What Are The Knowledge Intensive Sectors

Knowledge intensive sectors

The OECD identifies high and medium tech manufacturing; high value added “knowledge intensive” market service industries such as finance and insurance and telecommunications; and business services. The current OECD definition also includes education and health. The Work Foundation has extended this definition in the recent Ideopolis report to capture a higher share of employment in the cultural and creative industries.

The OECD wide definition of knowledge based industries indicates that Ireland was the most knowledge based economy in the OECD, with these industries accounting for 48 per cent of GDP followed by the US, Germany, and Sweden with around 43 per cent. The knowledge based industries accounted for around 40 per cent of GDP in the UK and France. Estimates for Japan are only available on the more restricted market based industry definition, excluding health and education. However, on this basis Japan has a less knowledge based industrial structure than Germany, the US, the UK or France. (based on 2005data)

Knowledge jobs and knowledge workers

There are (at least) three ways we can work towards a definition of knowledge workers:

  1. All those who work in the top three standard occupational classifications (managers, professionals, associate professionals)
  2. All those with high levels skills, indicated by degree or equivalent qualifications
  3. All those who perform tasks that require expert thinking and complex communication skills with the assistance of computers.

Knowledge workers account for about 42 per cent of all employment in the UK in the first quarter of 2006, using the occupational definition of the top three occupational groups. This compares with 31 per cent of total employment in 1984, according to the latest projections prepared for the Sector Skills Development Agency (SSDA). The SSDA is projecting the share will grow to just over 45 per cent by 2014.

The underlying story is one of fairly stable constant structural change in the labour market decade on decade. The share of knowledge economy jobs has increased by between 4 and 5 percentage points in each decade, while the share of unskilled jobs has fallen by about 2 and 3 percentage points in each decade.

International comparisons are difficult because of differences in occupational classifications and how these are interpreted in national surveys. The best comparable data we have found to date suggests that in 2004 or latest year available between 40 and 45 per cent workers in the smaller North European economies, North America, and Australia are knowledge workers. The UK lies alongside Germany and Canada, but behind the Nordic economies, Switzerland and the Netherlands.

Knowledge workers in the UK economy 1984-2014

Occupations

1984

1994

2004

2014

Knowledge workers

31%

36%

41%

45%

Personal services; sales; admin/clerical

25%

28%

28%

28%

Skilled/semi- skilled; manual

28%

23%

19%

18%

Unskilled jobs

16%

14%

11%

9%

Note:2014 projected. Knowledge economy jobs are managerial, professional, associate professional standard occupational classifications. Personal services include care, recreational, and some hospitality jobs. Employees and self-employed.Source: Working Futures 2004-2014, table 4.1

Human skill definitions

An interesting alternative approach to using broad occupational categories is set out in a recent paper by Autor, Levy and Murname5 that divides human skills into five categories:

  1. Expert thinking: solving problems for which rule based solutions do not exist. Computers cannot substitute for human beings but can assist by making information more readily available
  2. Complex communication: interacting with other people to acquire or convey information and persuading others of the implications – examples might include some managers, teachers, sales people
  3. Routine cognitive: mental tasks closely described by rules such as routine processing application forms and claims – these jobs are often vulnerable to computerisation
  4. Routine manual: physical tasks closely described by rules, such as assembly line work and packaging. These repetitive tasks can in some circumstances also be undertaken by programmed machines
  5. Non-routine manual tasks: physical tasks hard to define by rules because they require optical and fine muscle control, including truck-driving and cleaning. Such jobs are unlikely to be assisted or replaced by computers.

The authors applied these categories to the US workforce between 1969 and 1998 and found that jobs requiring complex communication increased by nearly 14 per cent, and jobs requiring expert thinking increased by just over 8 per cent. All other jobs saw a declining share of employment over this period.

