Developing skills foresights, scenarios and forecasts : guide to anticipating and matching skills and jobs. Volume 2.
Date: 2016-11-30
Institutions: Cedefop, European Training Foundation
Subjects: EU employment policy, apprenticeship, continuing vocational training, labour market, professional qualifications, provision of services, social integration, social mobility, vocational training

DEVELOPING SKILLS 
FORESIGHTS, SCENARIOS 
AND FORECASTS

GUIDE TO ANTICIPATING AND MATCHING SKILLS AND JOBS VOLUME 2

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Printed in Italy

Compendium on  
Anticipation and  
Matching of Skills

DEVELOPING SKILLS 
FORESIGHTS, SCENARIOS 
AND FORECASTS

GUIDE TO ANTICIPATING AND MATCHING SKILLS AND JOBS VOLUME 2

Part A: Martin Bakule, Veˇra Czesaná and Veˇra Havlícˇková 
Part B: Ben Kriechel, Tomáš Rašovec and Rob Wilson

FOREWORD

In a context of dynamic and complex labour markets, gathering 

intelligence on current and future skill needs can support 
better matching of training and jobs, which is of paramount 
importance for every country in the world. In recent years, 
better understanding of labour market needs and skills 
matching have featured high on the policy agenda of many 
countries, driven by both rapid technological advances and 
global competition. Skills matching can also help reduce 
unemployment, particularly among young people. It helps 
build a better life for individuals by improving employability, 
social mobility and inclusion.

The European Union (EU) places great emphasis on skills 
anticipation and better matching. The Europe 2020 strategy and, 
in particular, the Agenda for new skills and jobs, recognise that 
anticipation and matching approaches and methods can help 
develop a skilled workforce with the right mix of skills in 
response to labour market needs, in a way that promotes job 
quality and lifelong learning. The EU Skills Panorama, launched 
in 2012, supports the effort to provide better data and 
intelligence on skill needs in the labour market.

The tripartite representation of International Labour Organization 
(ILO) Member States agreed that countries that have succeeded 
in linking skills to gains in productivity, employment and 
development have targeted skills development policy towards 
three main objectives:

•  matching supply to current demand for skills;

•  helping workers and enterprises adjust to change;

•  building and sustaining competencies for future 

labour market needs.

Skills matching is a complex and dynamic process 
involving multiple stakeholders making multiple decisions 
at different times: individuals and their families, as they 
make decisions regarding their own education and 
training; education, training and labour market policy 
makers, as they decide on the configuration of education 
and training systems, employment policies and 
investments; training institutions, as they make decisions 
on the type and content of the training courses to be 
delivered; and employers, as they take decisions on how 
to train workers and utilise skills.

Jobs are changing rapidly and individuals are also 
changing their skill sets, either through education and 

training or through their work and life experience. 
Education and training systems, in particular, have 
a key role to play in ensuring that opportunities are 
provided for all individuals to develop their skills 
continually in a lifelong learning perspective, enabling 
them to adapt to rapidly changing labour market 
requirements and conditions.

Given the complexity and dynamics of the process, 
perfect matching between skills demand and supply 
is neither feasible (especially in rapidly changing labour 
markets and economies) nor necessary, given the fact 
that many people can do many different jobs and many 
jobs can be done by people with different skill sets. 
However, it is important for policy makers to be aware 
of the importance of reducing the risk of creating large 
skills gaps that undermine the employability of 
individuals and impede the productivity of enterprises 
and the growth of economies.

5

Developing skills foresights, scenarios and forecastsInternational experience suggests that a 
comprehensive labour market information system is 
the backbone of any education and employment 
strategy, but no single methodology can generate 
sufficient knowledge of labour markets to avoid or 
minimise skills mismatch. The right mix and 
complementarity of different methods is essential for a 
reliable and comprehensive overview of skills demand 
and matching.

