in STEM (science, technology, engineering and mathematics) careers. Many countries are seeking ways to foster that interest. For example, the United Kingdom, which has the fourth-highest gaming revenues per capita of all the countries in the index, boasts the Dare to be Digital contest, which fosters ICT-driven innovation and education by asking domestic and international students to submit designs for new video games.9 Such programmes create channels for youths interested in gaming to learn about programming and development, and ultimately bolster a technology-inclined society.

The most technological societies, despite fostering environments that promote tech-ingrained citizens, still face challenges. Internet affordability is an area of weakness for the most technological societies. Of the top five countries in the technology and society category, only Norway also ranks in the top five for affordability, which suggests that high costs are not necessarily connected to how much people actually use Internet; people with sufficient disposable income may be willing to pay a higher price for a mediocre Internet service.

The willingness to purchase high-priced, potentially mediocre Internet services is only found in countries where incomes are high enough to afford them in the first place. Lower-income families in the United States bear the brunt of inflated Internet rates, as they simply cannot afford such prices, regardless of the pervasiveness of the Internet in society. More than 25% of Americans do not have access to the Internet at home.10 A National Telecommunications and Information Administration and Economics and Statistics Administration report noted that 28% of the households in the US that lacked home Internet cited affordability as the key reason. For

9 “Innovation and Employment in the Intelligent Community”, Intelligent Community Forum, 2012. Available at https://www.intelligentcommunity.org/clientuploads/PDFs/WP-Innovation-Employment.pdf

10 “U.S. Internet users pay more and have fewer choices than Europeans”, The Centre for Public Integrity, April 1st 2015. Available at http://www.publicintegrity.org/2015/04/01/16998/us-Internet-users-pay-more-and-have-fewer-choices-europeans



another 13%, lack of an adequate computer was the primary deterrent, which also indicates an affordability issue.11

On a global level, unaffordable Internet also prevents society from using Internet technology: the five countries where Internet is least affordable—Nigeria, the Philippines, Argentina, South Africa and Peru—rank in the bottom ten countries in the technology and society category. Nigeria is the starkest example; it scores almost 15 points lower than all the other countries in the index on affordability. Only one-third of Nigerians use the Internet, according to the Alliance for Affordable Internet, as extortionate prices (the average broadband plan costs 39% of the average Nigerian’s monthly income) make it impossible to

11 “Exploring the Digital Nation: America’s Emerging Online Experience”, National Telecommunications and Information Administration and the Economics and Statistics Administration, June 2013. Available at http://www.ntia.doc.gov/files/ntia/publications/exploring_the_digital_nation_-_americas_emerging_online_experience.pdf

use the Internet.12 However, the cost of Internet access across the country is dropping. The introduction of 4G Long-Term Evolution (LTE) technology and the Nigerian Communications Commission’s licensing of more Internet service providers (ISPs) will stoke competition and drive down prices. Companies are focused on expansion through both organic growth and mergers and acquisitions. Moreover, companies in Nigeria are investing in long-distance submarine cables from Europe that are increasing broadband capabilities across the country.13 This investment is important because such cables are the typical way to carry digital data; 95% of all Internet traffic travels below the waves.14

12 “Multi-stakeholder affordable Internet forum held in Abuja, Nigeria”, oAfrica, March 12th 2014. Available at http://www.oafrica.com/ict-policy/multi-stakeholder-affordable-Internet-forum-held-in-abuja-nigeria/

13 Emma Okonji, “Nigeria: Competition, Market Forces and Cost of Internet Services”, allAfrica, May 15th 2014. Available at http://allafrica.com/stories/201405150682.html

14 Charlie McCann, “The Proto-Internet”, Intelligent Life, July/August 2015. Available at http://moreintelligentlife.com/content/places/charlie-mccann/proto-Internet

Affordability vs Technology and society overall score

Source: Economist Intelligence UnitAffordability score Technology and society overall score

Top five index performers Bottom five index performers

0

10

20

30

40

50

60

70

80

90

United States

United Kingdom

Denmark Netherlands Australia Tunisia Egypt Pakistan Algeria Nigeria



Labour marketThe labour market category considers the future supply of a country’s technologically proficient workers relative to the available work. The overall labour market is important because the international demand for technologically proficient workers is growing faster than the supply of qualified workers. Countries with more promising labour markets will be in a better position to leverage technology in the workforce.

Labour market rankings

Rank Country Score/



100

1 United States 86.9 20 New Zealand 53.1 39 Belgium 44.0

2 United Arab Emirates 71.4 21 China 52.3 40 Pakistan 43.6

3 Philippines 64.7 22 Argentina 52.0 41 Hungary 43.1

4 Singapore 61.3 23 Chile 51.2 42 Japan 42.9

5 India 59.4 24 Indonesia 51.1 43 Netherlands 42.9

6 United Kingdom 59.1 25 Colombia 50.9 44 Czech Republic 42.4

7 Hong Kong 58.7 26 Russia 50.7 45 South Africa 42.0

8 Canada 58.3 27 Germany 49.7 46 Slovakia 41.3

9 Ireland 57.4 28 Mexico 49.5 47 Poland 41.1

10 Qatar 57.2 29 Norway 49.3 48 South Korea 40.6

11 Australia 57.0 30 Peru 49.1 49 Turkey 40.1

12 Denmark 55.7 31 Croatia 48.5 50 France 39.6

13 Vietnam 55.6 32 Switzerland 47.5 51 Egypt 34.3

14 Thailand 55.2 33 Spain 47.1 52 Algeria 34.3

15 Malaysia 55.0 34 Sweden 47.1 53 Italy 34.0

16 Israel 54.8 35 Finland 46.1 54 Tunisia 33.8

17 Romania 53.9 36 Nigeria 46.1 55 Greece 33.7

18 Costa Rica 53.9 37 Austria 45.9 56 Saudi Arabia 31.6

19 Taiwan 53.4 38 Brazil 45.1

This category encompasses six indicators that collectively address labour market demographics, skills gaps and labour market conditions within an economy. The labour market indicators are:

l Brain drain: highly-skilled emigration ratel EIU business environment/labour market

regulationl Youth unemployment and skills gapl Old-age dependency ratiol Freelancersl Economic participation gender gap

The EIU business environment/labour market regulation indicator explores labour market conditions within a country. The indicator itself includes an assessment of productivity, labour market regulation, wage laws, hiring of foreign nationals and future labour flexibility. It serves as a proxy for whether or not a country’s labour market regulations are favourable for employers.

Shifting workforce demographics have made the recruitment and retention of highly skilled workers a challenge. Four indicators address workforce demographics and the potential gap between labour demand and supply. The old-age



dependency ratio, which measures the ratio of people over the age of 65 to those aged between 15 and 64 in a country, looks at whether or not the working population is growing at a fast enough rate to sustain the demand for labour. Simultaneously, youth unemployment and skills gap evaluates whether or not the youth population in a country has the necessary skills to meet employer demands. Countries with high old-age dependency ratios and high youth unemployment and skills gaps will continue to struggle to find enough qualified persons to meet workforce demands.

The skilled-worker deficit can be addressed, at least in part, by increased female labour participation rates. In advanced economies, where women have been integrated into the workforce for at least the past few decades, women are already providing highly skilled labour, and the economic participation gender gap—defined as a combination of the labour force participation gap, the remuneration gap and the advancement gap (the gap between the career advancement of women and men)—is low. However, across countries in the developing world, the economic participation gender gap is high. In some cases, culture barriers prevent women from entering the workforce, but even in countries where the cultural stigmas that have historically inhibited women from working have started to lessen, many women and girls are still not receiving the training needed to help close the gap between supply and demand in the labour force.

Declining economic participation rates for women in some developing countries are not always a bad sign, however. In India, which ranks 53rd in the economic participation gender gap category, women’s labour force participation rates are falling, mainly because more women attend educational institutions. This means that women who might previously have entered the workforce as unskilled labourers or been employed in the informal economy will now enter the workforce later, but as skilled or highly skilled workers. The International Labour Organisation (ILO) reports: “The proportion of young women aged 15-24 [in

India] attending education has increased dramatically in recent years, rising from only 16.1% of the population in 1994 to 31.9% in 2010 and to 36.7% in 2012. This has corresponded with a decline in the overall youth female labour force participation rate, which fell from 35.8% in 1994 to 22.2% in 2010 and 20.2% in 2012.”15 The rise in secondary and tertiary education among women in India will help to bridge the gap between the supply of and demand for highly skilled workers and is likely to create a more technologically inclined labour pool.