A recent large scale survey carried out by the Economist Intelligence Unit of top company executives and managers used a similar approach in identifying which skill sets would be most valuable in terms of competitive advantage in the year 2020. This adopted a five-category definition:

  • Complex knowledge based roles that are primarily outward facing and require developed communication and judgement skills
  • Complex knowledge based roles that are primarily inward-looking and require developed communication and judgement skills
  • Simple knowledge based roles that are rules-based, outward facing and do not require developed communication and judgement skills
  • Simple knowledge based roles that are rules based, inward facing and do not require developed communication and judgement skills
  • Production roles directly related to manufacturing or production processes.

Perhaps not surprisingly, 62 per cent of respondent’s said outward facing complex knowledge based roles would be most important for the organisation’s future competitive advantage, followed by 28 per cent saying inward facing knowledge based roles would be most important. The rest were cited by only 2 to 4 per cent of respondents as being important for future competitive advantage.

 This approach undoubtedly gets closerto defining knowledge jobs in terms of both cognitive complexity and the relationship to computers – in other words, what people actually do – and might be regarded as superior to simply classifying jobs by occupational title or educational qualification of the job holder. The disadvantage is that it requires either an extensive re-working of the statistics or original survey work and may not easily lend itself to direct comparisons with previous work or international comparisons.

knowledge economy

Investment in Knowledge

Investment in Knowledge

The OECD has produced a composite indicator of “Investment in Knowledge” which made of:

  1. Investment in R&D
  2. Investment in higher education
  3. Investment in IT software

By this measure, we can identify three groups of economies:

  • High knowledge investment economies, they are investing around 6 per cent of GDP
  • Middle knowledge investment economies, they are investing between 3 and 4 per cent of GDP
  • Low investment economies, they are investing between 2 and 3 per cent. of GDP.

The story of the past decade has been for most of the high investment economies to pull away from the rest. Most high investment economies stepped up their knowledge investment by between 1 and 2 percentage points of GDP while the middle and low investment economies showed relatively little change.

Globalisation and the knowledge economy

The development of the knowledge economy and the globalisation has been closely related. Global firms have built integrated international production chains, with their R&D facilities kept in the US and Europe creating new products that are built in assembly plants in China and india, then shipped back to the West for added value in “knowledge” areas such as design and marketing and providing associated services in Europe and the US.

The growth of the knowledge economy is seen as part of the strategic response to the threat to the jobs in the OECD from the low wage economies imports such as China and India.

As a response the low wage economies such as China and India are increasing their Investment in Knowledge heavily in, defined both as the share of GDP devoted to R&D and increasing the numbers of home grown graduates.

The implication is that through these investments in knowledge the lower wage economies will capture a much larger share of the “knowledge based” segments of the international production chain in the future unless the Western economies become even more competitive in these areas.

How to define and measure the knowledge economyInvestment in Knowledge

Without measurable definitions, the knowledge economy will remain a vague concept. The impact of the knowledge economy on industrial organisation, institutional structures, employment and society would remain more a matter of assertion and intuition rather than demonstrable proof based on hard facts. We can summarise the key features of knowledge economy and knowledge economy organisations as follows:

  • The knowledge economy represents a “soft discontinuity” from the past – it is not a “new” economy operating to a new set of economic laws
  • The knowledge economy is present in all sectors of the economy, not just the knowledge intensive industries
  • The knowledge economy has a high and growing intensity of ICT usage by well- educated knowledge workers
  • A growing share of GDP devoted to knowledge intangibles compared with physical capital
  • The knowledge economy consists of innovating organisations using new technologies to introduce process, organisational and presentational innovation
  • Knowledge economy organisations reorganise work to allow them to handle, store and share information through knowledge management practices.

Three ways in which the knowledge economy might be defined more precisely in ways that are measurable and therefore, in principle, testable against hard data:

  1. Industry sector definitions of knowledge intensive industries and services
  2. Occupational based definitions of knowledge workers
  3. Innovation related definitions of the share of innovating firms.