For developing and transition countries, skills matching 
and anticipation is becoming an even more complex 
task given their particular socio-economic conditions, 
weak institutions, capacities and governance systems. 
Many developing countries have limited labour market 
information and more effort and investment is needed 
to build robust information systems. At the same time, 
even limited evidence can be better, and more 
efficiently used, with proper methodological tools and 
analyses.

To respond to these challenges, the European Training 
Foundation (ETF), the European Centre for the 
Development of Vocational Training (Cedefop) and the 
International Labour Office have joined forces and 
combined expertise and geographic coverage to 
develop a compendium of methodological guides on 
anticipation and matching of skills supply and demand:

•  Volume 1: how to use labour market information.

•  Volume 2: how to develop skills foresights, 

scenarios and skills forecasts.

•  Volume 3: what works at sector level.

•  Volume 4: what is the role of employment service 

providers.

•  Volume 5: how to develop and run an establishment 

skills survey.

•  Volume 6: how to carry out tracer studies.

tomorrow. They target professionals, policy makers, 
research commissioners, social partners and experts 
who need an overview of how different anticipation 
and matching methodologies can generate reliable 
labour market information and how information and 
evidence can be analysed and used for the 
development of policy interventions or adjustments in 
education and employment strategies.

The compendium brings together state-of-the-art 
international good practice and experience 
worldwide. The most common approaches used for 
skills matching and anticipation in different economic 
and country contexts are reviewed, and their 
potential and methodological shortcomings for 
generating reliable data and information are 
examined. They serve as reference material for 
readers to explain the scope, added value and 
limitations of diverse methodologies. The guides also 
provide insight into how the results of different 
methodologies can be analysed to provide 
recommendations and policy formulations.

Any feedback from readers and users of the guides is 
very welcome, particularly regarding how the next 
editions could be improved or made relevant to their 
circumstances and policy dilemmas, how they are 
used in different countries and contexts, including 
especially in bringing stakeholders together, and which 
topics could be added in the future to complement the 
current compendium.

Chris Evans-Klock, 
Chief of the Skills and Employability Branch, 
Employment Policy Department 
ILO – International Labour Office, Geneva

Madlen Serban, 
Director 
ETF – European Training Foundation

The six guides complement each other. They include 
both qualitative and quantitative approaches, and 
advocate strong social dialogue and institutions 
conducive to better understanding the skills needs of 

Joachim James Calleja, 
Director 
Cedefop – European Centre for the Development of 
Vocational Training

6

Guide to anticipating and matching skills and jobsACKNOWLEDGEMENTS

The authors would like to thank Hana Ríhová, Zdenˇka Šímová, 

and Marta Salavová for their inputs to Part A of the volume; 
Pavel Luksha, Luiz A. Cruz Caruso, Marc Bovenschulte, Bernd 
Dworschak, Samuli Leveälahti, Tomoaki Wada, Gyu-Hee Hwang, 
Maria João Rodrigues, Anastasia Fetsi, Hajnalka Tarjani, Mike 
Campbell, and Rebecca Chesney for making available background 
materials for case studies, and Tatiana Cyro Costa and Erasmo 
Soto for their inputs to the initial literature review for Part A.

Our gratitude goes to all experts who participated in the 
development of Cedefop’s project Forecasting skills supply and 
demand in Europe and its methodology, which was used in Part B 
of the volume.

Many thanks to ILO colleague Olga Strietska-Ilina for technical 
editing, and Christine Evans-Klock, Fernando Vargas, and Theo 
Sparreboom for their useful comments on the volume. We would 
like to acknowledge all those experts who participated in the 
validation workshop for their recommendations.

We are grateful for the energy, steering of the publication and 
support received from Cedefop colleagues Pascaline Descy, Alena 
Zukersteinova, Vladimir Kvetan and Roula Panagiotou, ILO 
colleague Olga Strietska-Ilina, and ETF colleagues Anastasia Fetsi, 
Timo Kuusela and Mara Arno.