The final factor impacting labour market demographics is brain drain, or the emigration of highly skilled workers. High unemployment rates, labour market stagnation, economic depression, unfavourable employment laws and poor opportunities for career advancement are all factors that might incentivise highly skilled people (members of the population with at least a tertiary education) to emigrate.

Finally, this category considers the number of freelancers who work in IT, design and the manufacturing and engineering space. The inclusion of this indicator provides an illustration of the number of people with skills in areas related to technology who might not be formally employed in the sector (they could be employed in different sectors or still attend school and undertake IT, design and manufacturing and engineering work on the side; or they could be self-employed or not be formally employed at all). The freelancer indicator also reflects the technological creativity of a country’s population and its ease of dealing with technology.

The labour market category favours countries with more youthful populations, decreasing youth unemployment—which tends to be tied to robust GDP growth—and more flexible labour regulations. As such, the labour market category negatively correlates with the rest of the index, as the lower- and middle-income countries that exhibit these

15 Steven Kapsos, Andrea Silberman and Evangelia Bourmpoula, Why is female labour force participation declining so sharply in India? International Labour Office (ILO), August 2014. Available at http://www.ilo.org/wcmsp5/groups/public/---dgreports/---inst/documents/publication/wcms_250977.pdf



characteristics tend to struggle across the other index categories. The United States again dominates the rankings, largely owing to favourable labour market conditions: it has almost twice as many freelancers as any other country in the index and a small economic participation gender gap, although women hold only 30% of technology positions.16 The South Asian and East Asian and Pacific countries also perform particularly well. High retention rates of highly skilled workers, falling youth unemployment as economies expand and low old-age dependency ratios in the emerging markets of South Asia (Japan is an exception) underlie these strong scores.

Both Western and Central and Eastern Europe, where GDP growth has been modest or stagnant and populations are aging rapidly, struggle in the labour market category. Although the Scandinavian countries comprise half of the top ten countries in the economic participation gender gap indicator, the high old-age dependency ratios across Western Europe negatively impact the region’s overall score. In Central and Eastern Europe, high youth unemployment and extreme emigration figures offset moderate performance across the other indicators. The average regional emigration rate for workers with a tertiary education in the countries of Central and Eastern Europe is 16.3%, compared with an average of 7.3% for the 56 countries in the index. For example, in Romania, where the emigration rate for people with tertiary education is 20.6%, low wages and poor working conditions are resulting in an exodus of medical staff to countries where the

16 Catherine Bannister, Judy Pennington and John Stefanchik, IT worker of the future: A new breed. Deloitte University Press, January 29th 2015. Available at http://dupress.com/articles/tech-trends-2015-IT-worker-of-the-future/?id=gx:2el:3dc:dup1011:eng:cons:tt15:dcpromo

pay is better.17 The struggling economies of Western Europe, such as Greece and Spain, are also experiencing a brain drain: in Greece, as funds from publicly supported sectors are further reduced, IT professionals and scientists are moving to more stable European countries such as Germany and Switzerland.18

Brain drain does not plague the United States, and the country boasts the highest number of IT, design (multimedia) and manufacturing and engineering freelancers. The United States, with its density of tech-savvy residents, is tech-centric: Silicon Valley houses over US$3trn worth of technology companies. According to The Economist, one in five graduates from American business schools go to work for a technology firm, eschewing the prestige of Wall Street for the forward-facing start-up community in California. And the competition for these highly skilled workers is intense—the starting salary for a software engineer in San Francisco is US$150,000.19

Similar technology hubs are emerging all over the world: the UK has London’s Silicon Roundabout, Germany has Berlin’s Silicon Allee and Israel has Silicon Wadi in its coastal plain. Although lacking the sheer degree of funding on which America’s Silicon Valley runs, these technology communities are prolific. Silicon Wadi boasts more start-ups per capita than Silicon Valley.20 Like Silicon Valley, these companies recruit top talent straight out of university (London holds a popular “Silicon Milkroundabout” recruitment event), appealing to graduates to choose technology over the financial sector.21

17 Itzair Aguirre, “Struggling European economies contend with brain drain”, Global Risk Insights, January 15th 2015. Available at http://globalriskinsights.com/2015/01/struggling-european-economies-contend-with-brain-drain/

18 Ibid.

19 “To fly, to fall, to fly again,” The Economist, July 25th 2015.

20 “For Real Innovation, It’s Not Silicon Valley But Silicon Wadi”, Forbes, October 2nd 2013. Available at http://www.forbes.com/sites/theyec/2013/10/02/for-real-innovation-its-not-silicon-valley-but-silicon-wadi/

21 https://www.siliconmilkroundabout.com/



Education and technology skillsThe education and technology skills category evaluates the extent to which current and future workforces have mastered the skills necessary to work in a technology-driven environment. In the Workforce of the Future Index, it is crucial to assess not only the prevalence of skilled labour in a country (which is measured by the labour market category), but also by the quality of the skills attained.

Education and technology skills rankings

Rank Country Score/



100

1 United States 77.1 20 Austria 50.3 39 Vietnam 31.7

2 United Kingdom 69.1 21 Israel 50.2 40 Malaysia 31.2

3 Germany 64.6 22 Norway 49.5 41 United Arab Emirates 29.3

4 South Korea 63.3 23 Hong Kong 49.0 42 Tunisia 28.8

5 Canada 63.0 24 Poland 48.6 43 South Africa 24.9

6 Australia 62.6 25 Russia 48.1 44 Colombia 24.2

7 Netherlands 58.9 26 Czech Republic 47.2 45 Costa Rica 23.0

8 Taiwan 57.6 27 Greece 46.0 46 Brazil 22.0

9 France 56.3 28 Italy 45.0 47 Mexico 20.9

10 Finland 56.2 29 Hungary 45.0 48 India 20.7

11 Japan 55.4 30 Slovakia 41.8 49 Algeria 19.4

12 Switzerland 54.5 31 Croatia 41.2 50 Qatar 18.3

13 Denmark 54.4 32 China 39.6 51 Egypt 17.9

14 Sweden 54.0 33 Romania 36.6 52 Peru 17.4

15 New Zealand 53.2 34 Turkey 35.8 53 Indonesia 16.9

16 Spain 53.1 35 Chile 34.1 54 Philippines 16.0

17 Singapore 52.5 36 Saudi Arabia 33.8 55 Pakistan 6.6

18 Belgium 51.3 37 Thailand 32.6 56 Nigeria 2.1

19 Ireland 51.1 38 Argentina 32.0

This category is comprised of six indicators that explore the average amount of schooling a population obtains, the orientation of this schooling towards more technical fields, and the results of this schooling. The indicators included in the education and technology skills category are:

l General state of educationl Tertiary enrolment in STEM and artsl Maths and numeracy skills among studentsl Programming talentl Journal impact factor: the h-indexl Internet prevalence in schools

The general state of education indicator, a composite of the secondary gross enrolment ratio, the tertiary gross enrolment ratio and mean years of schooling, reflects the general education environment within each economy. People in countries with higher average years of schooling and enrolment rates in post-primary education are more likely to be introduced to technical fields and use technology in school; but, since in most cases training in programming and engineering requires that schools have Internet access, the Internet prevalence in schools indicator measures the percentage of primary and secondary schools that



have some form of Internet access. Tertiary enrolment in STEM and arts and

maths and numeracy skills among students gauges the quality of skills related to technology attained during education. Both enrolment in STEM and enrolment in arts and humanities courses are considered in the tertiary enrolment indicator. Although enrolment is STEM carries most of the weight within the indicator, the technology field also requires creative and visual work—for example, in design and multimedia roles; thus, tertiary enrolment in arts-related courses is included. The student maths and numeracy skills indicator is derived from PISA (Programme for International Student Assessment) and TIMSS (Trends in International Mathematics and Science Study) scores in mathematics, and numeracy skills at the primary level. This indicator, which quantifies the mathematics knowledge of students at both the primary and the secondary school level, serves as a proxy for problem-solving skills.