Knowledge Index

Knowledge Index

The Knowledge Index or KI is an economic indicator prepared by the World Bank Institute to measure a country s ability to generate, adopt and diffuse knowledge. Methodologically, the KI is the simple average of the normalized performance scores of a country or region on the key variables in three Knowledge Economy pillars – education and human resources, the innovation system and information and communication technology (ICT)

Knowledge Economic Index

The Knowledge Economy Index (KEI) takes into account whether the environment is conducive for knowledge to be used effectively for economic development. It is an aggregate index that represents the overall level of development of a country or region towards the Knowledge Economy. The KEI is calculated based on the average of the normalized performance scores of a country or region on all 4 pillars related to the knowledge economy – economic incentive and institutional regime, education and human resources, the innovation system and ICT.

The 4 pillars of the Knowledge Economy framework

  • An economic and institutional regime to provide incentives for the efficient use of existing and new knowledge and the flourishing of entrepreneurship;
  • An educated and skilled population to create, share, and use knowledge well;
  • An efficient innovation system of firms, research centers, universities, consultants and other organizations to tap into the growing stock of global knowledge, assimilate and adapt it to local needs, and create new technology;
  • Information and communication technology to facilitate the effective creation, dissemination, and processing of information.

• Information and communication technology to facilitate the effective creation, dissemination, and processing of information.

 