7

Developing skills foresights, scenarios and forecastsTABLE OF CONTENTS

FOREWORD  

ACKNOWLEDGEMENTS  

LIST OF FIGURES, TABLES AND BOXES  

EXECUTIVE SUMMARY  

Purpose of skills foresighting and forecasting  

Approaches: foresighting versus forecasting  

Implementation  

INTRODUCTION  

Foresights and skills anticipation: Part A  

Skills forecasts: Part B  

Foresights versus quantitative skills forecasts  

Objectives  

The reader  

PART A: FORESIGHT 

1.  FORESIGHT AND SKILLS  

1.1.  What is a foresight?  

1.2.  Skills anticipation in foresight activities  

1.3.  Foresight story  

1.3.1.  Bellandia’s foresight programme   

2.  COMPARATIVE REVIEW OF FORESIGHT AND SCENARIO METHODS   

2.1.  Categories of foresight methods  

2.1.1.  Exploratory methods  

2.1.2.  Normative methods  

2.1.3.  Other categories  

2.2.  Foresight methods  

2.2.1.  Supplementary methods  

2.2.2.  Exploratory methods  

2.2.3.  Normative methods  

2.3.  Conclusion: how to choose a method, or methods  

3.  KEY STEPS IN IMPLEMENTING FORESIGHT PROGRAMMES  

3.1.  Define foresight area to be considered  

3.2. 

 Clarify the purpose of the foresight exercise  

3.3. 

 Clarify the key programme design elements  

3.4. 

 Clarify key questions and the way to find answers  

3.5.  Manage the foresight exercise  

3.6.  Ensuring the use of results   

4.  ADAPTING FORESIGHTING TO LOCAL NEEDS  

4.1.  Key context factors  

4.1.1.  Country size  

4.1.2.  Economic and social context  

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Guide to anticipating and matching skills and jobs4.1.3.  Political stability and culture  

4.1.4. 

 Openness and vulnerability to external changes  

4.1.5.  Cultural context  

4.1.6. 

Institutional background   

4.1.7.  Resources  

4.2.  Success factors   

4.2.1. 

 Setting reasonable goals and scope of activities   

4.2.2.  Adequate institutional framework   

4.2.3.  Engaged stakeholders  

4.2.4.  Availability of resources   

4.2.5.  Choice of methods  

4.2.6.  Effective dissemination of results  

4.3.  Building foresight culture  

4.4. 

 Questions on preparation and implementation of a foresight exercise  

REFERENCES AND RESOURCES ON PART A  

PART B: SKILLS FORECASTS 

5. 

INTRODUCTION TO SKILLS FORECASTS  

5.1.  Skills forecasting: for whom?  

5.2.  Duration of forecast  

5.3. 

 A brief history of labour market forecasting  

5.4. 

 Best practices: when to use quantitative skills forecasts  

5.4.1.  Limitations of quantitative models  

6.  GENERAL APPROACHES IN SKILLS FORECASTING  

6.1.  Quantitative modelling  

6.2.  Qualitative methods  

6.3. 

 Combining quantitative and qualitative methods  

7.  DATA  

7.1.  Key data  

7.2.  Data collection  

7.2.1.  Classifications  

8.  MODELLING APPROACHES  

8.1.  Macro models   

8.1.1. 

 The macro model for the Cedefop skills project: E3ME  

8.1.2. 

 A model suitable for transition countries: Hermin  

8.2.  Other economic modelling  

8.3.  Simpler models of employment change   

8.4. 

 Qualitative inputs into quantitative forecasts and expert evaluation (51)  

9. COMPONENTS OF SKILLS FORECASTING  

9.1.  Supply  

9.2.  Employment change  

9.3.  Replacement needs  

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Developing skills foresights, scenarios and forecasts10.  SPECIFIC ISSUES IN SKILLS MODELLING  

10.1.  Technological change  

10.2.  Skills mismatch  

10.3. 

Imbalances  

10.4. 