Although programming skills does not look specifically at the quality of programming taught in schools or formal education programmes, it measures the number of programmers available in a country that has extremely high ratings on Stack Overflow, a question-and-answer site for programmers. This indicator indirectly assesses the quality of programming talent available in the country.

Finally, the journal impact factor (the h-index) substitutes for the quality and quantity of research that a country is generating. The h-index looks at both the number of articles published in a country and the number of citations per publication, creating a way of quantifying the amount of high-level research being done in each country.

Low Internet prevalence in schools and poor maths and numeracy skills, which are likely to be a product of less schooling in general, drive weak performances in education and technology skills in Nigeria, Pakistan, the Philippines and Indonesia. Nigeria and Pakistan, which rank in the bottom five on almost every indicator in this category, fare particularly badly. This is unsurprising, as many of

the indicators in this category—Internet prevalence in schools, programming talent and journal impact factor—require Internet usage, and both Nigeria and Pakistan have limited Internet usage. There is a strong correlation between general education and Internet use. A recent study by McKinsey & Company, a consultancy, noted that many people in the developing world first gain access to the Internet and technology through schools and jobs. As enrolment in higher education surges, the opportunities for contact with technology increase, fostering higher Internet usage. The report suggests that improving educational quality and access breaks down barriers to Internet adoption and improves digital and language literacy, especially since people without Internet access are disproportionately low-income, illiterate and female and live in rural areas.22 As education spreads to marginalised populations, and as tech connectivity and infrastructure improve and the urbanisation trend continues, the middle class will continue to grow and Internet usage will also rise.

The United States, the United Kingdom and Germany top the rankings in the education and technology skills category. Large numbers of top-talent programmers and a high journal impact factor are common strengths among these countries. The United States, despite some deficiencies in maths and numeracy skills among students, overshadows the other 55 countries in the journal impact factor indicator with much stronger h-index scores than the United Kingdom, which is ranked second.

In addition to relatively modest maths numeracy skills, the performance of the United States in tertiary enrolment per 100,000 people in STEM programmes is middling, but its strong performance in arts enrolment per capita counterbalances this weakness. Although poor scores in maths and science tests are a concern for many Americans, the sheer quantity of quality

22 Offline and falling behind: Barriers to Internet adoption, McKinsey & Company, September 2014. Available at http://www.mckinsey.com/insights/high_tech_telecoms_Internet/offline_and_falling_behind_barriers_to_Internet_adoption



research generated in the United States suggests that there is a sizeable segment of the population capable of generating technological innovation. However, even if STEM skills are more correlated

with technology, there will always be a need for people such as designers to help translate and communicate ideas and messages.

Government environmentThe government environment category describes the extent to which the regulatory environment and laws in each country facilitate ICT visibility, access and security. It also measures how well the government has integrated technology into its operations.

Government environment rankings

Rank Country Score/



100

1 France 88.0 20 Belgium 73.2 39 Turkey 52.0

2 United States 85.6 21 Hungary 71.5 40 South Africa 51.9

3 Netherlands 83.2 22 Norway 69.3 41 Brazil 50.2

4 Australia 81.7 23 Chile 68.4 42 Tunisia 49.9

5 Finland 79.9 24 Colombia 66.4 43 Philippines 48.5

6 Switzerland 79.7 25 Ireland 66.1 44 China 47.9

7 United Kingdom 79.2 26 Malaysia 65.6 45 Qatar 47.4

8 Canada 78.9 27 Italy 65.0 46 Vietnam 45.7

9 Taiwan 78.5 28 Czech Republic 64.2 47 United Arab Emirates 45.2

10 Sweden 78.2 29 Costa Rica 63.9 48 Thailand 43.4

11 Spain 78.0 30 Israel 63.1 49 Indonesia 43.3

12 South Korea 77.2 31 Slovakia 63.1 50 India 39.9

13 Singapore 77.1 32 Croatia 62.3 51 Saudi Arabia 39.6

14 Austria 77.1 33 Poland 59.6 52 Russia 38.1

15 Japan 76.6 34 Greece 58.3 53 Egypt 36.5

16 Denmark 76.3 35 Romania 56.9 54 Algeria 21.0

17 Germany 75.9 36 Mexico 56.8 55 Nigeria 20.5

18 Hong Kong 74.1 37 Peru 54.5 56 Pakistan 17.8

19 New Zealand 73.2 38 Argentina 53.2

This category consists of five indicators, which look at governments’ commitment to creating an efficient ICT sector and at their adoption of technology. The following indicators are used to assess the government environment:

l Government commitment to ICTl Government effectiveness risk

l Online services indexl Cyberlaw adoptionl IP (intellectual property) environment

Three indicators explore the extent of the ICT regulatory environment, a country’s government effectiveness in implementing and supporting the regulatory environment, and its encouragement of



technology innovation. Government commitment to ICT qualitatively gauges public financial commitment to ICT and regulations to promote universal ICT provision and market innovation. The IP environment indicator considers government encouragement of innovation and the protection of intellectual property. The indicator addresses resident IP activity, membership and ratification of international treaties and the prevalence of IP infringement. Finally, government effectiveness risk measures the general effectiveness of a country’s government to determine if the country is sufficiently able to implement and enforce the regulations that are in place; policy formulation, quality of bureaucracy, excessive bureaucracy, vested interests, corruption and human rights violations are all reflected.

Cyberlaw adoption looks at whether a country has both cybercrime and e-commerce legislation in place that protects consumers, data and e-transactions. Cyberlaw protection promotes the use of technology by both citizens and individuals. Finally, the online services index looks at whether or not the government provides simple and accessible online services. The indicator includes four stages of development: emerging information services, enhanced information services, transactional services and connected services.

France, with its comprehensive IP protection laws and online government services, has the most ICT-friendly government environment. It also has broad cyberlaws and a fairly strong government commitment to ICT, although government effectiveness risk is an area of relative weakness for France, primarily through limitations on liberalisation and pro-business policies for both nationals and foreigners. Interventionist habits are deeply rooted in France, and there is a tangible risk that the legal and regulatory environment will continue to be undermined by unpredictable state interventions.23

The OECD countries, especially those in North

23 “The business environment continues to be distorted by state intervention”, The Economist Intelligence Unit, June 5th 2015. Available at http://viewswire.eiu.com/index.asp?layout=RKArticleVW3&article_id=1993228783#5

America and Western Europe, also perform well in this category. The OECD Digital Economy Outlook 2015 notes that the majority of OECD countries cite digital privacy and security as their top ICT priority after building up and further developing broadband infrastructure.24 These priorities are reflected in the Workforce of the Future Index: all OECD countries in the index have comprehensive cybersecurity regulations in place, and the Western OECD countries account for the 12 best-performing countries in the IP environment indicator.

As anticipated, there is a strong correlation between government environment scores and technology and connectivity infrastructure scores. Governments invest more in the technological infrastructure that supports innovation when they are committed to improving ICT and creating a more technologically adept society.

The economies of East Asia made huge public-sector investments in power generation, telecommunications and transport in the late 1990s and early 2000s. By the turn of the century these governments were investing 6-8% of their GDP in infrastructure, which transformed the region into a world of manufacturing; the success has supported government investments across the telecoms, transport and construction industries.25 These investments, coupled with a skilled and proficient workforce, are the foundation of future economic success. East Asia and Pacific’s high average score in the government commitment to this ICT indicator reflects this prioritisation.