KEI and KI indexes by country

Country KEI   KI Economic Incentive Regime Innovation Education ICT 2008 Rank
Denmark 9.58 9.55 9.66 9.57 9.80 9.28 1
Sweden 9.52 9.63 9.18 9.79 9.40 9.69 2
Finland 9.37 9.33 9.47 9.66 9.78 8.56 3
Netherlands 9.32 9.36 9.18 9.48 9.26 9.36 4
Norway 9.27 9.27 9.25 9.06 9.60 9.16 5
Canada 9.21 9.14 9.42 9.43 9.26 8.74 6
Switzerland 9.15 9.03 9.50 9.89 7.69 9.52 7
United Kingdom 9.09 9.03 9.28 9.18 8.54 9.38 8
United States 9.08 9.05 9.16 9.45 8.77 8.93 9
Australia 9.05 9.17 8.66 8.72 9.64 9.16 10
Ireland 8.92 8.82 9.23 9.04 9.08 8.33 11
Austria 8.89 8.76 9.30 8.90 8.53 8.85 12
Iceland 8.88 8.87 8.92 7.98 9.44 9.18 13
Germany 8.87 8.83 8.99 9.00 8.46 9.04 14
New Zealand 8.87 9.00 8.48 8.65 9.79 8.56 15
Belgium 8.73 8.70 8.82 8.96 9.14 8.02 16
Taiwan 8.69 8.80 8.35 9.24 7.91 9.26 17
Luxembourg 8.65 8.40 9.42 8.91 6.66 9.62 18
Japan 8.56 8.84 7.71 9.15 8.71 8.66 19
France 8.47 8.69 7.82 8.61 9.08 8.38 20
Estonia 8.34 8.22 8.68 7.49 8.27 8.90 21
Slovenia 8.25 8.29 8.11 8.31 8.24 8.33 22
Spain 8.24 8.13 8.58 8.14 8.21 8.04 23
Singapore 8.24 7.75 9.71 9.56 5.19 8.50 24
Israel 8.22 8.24 8.16 9.34 6.72 8.64 25
Hong Kong, China 8.20 7.73 9.60 8.64 5.30 9.26 26
Italy 7.86 8.19 6.84 8.04 7.86 8.68 27
Hungary 7.85 7.67 8.39 8.14 7.62 7.25 28
Czech Republic 7.83 7.70 8.23 7.60 8.11 7.39 29
Lithuania 7.68 7.60 7.94 6.59 8.36 7.84 30
South Korea 7.68 8.38 5.57 8.47 7.97 8.71 31
Latvia 7.64 7.51 8.04 6.40 8.41 7.73 32
Cyprus 7.55 7.47 7.77 7.65 6.45 8.32 33
Portugal 7.52 7.22 8.44 7.43 6.83 7.39 34
Greece 7.38 7.48 7.08 7.63 8.20 6.62 35
Poland 7.38 7.37 7.39 6.92 7.94 7.25 36
Slovakia 7.33 7.12 7.99 6.86 6.98 7.51 37
Barbados 7.25 7.78 5.66 7.51 8.40 7.44 38
Croatia 7.19 7.19 7.16 7.54 6.44 7.61 39
Chile 6.92 6.53 8.11 6.81 6.31 6.46 40
Bulgaria 6.80 6.73 7.01 6.43 7.42 6.33 41
United Arab Emirates 6.66 6.57 6.95 6.74 4.78 8.18 42
Romania 6.37 6.20 6.87 5.66 6.30 6.63 43
Uruguay 6.35 6.31 6.49 5.26 7.18 6.48 44
Qatar 6.15 6.20 5.99 5.77 5.29 7.56 45
Dominica 6.07 5.61 7.46 3.76 6.24 6.82 46
Costa Rica 6.06 5.85 6.70 6.24 5.01 6.30 47
Malaysia 6.06 6.02 6.18 6.83 4.14 7.08 48
Russian Federation 5.40 6.69 1.55 6.89 7.09 6.08 49
Bahrain 6.02 5.75 6.84 4.20 5.82 7.22 50
Kuwait 6.01 5.68 7.01 5.05 4.87 7.13 51
Ukraine 5.80 6.38 4.06 5.77 7.91 5.45 52
Argentina 5.49 6.44 2.63 6.85 6.49 5.98 53
Trinidad and Tobago 5.64 5.54 5.95 6.02 4.34 6.27 54
Brazil 5.57 6.00 4.30 6.07 5.84 6.08 55
Turkey 5.61 5.14 7.02 5.67 4.38 5.38 56
South Africa 5.55 5.47 5.81 6.92 4.51 4.98 57
Jordan 5.53 5.46 5.77 5.66 5.49 5.21 58
Armenia 5.51 5.44 5.71 6.17 6.32 3.84 59
Mexico 5.45 5.48 5.38 5.82 4.85 5.77 60
Thailand 5.44 5.41 5.51 5.98 5.27 5.00 61
Oman 5.37 4.72 7.32 4.95 4.30 4.90 62
Macedonia 5.33 5.23 5.61 4.76 4.87 6.06 63
Mauritius 5.18 4.58 6.95 3.70 4.09 5.96 64
Saudi Arabia 5.15 5.07 5.39 4.04 4.87 6.29 65
Jamaica 5.04 5.40 3.99 5.36 4.10 6.74 66