Indicators   

10.5.  Regional forecasting  

11.  EXAMPLES OF SKILLS FORECASTING  

11.1.  Pan-European forecasts: Cedefop  

REFERENCES AND RESOURCES ON PART B  

ACRONYMS  

ANNEX: COUNTRY CASE STUDIES ON FORESIGHTS AND SCENARIOS  

ANNEX 1. 

BRAZIL  

ANNEX 2.  GERMANY  

ANNEX 3. 

FINLAND  

ANNEX 4. 

JAPAN  

ANNEX 5. 

KOREA  

ANNEX 6. 

THE EUROPEAN UNION  

ANNEX 7. 

SKILLS FORESIGHT IN THE EU ENLARGEMENT REGION  

ANNEX 8. 

RUSSIA  

ANNEX 9. 

AUSTRALIA  

ANNEX 10.  UNITED KINGDOM  

ANNEX 11.  UNITED STATES OF AMERICA  

KEY TECHNICAL TERMS  

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Guide to anticipating and matching skills and jobsLIST OF FIGURES, TABLES AND BOXES

Figures

Figure 1. 

Foresight activity chain  

Figure 2. 

Foresight as part of a continuous process  

Figure 3. 

Scheme of exploratory methods  

Figure 4. 

Scheme of exploratory methods  

Figure 5. 

Foresight diamond  

Figure 6. 

SWOT matrix  

Figure 7.  Activities employed in Delphi analysis  

Figure 8. 

Scheme of the backcasting method  

Figure 9.  Graphical representation of technological roadmapping  

Figure 10.  National accounts data by expenditure category and by sector  

Figure A1.  Updated general outline of the SENAI model  

Figure A2.  FIRJAN occupational observatory  

Figure A3.  Schedule of topic searches  

Figure A4.  Combination of methods in the BMBF’s process  

Figure A5.  Target scenarios  

Figure A6.  Vision for skills 2020  

Figure A7.  Process of foresight  

Figure A8. 

 Growing mismatch between training and new skills demand due to increased rate of 

technological change  

Figure A9. 

 Key steps of the skills foresight study  

Figure A10.   General methodology of foresight sessions  

Figure A11.   Key factors changing working tasks in all technology-driven sectors of the Russian economy  

Figure A12.   Emergence of new competency type in knowledge-based sectors of the Russian economy  

Figure A13.   Scheme of the national strategic skills audit  

Figure A14.   The scheme of the project methods/activities  

Figure A15.   Six drivers and 10 skills needed in the future  

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Developing skills foresights, scenarios and forecastsTables

Table 1. 

Generations of foresight  

Table 2. 

Pros and cons of literature and statistics reviews  

Table 3. 

Pros and cons of SWOT analysis 

Table 4 

Pros and cons of brainstorming  

Table 5. 

Pros and cons of expert panels  

Table 6. 

Pros and cons of Delphi analysis  

Table 7. 

Pros and cons of horizon scanning  

Table 8. 

Pros and cons of using scenarios  

Table 9. 

Pros and cons of cross-impact analysis  

Table 10. 

Pros and cons of backcasting  

Table 11. 

Pros and cons of morphological analysis  

Table 16. 

 Canadian COPS model: the outlook for labour market conditions by occupation is determined 
using observed conditions in recent years and projected trends in jobseekers and job openings  

Table A1.  Original SENAI foresight model  

Table A2. 

2013-27 scenario  

Table A3.  Main drivers of change in each activity  

Table A4.  New critical competencies by occupation function in the specialised software industry  

Table A5.  Scenarios and implication for employment trends  

Table A6. 

 Jobs in expansion, transformation or decline in Scenario E ‘Expanding in specialised 
software, content industries and telecommunications and declining in executive jobs 
in the other industries’  

Table A7.  Main corporate strategies by activity  

Table A8.  Key framework conditions by activity  

Table A9.  Occupation profiles by function and activity in the EU Member States X  

Table A10.  Strategic choices to meet skills needs  

Table A11.  Overview of phases and tasks  

Table A12. 