For example, in 2006 the government of Singapore launched the Wireless@SG programme to roll out a wireless broadband market in Singapore and to promote usage through setting up Wi-Fi hotspots across the country.26 The programme has developed into the Next

24 OECD Digital Economy Outlook 2015, Organisation for Economic Co-operation and Development, July 15th 2015. Available at http://www.oecd.org/Internet/oecd-digital-economy-outlook-2015-9789264232440-en.htm

25 Michael Ivanovitch, “As Asia infrastructure booms, investment opportunities abound”, CNBC, April 5th 2015. Available at http://www.cnbc.com/2015/04/05/asias-infrastructure-investment-bonanza.html

26 “Wireless”, iDA Singapore. Available at http://www.ida.gov.sg/Tech-Scene-News/Infrastructure/Wireless



Generation National Infocomm Infrastructure initiative, an ultra-high-speed optical fibre network intended to spur the development of knowledge-based sectors and increase innovative interactive digital services in businesses, schools and homes—and to reinforce the country’s status as an infocomm centre.27

Conversely, countries that struggle in the government environment category also suffer in connectivity and infrastructure. Countries in Central and Eastern Europe tend to receive middling scores in both the government environment category (primarily because of weak scores in the online services index) and the technology connectivity and infrastructure

27 “Towards an Infocomm-Enabled Future with Next Gen NBN”, iDA Singapore. Available at http://www.ida.gov.sg/Tech-Scene-News/Infrastructure/Wired/Next-Gen-NBN

category. Russia’s performance in government environment, where it ranks 52nd out of 56, is particularly poor. An IBM Global Business Services report suggests that Russia’s inadequate connectivity infrastructure is considerably less developed than that of other east European countries and the advanced economies, which, combined with weak regulation, is restricting technology and innovation that could promote productivity growth.28 A lack of government prioritisation, as well as weak intellectual property right laws and enforcement (which would otherwise promote private-sector action), are key inhibiting factors.29

28 Susanne Dirks and Mary Keeling, “Russia’s productivity imperative”, IBM Global Business Services, 2009. Available at http://www.ibm.com/smarterplanet/global/files/us__en_us__government__gbe03244usen.pdf

29 Ibid.



Business environmentThe business environment category describes how conducive a market is to technology adoption. In order to assess a market’s ability and willingness to adopt technology, it is important to capture not only the overall macroeconomic and political environment, but also the penetration of ICT in businesses more generally.

Business environment rankings

Rank Country Score/



100

1 United States 79.2 20 United Arab Emirates 53.3 39 Hungary 34.8

2 Singapore 69.3 21 Ireland 53.3 40 Poland 33.4

3 Hong Kong 68.7 22 Belgium 52.8 41 Indonesia 33.3

4 Germany 64.0 23 Austria 52.6 42 Peru 29.1

5 Switzerland 62.7 24 France 52.6 43 India 28.6

6 Sweden 62.4 25 South Korea 50.9 44 Romania 28.4

7 United Kingdom 60.7 26 Qatar 50.7 45 Italy 27.8

8 Taiwan 59.4 27 Chile 48.3 46 Croatia 27.6

9 Norway 58.7 28 Czech Republic 43.0 47 Colombia 27.3

10 Canada 58.4 29 Spain 42.3 48 Vietnam 24.0

11 Israel 57.6 30 Philippines 41.6 49 Russia 22.4

12 New Zealand 57.6 31 Saudi Arabia 40.9 50 Greece 22.4

13 Finland 57.0 32 Slovakia 40.6 51 Tunisia 21.5

14 Denmark 56.4 33 Costa Rica 40.3 52 Pakistan 17.5

15 Australia 56.1 34 Thailand 40.2 53 Nigeria 16.0

16 China 55.9 35 Turkey 38.5 54 Argentina 15.7

17 Netherlands 55.9 36 South Africa 36.8 55 Egypt 13.0

18 Malaysia 55.6 37 Mexico 36.7 56 Algeria 3.5

19 Japan 54.8 38 Brazil 36.0

Five indicators, which centre on the overall business environment, the market for technology-related goods and the receptiveness to and use of technology at the company level, collectively measure each country’s business environment. The indicators considered are:

l EIU overall business environmentl Firm-level absorption of technologyl ICT goods tradel Entrepreneurshipl Creative goods and services exports

A country’s general business environment is quantified by the EIU’s overall business environment scores, which encompass the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. Countries with liberal markets, lax policies towards foreign investment, extensive financing and a sound macroeconomic environment present the best opportunities for



technology and innovation to thrive. The ICT goods trade indicator, a measure of ICT

goods as a percentage of total merchandise trade, and creative goods and services exports—the sum, in US dollars, of all creative goods and creative services that a country exports—collectively gauge a country’s market for and production of technology-related goods and services. Firm-level absorption of technology assesses, at the company level, the extent to which executives perceive that businesses adopt new technologies.

The final indicator in this category, entrepreneurship, looks at both the strength of the start-up culture in a country and the country’s perception of entrepreneurship. The indicator is comprised of four sub-indicators, three of which evaluate the prevalence of entrepreneurship in an economy. Both the absolute and the per-capita numbers of start-ups on CrunchBase, the most comprehensive online database or platform that showcases innovative companies, are included in this indicator. The percentage of the population involved in opportunity-driven entrepreneurial activity measures the proportion of the working-age population that has identified opportunities in the market to create a start-up, excluding those engaged in entrepreneurship out of necessity. Finally, the perceptions of entrepreneurial environment sub-indicator assesses the percentage of people between the ages of 18 and 64 who regard entrepreneurship as a desirable career choice. If people take a negative view of opportunity-driven entrepreneurship, they may be dissuaded from starting innovative companies.

The United States, which performs well in every indicator (although it ranks 17th in ICT goods trade, behind the East Asian and Pacific countries that account for eight out of ten of the top-ranked countries in this indicator), dwarfs other countries in this category. The high-income countries of East Asia, notably Singapore, Hong Kong and Taiwan, also score well. In addition to strong ICT goods trade, Singapore and Hong Kong do well in the EIU overall business environment indicator and export large amounts (in US dollar terms) of creative

goods and services. Low corporate tax rates—16.5% in Hong Kong and 17% in both Singapore and Taiwan—as well as continuing reforms to further improve the business environment continue to help the region flourish as a business hub.30 Additionally, increased receptiveness to foreign direct investment has boosted growth and increased competitive opportunities in the region. China has by far the highest amount of creative goods and services exports, but a weak business environment, modest firm-level absorption of technology and a rather poor environment for entrepreneurship result in the country ranking 16th overall in this category.

The African and Middle Eastern countries sit at the bottom of the business environment category, with Algeria, Egypt, Nigeria and Pakistan comprising four of the bottom five countries. A poor business environment, low levels of technology penetration within individual businesses and limited ICT goods trade all account for the weak performance. Entrepreneurship, however, is an area of relative strength and opportunity for these countries. Nigeria, for example, ranks second globally in the entrepreneurship indicator, owing to its top scores in the percentage of the population involved in entrepreneurial activity (19.7%, compared with an average of 5.3% across all the countries in the index) and in perceptions of entrepreneurial activity. That said, Nigeria scores quite poorly in the indicator that looks at the number of technology start-ups based in the country, which is true for most of the countries in the region (Israel is the exception).

Funding is the predominant hindrance to entrepreneurship in African countries, according to a study by the Omidyar Network and Monitor Group (now Monitor Deloitte). Many business owners in the region prefer to draw on funding from family and personal accounts because they fear the costs of external financing. Personal funds, however, are rarely extensive enough to bring projects to

30 “Corporate tax rates table”, KPMG. Available at https://home.kpmg.com/xx/en/home/services/tax/tax-tools-and-resources/tax-rates-online/corporate-tax-rates-table.html



fruition. Additionally, poor infrastructure and a lack of business advisory services inhibit entrepreneurial ideas from being acted upon across the region.31

Despite these challenges, there is a strong push for entrepreneurship not only in Nigeria but also across the African continent. Calls for “Africapitalism” ring out as those who support entrepreneurship hope to create wealth that can be reinvested in the continent through infrastructure,

31 “Accelerating Entrepreneurship in Africa”, Omidyar Network, April 2013. Available at http://ventureburn.com/wp-content/uploads/2013/04/Accelerating_Entrepreneurship_in_Africa_source_Ventureburn.pdf

jobs and, ultimately, the creation of an African middle class.32 The Nigerian billionaire chairman of investment firm Heirs Holdings, Tony Elumelu, has established the Tony Elumelu Entrepreneurship Programme, which will provide US$100m to entrepreneurs across Africa.33 The programme, and others like it, seeks to democratise access to opportunity so that the talent and innovation of Africa’s youth can be developed and lead to the emergence of a new “self-made” middle class.

32 “The rise of Africapitalism”, The Economist, November 20th 2014. Available at http://www.economist.com/news/21631956-entrepreneurs-will-transform-africa-says-tony-elumelu-chairman-heirs-holdings-and

33 Ibid.



In a benchmarking index, weights are assigned to each category and indicator to reflect different assumptions about their relative importance. There are various methods that can be used to determine those weights.