Moldova 5.04 5.32 4.19 4.39 6.40 5.17 67
Kazakhstan 5.01 5.08 4.82 3.77 7.21 4.25 68
Belarus 4.93 6.39 0.55 5.54 8.00 5.63 69
Lebanon 4.86 4.91 4.70 4.69 4.76 5.27 70
Tunisia 4.73 4.56 5.26 4.58 4.10 5.00 71
Panama 4.69 4.45 5.39 5.45 4.86 3.04 72
Georgia 4.69 5.07 3.54 5.38 5.97 3.85 73
Peru 4.64 4.86 3.98 3.88 5.57 5.12 74
Mongolia 4.50 4.28 5.18 2.06 6.31 4.46 75
Colombia 4.42 4.62 3.83 4.26 4.79 4.80 76
China 4.35 4.46 4.01 5.12 4.11 4.16 77
Guyana 4.31 4.97 2.33 4.47 5.80 4.64 78
Philippines 4.25 4.02 4.95 3.63 4.76 3.66 79
Venezuela 4.23 5.47 0.51 5.73 5.27 5.41 80
Namibia 4.19 3.20 7.14 3.30 2.57 3.74 81
Sri Lanka 4.16 4.07 4.44 4.44 4.91 2.85 82
Albania 4.04 4.08 3.91 3.10 4.94 4.20 83
Egypt 4.03 4.19 3.57 4.55 4.35 3.66 84
Botswana 3.96 3.50 5.34 4.34 2.58 3.59 85
Dominican Republic 3.92 3.81 4.24 2.91 4.11 4.42 86
El Salvador 3.91 3.65 4.70 3.19 3.26 4.50 87
Azerbaijan 3.81 3.93 3.42 3.05 5.03 3.73 88
Kyrgyzstan 3.74 3.90 3.25 2.70 6.25 2.75 89
Paraguay 3.62 3.87 2.87 3.47 4.20 3.93 90
Ecuador 3.46 4.08 1.58 3.55 3.77 4.93 91
Morocco 3.45 3.33 3.80 3.67 2.00 4.32 92
Bolivia 3.42 3.63 2.78 3.05 4.76 3.09 93
Iran 3.39 4.13 1.18 3.02 3.89 5.48 94
Uzbekistan 3.28 4.03 1.03 3.51 6.17 2.40 95
Algeria 3.25 3.50 2.53 3.48 3.64 3.37 96
Cape Verde 3.24 3.05 3.81 2.25 2.96 3.96 97
Indonesia 3.23 3.19 3.36 3.32 3.42 2.82 98
Honduras 3.21 3.18 3.30 3.30 3.17 3.06 99
India 3.12 2.94 3.67 3.97 2.26 2.59 100
Guatemala 3.11 2.88 3.78 2.47 2.21 3.97 101
Vietnam 3.02 3.08 2.85 2.83 3.32 3.08 102
Swaziland 2.93 3.05 2.56 4.55 1.73 2.88 103
Syrian Arab Republic 2.90 3.34 1.55 3.44 2.91 3.68 104
Nicaragua 2.87 2.64 3.57 1.99 2.93 3.02 105
Kenya 2.82 2.65 3.31 3.87 1.49 2.60 106
Tajikistan 2.79 2.93 2.37 2.33 5.34 1.10 107
Senegal 2.63 2.15 4.07 2.77 0.92 2.75 108
Zimbabwe 2.51 3.25 0.29 4.09 2.38 3.29 109
Ghana 2.50 2.00 3.97 2.08 1.80 2.13 110
Uganda 2.46 1.93 4.04 2.72 1.16 1.92 111
Madagascar 2.37 1.51 4.93 2.54 0.76 1.25 112
Mauritania 2.35 1.83 3.89 1.75 0.94 2.80 113
Tanzania 2.28 1.72 3.98 2.39 1.05 1.70 114
Pakistan 2.24 2.18 2.43 2.75 1.07 2.72 115
Lesotho 2.15 1.99 2.65 2.70 1.73 1.53 116
Benin 2.10 1.80 3.00 2.33 1.14 1.93 117
Nigeria 2.04 2.33 1.16 2.72 1.87 2.41 118
Yemen 1.80 1.83 1.72 1.68 1.83 1.99 119
Mali 1.78 1.18 3.58 1.69 0.66 1.19 120
Mozambique 1.71 1.20 3.24 1.86 0.33 1.41 121
Angola 1.70 1.67 1.76 2.44 0.88 1.70 122
Cameroon 1.69 1.85 1.20 2.49 1.36 1.70 123
Burkina Faso 1.64 1.11 3.24 2.15 0.26 0.93 124
Nepal 1.61 1.46 2.06 2.04 1.50 0.84 125
Malawi 1.55 1.17 2.71 2.11 0.87 0.53 126
Laos 1.53 1.68 1.08 1.43 2.01 1.59 127
Bangladesh 1.49 1.63 1.10 1.71 1.52 1.66 128
Myanmar 1.48 1.52 1.35 1.17 2.58 0.82 129
Rwanda 1.34 0.85 2.80 1.47 0.35 0.74 130
Ethiopia 1.18 0.93 1.95 1.57 0.73 0.48 131
Djibouti 1.15 1.14 1.19 1.29 0.49 1.63 132
Eritrea 1.07 1.20 0.68 1.56 0.81 1.22 133
Sierra Leone 0.91 0.92 0.87 1.70 0.67 0.39 134

 

Dataset list