The findings presented in the Workforce of the Future Index are based on a preferred weighting scheme developed by The Economist Intelligence Unit (EIU). This weighting scheme combines quantitative methods and qualitative expertise to place a higher importance on categories and indicators that are more critical for evaluating the technological proficiency of future workforces.

The EIU developed the weighting scheme using a three-step process: (1) conducting a principle-component analysis; (2) verifying the weights with members of the expert panel; and (3) applying the EIU’s subject matter expertise. Based on this comprehensive process, the EIU preferred weighting scheme assigns the following category weights:

I. Technology and connectivity infrastructure – 20%II. Technology and society – 25%III. Labour market – 10%IV. Education and technology skills – 20%V. Government environment – 15%VI. Business environment – 10%

The table (next page) compares the EIU preferred weighting scheme with a neutral weighting scheme (that is, where each category is assigned an equal weight, which would reflect the equal importance of each domain, and each indicator within each category is also equally weighted).

The overall trends in the rankings and categories are comparable between the EIU preferred weighting scheme and the neutral weighting scheme. The United States stills ranks first with significantly higher scores than the other countries, and OECD countries continue to score well; however, there are differences in the individual country rankings. The countries most positively affected by the use of EIU preferred weights are Denmark, Sweden and Russia, while Singapore, Taiwan, Malaysia and Thailand are most negatively impacted.

EIU preferred weighting scheme



Category Overall score

EIU weights

Overall score

Neutral weights

Rank EIU

weights

Rank Neutral weights

Rank Change

United States 78.1 76.8 1 1 0

United Kingdom

66.4 63.6 2 2 0

Denmark 64.1 60.2 3 11 +8

Netherlands 64.0 61.5 4 5 +1

Australia 63.9 62.0 5 3 -2

Switzerland 63.5 60.9 6 7 +1

Germany 63.4 61.7 7 4 -3

Sweden 62.3 59.5 8 13 +5

South Korea 62.2 60.3 9 10 +1

Canada 61.9 60.4 10 8 -2

Finland 61.6 58.3 11 15 +4

Norway 61.4 58.5 12 14 +2

Singapore 60.1 61.2 13 6 -7

Taiwan 60.0 60.4 14 8 -6

Japan 59.1 56.2 15 16 +1

Hong Kong 57.9 59.7 16 12 -4

France 57.7 56.0 17 17 0

Austria 57.7 55.4 18 18 0

New Zealand 56.8 54.1 19 19 0

Belgium 55.6 53.0 20 20 0

Ireland 54.6 52.0 21 21 0

Spain 53.8 51.5 22 23 +1

Israel 51.8 51.7 23 22 -1

Czech Republic 49.1 47.4 24 26 +2

Hungary 47.5 46.0 25 28 3

China 47.4 48.3 26 25 -1

Slovakia 46.9 44.5 27 30 +3

Poland 46.6 43.4 28 31 +3

Category Overall score

EIU weights

Overall score

Neutral weights

Rank EIU

weights

Rank Neutral weights

Rank Change

Italy 46.1 42.0 29 33 +4

Chile 45.8 46.8 30 27 -3

Croatia 44.8 41.6 31 35 +4

Malaysia 44.8 48.4 32 24 -8

United Arab Emirates

44.6 44.8 33 29 -4

Greece 44.4 41.2 34 38 +4

Romania 43.7 41.3 35 37 +2

Russia 41.4 39.4 36 43 +7

Turkey 41.3 41.6 37 35 -2

Costa Rica 40.6 41.8 38 34 -4

Thailand 39.8 42.1 39 32 -7

Qatar 39.7 40.0 40 41 +1

Vietnam 38.6 40.9 41 39 -2

Mexico 38.4 39.5 42 42 0

Colombia 38.3 40.5 43 40 -3

Brazil 37.3 39.1 44 45 +1

Saudi Arabia 36.1 38.3 45 46 +1

Argentina 35.8 36.6 46 47 +1

Philippines 35.1 39.4 47 43 -4

South Africa 34.6 33.2 48 51 +3

Peru 33.5 36.5 49 48 -1

Indonesia 32.8 34.9 50 50 0

India 32.2 35.9 51 49 -2

Tunisia 31.6 32.3 52 52 0

Egypt 29.3 29.2 53 53 0

Pakistan 21.5 24.8 54 55 +1

Algeria 21.2 22.1 55 56 +1

Nigeria 20.0 25.3 56 54 -2

Comparison of the EIU preferred weighting scheme with a neutral weighting scheme



As businesses address the challenge of ensuring that there are enough technology-proficient workers over the next few decades, they require a framework that shows which countries have the structures in place to produce these workers. The Workforce of the Future Index creates this framework and measures each country against it.

Every country and region has strengths and weaknesses. Western European countries have exceptionally strong education levels, technology-integrated societies, excellent government regulations and developed infrastructures, but their stagnant economies and rapidly ageing populations risk a dearth of tech-savvy future workers. In the developing economies, there will be ample workers; however, insufficient connectivity infrastructure, a weak regulatory and enforcement environment and a society where technology has not yet been ingrained will limit the technology skills these workers are able to garner. It is in the advanced economies of East Asia that sufficient numbers of tech-savvy workers will be found. The high surpluses generated from manufacturing will underpin infrastructure

spending; business-friendly regulations will encourage further investment and growth; youth will have comprehensive introductions to both technology and STEM; and countries will be able to retain their talent. The United States and Canada are also countries where there will be few quantity and quality problems.

The Workforce of the Future Index identifies key factors that underpin a strong workforce of the future and point to areas for development. These areas of development vary across regions and countries, such as labour market challenges in Europe and infrastructure insufficiencies in Africa. The focus of government policies and business strategies that support the development of technological proficiency of the future workforce will differ, based on the unique conditions of each country. Despite the challenges facing each country, a strong emphasis on higher education, solid labour market fundamentals, improved infrastructure and increased Internet accessibility and social adoption will produce workforces of the future capable of leveraging more advanced technologies.

Conclusion



1. Summary

Definition of workforce of the futureFor the purposes of the index and this report, we define workforce of the future as the workforce readiness for technologically rich environments. This index is meant to serve as a foundation for educating business leaders about the different workforce challenges and opportunities facing employers as the world becomes more digitised. A country’s workforce is technologically proficient if there is a sufficient supply of workers capable of both leveraging technology in the workplace and developing technology-based innovations.

Workforce of the Future Index indicator framework1. Technology and connectivity infrastructure

1. Prevalence of high-speed Internet2. Electric power3. Secure Internet servers4. Quality of high-speed Internet5. Competition in ICT

2. Technology and society1. Affordability2. Internet usage3. Advanced usage of Internet4. B2C e-commerce 5. Locally hosted web content generation

3. Labour market1. Brain drain: highly-skilled emigration rate2. EIU business environment/labour market

regulation 3. Youth unemployment and skills gap4. Old-age dependency ratio5. Freelancers6. Economic participation gender gap

4. Education and technology skills1. General state of education2. Tertiary enrolment in STEM and arts3. Maths and numeracy skills among students4. Programming talent5. Journal citations6. Internet prevalence in schools

5. Government environment1. Government commitment to ICT2. Government effectiveness risk3. Online services index4. Cyberlaw adoption5. IP (intellectual property) environment

6. Business environment1. EIU overall business environment2. Firm-level absorption of technology3. ICT goods trade4. Entrepreneurship5. Creative goods and services exports

Appendix



Expert panelThe Workforce of the Future Index framework draws on the input of an expert panel that convened in April 2015. The panel was comprised of eight specialists representing a wide array of disciplines, ranging from labour economics and policy to human capital and technology. These experts discussed key topics at the intersection of

technology and the workforce and their suitability for inclusion in the indicator framework that forms the foundation of the Workforce of the Future Index. Their input, combined with feedback from AT&T and the EIU’s research, resulted in 32 quantitative and qualitative indicators distributed across six categories.

2. Categories and scoring criteriaThe six categories comprising the framework include three categories directly assessing the “preparedness” of the workforce for technology and three “environment” categories. Although this structure is not necessarily relevant for the index’s results, it does help the reader to understand the conceptual framework of the index.

For a successful adoption of technology by the workforce, the right conditions and enabling environment need to be present. Without these conditions, a workforce may not even have the ability to become technology-ready. The three pillars that the index has identified as

requirements for assessing a technology-capable workforce are: the availability and access to technology (Category 1: Technology and connectivity infrastructure); sound government commitment and protection of technology (Category 5: Government environment); and a friendly business environment that encourages innovation (Category 6: Business environment).

The “preparedness” categories assess the three key aspects for actual technology adoption by the workforce. A society with a “workforce of the future” has high adoption and utilisation rates of technology (Category 2: Technology and society); it has favourable labour market conditions (Category 3: Labour market) and a skilled workforce (Category 4: Education and technology skills).

I. Technology and connectivity infrastructureThis category describes not only the extent to which individuals and businesses can readily access the Internet and mobile networks, but also the quality, security and reliability of those networks. Faster and more secure technology infrastructure enables workers to better utilise technology in the workplace.

Indicator Sub-indicators and scoring schemes Source

1.1 Prevalence of high-speed Internet

This indicator includes both fixed broadband and mobile Internet penetration to properly account for the different forms of Internet adoption across countries

1.1.1 Fixed (wired) broadband per 100 peopleMeasured as the number of fixed (wired) subscriptions to high-speed public Internet access to the public Internet per 100 people

1.1.1 International Telecommunication Union (ITU), World Telecommunication/ICT Development Report and database

1.1.2 Mobile broadband penetration per 100 peopleMeasured as the number of mobile broadband (3G and 4G) subscriptions per 100 people

1.1.2 ITU



1.2 Electric power This indicator assesses whether or not a country has the basic electric power infrastructure that allows people to access to the internet

1.2.1 Electric power transmission and distribution losses (% of output)Takes into account losses in transmission between sources of supply and points of distribution and in the distribution to consumers; calculated as electric power transmission and distribution losses as a percentage of output

1.2.1 US Energy Information Administration (EIA)

1.2.2 Rural access to electricity (% of rural population)High electricity access in rural areas is a proxy for good infrastructure in remote areas

1.2.2 World Bank

1.3 Secure Internet servers (per 1m people)

1.3.1 Secure Internet servers (per 1m people)Measures the prevalence of servers using encryption technology in Internet transactions, an important condition for businesses to adopt technology

1.3.1 World Bank/Netcraft

1.4 Quality of high-speed Internet

This indicator assesses the quality of high-speed Internet based on six sub-indicators covering fixed and mobile

1.4.1 Internet download speedMeasured as the median consumer download speed on a fixed network in Mbps

1.4 Cisco

1.4.2 Internet upload speedMeasured as the median consumer upload speed on a fixed network in Mbps

1.4.3 Internet latencyMeasured as the delays experienced in delivering packets of data from one point to another; high latency (measured in ms) results in more delays

1.4.4 Mobile download speedMeasured as the median mobile consumer download speed in Mbps

1.4.5 Mobile upload speedMeasured as the median mobile consumer upload speed in Mbps

1.4.6 Mobile latencyMeasured as the delays experienced in delivering packets of data from one point to another; high latency (measured in ms) results in more delays

1.5 Competition in ICT 1.5.1 Level of competition index for Internet and telephony sectorsLiberalisation in ICT sectors tends to correlate highly with improved infrastructure and innovation in ICT sectors. This sub-indicator measures the degree of market liberalisation in 17 categories of ICT services on a scale of 0-2 (more liberal). For each economy, the level of competition in each of the categories is assessed as follows:0=Monopoly1=Partial competition2=Full competition

1.5.1 The World Economic Forum (WEF) Global Information Report (2015) calculations based on International Telecommunication Union (ITU); ITU World Telecommunication Regulatory Database




II. Technology and societyThis category describes the technology environment surrounding consumers and the general population. For the purposes of this index, this measures the prevalence of technology in the daily lives of the general population and their capacity for adopting it.


2.1 Affordability This indicator assesses the affordability of Internet based on five sub-indicators

2.1.1 Fixed broadband prices as percentage of GNIBy taking prices as a percentage of gross national income (GNI) per capita, the relative prices of broadband Internet are measured and allow for cross-country comparison

2.1.1 International Telecommunication Union (ITU) with GNI per capita based on World Bank data

2.1.2 Mobile broadband prices as percentage of GNIBy taking prices as a percentage of GNI per capita, the relative prices of mobile Internet are measured and allow for cross-country comparison

2.1.2 ITU with GNI per capita based on World Bank data

2.1.3 Fixed broadband Internet tariffsMeasures the monthly subscription charge for fixed (wired) broadband Internet service in PPP US$ per month

2.1.3 World Economic Forum calculations based on ITU and World Bank data

2.1.4 Mobile broadband Internet tariffsMeasures the average per-minute cost of different types of mobile cellular calls, pre-paid in PPP US$ per month

2.1.4 World Economic Forum calculations based on ITU and World Bank data

2.1.5 Gini indexCaptures income inequality in an economy by measuring the extent to which the distribution of income or consumption expenditure among individuals or households deviates from a perfectly equal distribution, where:0=Perfect equality100=Perfect inequality

2.1.5 World Bank; CIA World Factbook; International Monetary Fund

2.2 Internet usage This indicator assesses the society’s usage of the Internet in everyday life based on two sub-indicators

2.2.1 Proportion of active Internet users managing a social media accountRepresents the extent to which Internet users have integrated technology as channels for communication in their day-to-day lives; measured as the percentage of active Internet users aged 16-54 who have managed a social media account in the past six months

2.2.1 Universal McCann

2.2.2 Proportion of individuals using the InternetMeasures the actual number of Internet users per 100 people

2.2.2 International Telecommunication Union (ITU)

2.3 Advanced usage of Internet

This composite indicator takes into account two forms of sophisticated Internet usage: gaming and hacking activity (adjusted for population and in absolute terms)

2.3.1 Gaming revenues per capitaTotal revenues per capita are based on consumer revenues generated by companies in the global gaming industry

2.3.1 Newzoo

2.3.2 Gaming revenuesTotal revenues (in absolute terms) are based on consumer revenues generated by companies in the global gaming industry

2.3.2 Newzoo



2.3.3 Hacking activity (DDoS attack bots per 100,000 people)Measures the number of bots responsible for DDoS attacks per 100,000 population

2.3.3 Akamai: State of the Internet; EIU population estimates

2.3.4 Hacking activity (DDoS attack bots)Measures the number of bots (in absolute terms) responsible for DDoS attacks

2.3.4 Akamai: State of the Internet; EIU population estimates

2.4 B2C e-commerce This indicator assesses the level of business-to-consumer e-commerce based on two sub-indicators

2.4.1 B2C e-commerce per capitaMeasures business-to-consumer (B2C) e-commerce turnover per capita

2.4 Ecommerce Foundation; Ekos Global; Visa; national statistics agencies; US Internal Revenue Service (IRS) yearly currency exchange rates; World Bank

2.4.2 B2C e-commerce Measures absolute business to consumer (B2C) e-commerce turnover

2.5 Locally hosted web content generation

2.5.1 Locally hosted web content generationA measurement of the volume of web content generated per country, this sub-indicator measures the level of generic and country-code top-level domains

2.5.1 ZookNIC; EIU

III. Labour marketThis category describes the supply of technologically proficient workers in relation to the available work. The overall labour market is important because the international demand for technologically proficient workers is growing faster than the supply of qualified workers. Countries with more promising labour markets will be in a better position to leverage technology in the workforce.

3.1 Brain drain: highly-skilled emigration rate

3.1.1 Brain drain: highly-skilled emigration rateCaptures the emigration rate (%) of the highly skilled population, defined as the proportion of the population with at least a tertiary education

3.1.1 Organisation for Economic Co-operation and Development (OECD)

3.2 EIU business environment/labour market regulation

3.2.1 EIU business environment/labour market regulationThis is a composite indicator based on forecast qualitative scores (2015-19) for five labour sub-indicators from the EIU Business Environment Rankings: productivity, labour regulation, wage laws, hiring of foreign nationals, and labour flexibility

3.2.1 EIU Business Environment Rankings

3.3 Youth unemployment and skills gap

3.3.1 Youth unemployment and skills gapA proxy for skills gaps in an economy, this is an EIU custom-created qualitative indicator which characterises the youth skills gap based on youth unemployment trends relative to macroeconomic growth on a normalized 0-12 scale

3.3.1 EIU calculations based on data from the World Bank

3.4 Old-age dependency ratio 3.4.1 Old-age dependency ratioCharacterises the population by the estimated ratio of people aged 65+ to the workforce (aged 15-64) in 2020

3.4.1 EIU calculations based on data from the US Census Bureau International database

3.5 Freelancers 3.5.1 FreelancersWith freelancers becoming an increasingly important segment of the workforce, this sub-indicator qualitatively assesses the prevalence of freelancers in IT, design, manufacturing and engineering

3.5.1 EIU calculations

3.6 Economic participation gender gap

3.6.1 Economic participation gender gapA sub-index of the World Economic Forum’s Gender Gap Report, the economic participation gender gap is a composite indicator that evaluates an average of the labour force participation gap, the remuneration gap and the advancement gap

3.6.1 The World Economic Forum Gender Gap Report 2014




IV. Education and technology skills This category describes the extent to which the current and future workforces have mastered the skills necessary to work in a technology-enabled environment. For the purposes of this index, it is important to assess not only the proportion of skilled labour but also the quality of the skills attained.


4.1 General state of education A composite indicator measuring the general education levels of 1) the adult population as well as 2) the future workforce

4.1.1 Secondary gross enrolment ratioMeasures the total enrolment in secondary education, regardless of age, as a proportion of the official secondary education-aged population

4.1.1 UNESCO Institute for Statistics; Ministry of Education of the Republic of China (Taiwan)

4.1.2 Tertiary gross enrolment ratioMeasures the total enrolment in tertiary education, regardless of age, as a proportion of the total population in the five-year age group preceding secondary-school age

4.1.2 UNESCO Institute for Statistics; national statistics agencies

4.1.3 Mean years of schoolingCaptures the average number of years of education received by people aged 25 and over

4.1.3 Barro-Lee (2013); UNESCO Institute for Statistics (2013b); Human Development Report Office (HDRO) estimates based on data on educational attainment from UNESCO Institute for Statistics (2013b) and on methodology from Barro-Lee (2013)

4.2 Tertiary enrolment in STEM and arts

A composite indicator measuring the proportion of university students specialising in fields of study considered important for acquiring the necessary technology and creative skills

4.2.1 Enrolment in STEM per 100,000Measures student enrolment at the tertiary level in science, technology, engineering and mathematics per 100,000 people

4.2.1 UNESCO Institute for Statistics; CIA World Factbook; national statistics agencies; EIU calculations

4.2.2 Enrolment in arts per 100,000Measures student enrolment at the tertiary level in arts and humanities per 100,000 people

4.2.2 UNESCO Institute for Statistics; CIA World Factbook; national statistics agencies; EIU calculations

4.3 Maths and numeracy skills among students

4.3.1 Maths and numeracy skills among studentsA proxy for the quality of science, technology, engineering and mathematics (STEM) education and the acquisition of problem-solving skills, this is an EIU custom indicator assessing the mathematics knowledge of students (primarily 15-year-olds or 8th-graders). Scores are calculated using international studies that produce comparable data across countries.

4.3.1 EIU calculations based on data from 2012 OECD PISA scores in mathematics for 15-year-olds; TIMSS scores in mathematics for 8th-graders (2011, 2007); numeracy skills at the primary school level compiled by the Education for All WIDE database

4.4 Programming talent 4.4.1 Programming talentAn assessment of where the “highest-skilled” coders are, this quantitative indicator measures the number of top programmers on Stack Overflow per country, specifically coders with crowdsourced “reputations” greater than 5,000 points

4.4.1 Stack Overflow

4.5 Journal citations 4.5.1 Journal impact factor: the h-indexA proxy for the quality of research being produced, this sub-indicator measures the number of articles (h) published by countries that have received at least h-citations

4.5.1 SCImago Journal & Country Rank based on information from the Scopus® database (Elsevier B.V.)

4.6 Internet prevalence in schools

4.6.1 Internet prevalence in schoolsAn EIU-calculated indicator capturing early exposure to technology, specifically a scoring of 0-4 indicating the proportion of all primary and secondary schools (public and private) with any degree of access to the Internet, where:0=No Internet prevalence4=Full Internet prevalence

4.6.1 UNESCO Institute for Statistics (UIS) World Summit on Information Society Review 2014; national statistics; EIU where indicated



V. Government environmentThis category describes the extent to which the regulatory environment and laws in each country facilitate ICT visibility and access and measures how well the government has integrated technology in its operations.


5.1 Government commitment to ICT

5.1.1 Government commitment to ICTAn EIU custom indicator qualitatively assessing the government commitment and investment in ICT on a scale of 1-8, where 1=No commitment and 8=Full dedication and commitment to ICT. It appraises the presence of e-waste regulation, a Universal Service Fund, a separate ICT regulatory authority, and a national broadband plan in addition to government spending on ICT as a percentage of GDP

5.1.1 EIU calculations based on data from the International Telecommunication Union (ITU); World Information Technology And Services Alliance (WITSA) Digital Planet 2010; national statistics agencies

5.2 Government effectiveness risk

5.2.1 Government effectiveness riskAn EIU composite indicator measuring general government effectiveness based on the following sub-indicators from the EIU Risk Briefing report: policy formation, quality of bureaucracy, excessive bureaucracy, vested interests, corruption, and human rights

5.2.1 EIU Risk Briefing

5.3 Online services index 5.3.1 Online services indexMeasures the degree to which governments provide online services to citizens. The sub-index includes four stages of development: emerging information services, enhanced information services, transactional services, and connected services. This indicator is based on a sub-index from the UN E-Government Development Index

5.3.1 UN E-Government Development Index

5.4 Cyberlaw adoption 5.4.1 Cyberlaw adoptionA qualitative score assessing the status of national e-commerce legislation adoption where:1=No legislation5=Draft legislation10=LegislationThe final score is an average of scores across four laws: consumer protection, data protection, e-transaction and cybercrime legislation

5.4.1 UNCTAD; national government and statistical offices

5.5 IP environment 5.5.1 IP environmentAn EIU custom indicator evaluating the protection of intellectual property (IP) by governments and their encouragement of innovation. This indicator is calculated across three dimensions: resident IP activity intensity (patents, trademarks and industrial design), membership and ratification of international IP treaties, and IP infringement

5.5.1 EIU calculations based on the EIU Risk Briefing report and data from the World Intellectual Property Organisation (WIPO); BSA (The Software Alliance); OECD; World Bank; national statistical offices

VI. Business environmentThis category describes how conducive a market is for technology adoption. It is important to capture not only the overall macroeconomic and political environment, but more generally the penetration of ICT in businesses.

6.1 EIU overall business environment

6.1.1 EIU overall business environmentThe EIU’s Business Environment Rankings quantify the attractiveness of the business environment. The overall score is derived as an unweighted average of ten component category scores. The ratings run from 1 to 10, where:1=Worst environment10=Best environment

6.1.1 EIU Business Environment Rankings



6.2 Firm-level absorption of technology

6.2.1 Firm-level absorption of technologyBased on a survey question from the World Economic Forum Executive Opinion Survey of 2014, this measures executives’ perceptions of the extent to which businesses in their country adopt new technologies

6.2.1 World Economic Forum Executive Opinion Survey 2014 (Global Competitiveness Report, values also reported in 2015 Global Information Technology Report)

6.3 ICT goods trade 6.3.1 ICT goods as a percentage of total tradeMeasures ICT exports as a percentage of total merchandise trade

6.3.1 UNCTAD

6.4 Entrepreneurship A composite indicator capturing the prevalence and perceptions of entrepreneurship in an economy:

6.4.1 Number of start-ups on CrunchBase per 100,000Measures the number of registered start-ups adjusted by population on the online platform

6.4.1 CrunchBase; United Nations, Department of Economic and Social Affairs, Population Division (2013); World Population Prospects: The 2012 Revision

6.4.2 Number of start-ups on CrunchBaseMeasures the number of registered start-ups (in absolute terms) on the online platform

6.4.2 CrunchBase

6.4.3 Percentage of population involved in opportunity-driven entrepreneurial activity Measures the proportion of population aged 18-64 who are either nascent entrepreneurs or owner-managers of a new business and claim to be driven by opportunity as opposed to necessity

6.4.3 GEM Consortium

6.4.4 Perceptions of entrepreneurial environmentCaptures society perceptions towards entrepreneurship as a proxy for a favourable entrepreneurial environment. This is a composite indicator measuring whether the working-age population of an economy see opportunities to start a firm where they live and whether they see entrepreneurship as a desirable career choice

6.4.4 GEM Consortium

6.5 Creative goods and services exports

6.5.1 Creative goods and services exportsA proxy for the creative class and its contribution to economic activity

6.5.1 UNCTAD; World Bank; EIU calculations


3. Methodologya. GeneralThe Workforce of the Future Index is comprised of a mixture of 32 quantitative and qualitative indicators. These indicators are sourced from:

l Government statistics agencies, publications and reports

l Academic publications and reportsl Websites of governmental authorities,

international organisations and non-governmental organisations, including the UN’s International Telecommunication Union, the World Economic Forum, the World Bank, UNESCO and UNCTAD

l Primary data collected by technology

companies, including Cisco and Akamail Economist Intelligence Unit proprietary

databases and reports, including the EIU Business Environment Rankings and EIU Risk Briefing.

The EIU created three custom sub-indicators:

l 3.3.1 Youth unemployment and skills gapl 4.3.1 Maths and numeracy skills among students l 5.5.1 Intellectual property environment.

To score these indicators, the research team gathered data from the following sources:

l International and regional survey-based skills assessment tests, including the PISA, TIMSS,



SACMEQ and PASEC testsl EIU Risk Briefingl Government statistics agencies, publications

and reportsl Websites of governmental authorities,

international organisations and non-governmental organisations.

Specific sources by indicator are available on request from the EIU.

b. Indicator choiceThe EIU convened a panel of eight experts to help build the indicator framework of the Workforce of the Future Index. The panel met in New York City to provide guidance on the indicator framework. These experts were drawn from a broad range of institutions and companies, including the World Bank, Mercer Workforce Sciences Institute, USC Marshall School of Business, Boston University School of Law, New York University Stern School of Business, Marsh & McLennan Companies and LeaderAmp.

The expert panel played a key role in:

l Defining what constitutes a workforce ready for technologically enabled workplaces

l Refining the overall structure of the indexl Identifying the elements required to assess the

readiness of a workforce.

The EIU collated the experts’ input and conducted additional research to finalise the indicator selection. This process was a collaborative effort between the EIU, AT&T and the experts.

c. Country coverageThe Workforce of the Future Index is composed of 56 countries. Several parameters were taken into account to determine the country list, including:

l Contribution to world GDPl Population sizel Level of ICT infrastructure developmentl Level of economic developmentl Regional representation.

Africa & Middle East

Algeria

Egypt

Israel

Nigeria

Qatar

Saudi Arabia

South Africa

Tunisia

Turkey

United Arab Emirates

Central & Eastern Europe

Croatia

Czech Republic

Hungary

Poland

Romania

Russia

Slovakia

East Asia & Pacific

Australia

China

Hong Kong

Japan

Malaysia

New Zealand

Philippines

Singapore

South Korea

Taiwan

Thailand

Vietnam

Latin America

Argentina

Brazil

Chile

Colombia

Mexico

Peru

Costa Rica

North America

Canada

United States

South Asia

India

Indonesia

Pakistan

Western Europe

Austria

Belgium

Denmark

Finland

France

Germany

Greece

Ireland

Italy

Netherlands

Norway

Spain

Sweden

Switzerland

United Kingdom



d. Scoring criteriaThere are 54 sub-indicators used to construct 32 indicators across six categories within the dynamic Workforce of the Future Index model. The overall scores (0-100) for countries in the index are a weighted average of the six categories as determined by the weighting profile. For more information on index weights, please refer to section f), where each indicator is scored on a scale of 0 to 100, such that 100 is associated with the most favourable technology-ready workforce and enabling technology environment.

Most of the sub-indicators in the index are quantitative and have been collected and checked for quality by the EIU for inclusion in the index. For the EIU custom indicators (3.3.1, 4.3.1 and 5.5.1), an experienced team of researchers probed the sources listed in section a) to build these indicators and provide quantitative and qualitative data points across all 56 countries. The EIU supplied a detailed set of guidance outlining the criteria and a scoring scheme for each indicator. While the criteria for data collection were rigorous, they remain subjective. Staff from the EIU thoroughly reviewed, calibrated and compared scores to ensure proper justification and consistency across all countries.

Each sub-indicator is normalised on a scale of 0 to 100, such that the higher value always associates with more favourable technology and workforce conditions. This normalisation scheme allows the calculations at the indicator and category level, where the scores also range from 0 to 100.

e. Calculating the Workforce of the Future IndexModelling the sub-indicators, indicators and categories in the Workforce of the Future Index results in overall scores of 0-100 for each country, where 100 represents the most favourable conditions for a technology-ready workforce and 0 the least favourable. A score of 100 does not suggest that a country has achieved perfect technology-readiness; likewise, a score of 0 does not mean that a country has no technology-ready

workforce. Rather, scores of 100 and 0 represent the highest or lowest possible scores, respectively, as measured by the index criteria.

For a sub-indicator where a higher score is considered to be contributing positively to a technology-ready workforce, the sub-indicator values are first normalised on the basis of the following equation:

x = (x – Min(x)) / (Max(x) – Min(x)),

where Min(x) and Max(x) are, respectively, the lowest and highest values allowed by the scoring scheme for any given sub-indicator. For a sub-indicator where a lower score is considered more positive, the equation for the normalisation is then flipped:

x = 1-[(x – Min(x)) / (Max(x) – Min(x))],

Those values are averaged to determine the value of the indicator:

Indicator score = ∑ individual sub-indicators / # sub-indicators

The indicators are classified into six categories: Technology and connectivity infrastructure (5 indicators); Technology and society (5 indicators); Labour market (6 indicators); Education and technology skills (6 indicators); Government environment (5 indicators); and Business environment (5 indicators).

There are indicators that were not normalised based on the method above since they were already normalised or based on a 0-100 scale. These indicators are the following:

l 1.2.2 Rural access to electricityl 2.1.5 Gini indexl 3.3.1 Youth unemployment and skills gapl 4.3.1 Maths and numeracy skills among studentsl 5.5.1 IP environment

The indicator and category values are then assigned weights according to their contribution to the index, which ultimately determines the overall scores and rankings in the index. Please refer to section f) for more details.



f. WeightsAssigning weights to index components is the final step of index construction, and the weighting scheme reflects different assumptions about the relative importance of certain topics. As described in the EIU preferred weights section, the findings presented in the Workforce of the Future Index are based on a preferred weighting scheme developed by the EIU. This scheme combines quantitative methods and qualitative expertise to place a higher importance on categories and indicators that are more critical for evaluating the technological proficiency of future workforces.

The EIU utilised a three-step process in developing the preferred weight scheme: (1) conducting a principle-component analysis; (2) verifying the weights with members of the expert panel; and (3) applying the EIU’s qualitative knowledge of the subject matter. Based on this comprehensive process, the EIU preferred weighting scheme assigns the following category weights:

I. Technology and connectivity infrastructure – 20%

II. Technology and society – 25%III. Labour market – 10%IV. Education and technology skills – 20%V. Government environment – 15%VI. Business environment – 10%

Principal components analysisPrincipal components analysis (PCA) was the primary quantitative method used in establishing the weights. The goal of PCA is to define quantitatively a weighting scheme for the indicators that are used to create a composite index or ranking of overall technological proficiency in the future workforce. PCA is a method for removing redundant information shared across indicators by specifying a weighting that explains the most variance in the data. PCA weights are derived through a mathematical process that takes into account the covariance between indicators and the importance of a particular element in maximising the variation in the index scores. It aims to minimise redundancy between variables and maximise the variance within the index, but does not consider indicators’ perceived importance.



Whilst every effort has been taken to verify the accuracy of this

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for reliance by any person on this white paper or any of the

information, opinions or conclusions set out in the white paper.

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