 Key competencies of the Russian worker of the future  

Table A13. 

 Four competency types for knowledge-based sectors of the Russian economy  

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Guide to anticipating and matching skills and jobs

Boxes

Box 1. 

Box 2. 

Box 3. 

Box 4. 

Box 5. 

Box 6. 

Box 7. 

Box 8. 

Box 9. 

Literature review: example of Brazil  

Brainstorming: US example  

Expert panel: example of Germany  

Delphi survey: example of Japan  

Horizon scanning: example of the UK  

Scenarios: example of Australia  

Backcasting: example of the UK  

Finding research fields and topics: BMBF foresight in Germany  

Involving stakeholders  

Box 10. 

Foresight programme team skills  

Box 11. 

Cultural context in Japan  

Box 12. 

Institutional framework in Russia  

Box 13.  Rationale for skills projections  

Box 14.  Use of input-output data  

Box 15. 

The Lotus model for Vietnam  

Box 16. 

Examples of econometric specifications in Cedefop’s expansion demand  

Box 17.  Simplified replacement demand: concentrating on retirement outflows  

Box 18. 

Imbalances in the Cedefop forecast: RAS procedure  

Box A1.  Original SENAI model: activities and methods  

Box A2.  Adequate institutional framework in place  

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Developing skills foresights, scenarios and forecasts

13

Executive summary

Purpose of skills foresighting and forecasting

Foresighting and forecasting support decisions in areas 
which involve long lead times, such as education and 
training, and long-term labour market planning. Skills 
anticipation offers early warning of evolving skill 
mismatches, allowing sufficient time for action to 
counteract them. Individuals, firms and education and 
training providers, who have to make decisions about 
the kinds of education and training required for the 
future workforce, need to assess future prospects 
carefully, looking to fill information deficits and avoid 
future imbalances and mismatches:. Anticipating the 
future is not straightforward, yet it allows the 
identificaiton of current trends and strategies and their 
likely implications in the future.

The main rationale for skills anticipation is that labour 
market failures make a case for public intervention. 
Generating information for labour market actors 
ensures a better match of skills acquired through 
education and training, an important element for 
economic policy decision-making. Ideally it is 
undertaken within a broader approach, with other 
elements of economic development, where 
investment in education, training and quality of jobs 
is part of the process.

Approaches: foresighting versus forecasting

The guide focuses on mid- to long-term anticipation, 
ranging from five up to 20 years. These foresight and 
forecasting approaches are applied either at national 
level covering the whole economy or – in the case of 
foresights – at sector level for one or more sectors 
within the economy.

Our review from various countries shows that there is 
no single, simple model that could be applied 
universally. However, several principles and approaches 
can be identified that have been successful. Common 

experience in data and institutional requirements are 
reported.

While foresights and quantitative skills forecasts have 
similar goals in informing decision-makers and 
stakeholders about likely future outcomes and their 
probable consequences, they differ in the way they are 
implemented, their requirements in terms of inputs, 
and in the types of outputs that they can generate.

Qualitative foresights require less formalised (data) 
inputs and are easier to set up initially. They do not 
require extensive data series or quantitative modelling 
of labour market relations. Foresights depend on the 
inputs from key experts and stakeholders and on the 
way which they are combined in methodology. 
Foresight is a visionary tool which provides incentives 
for participating stakeholders to come up with the 
future they want and commit themselves to the 
implementation of the vision. Foresight is a highly 
interactive tool of social dialogue with representatives 
of the private sector.

Quantitative skills forecasts offer a consistent and 
detailed picture of future developments by sector, 
occupation, qualification or skills. They are more 
demanding in terms of the availability of adequate 
labour market data, both in quality and in the data 
series length. Building and interpreting quantitative 
models takes time and expertise, even if they are 
based on established principles. Based on international 
experience, the guide suggests combining (modules 
generating) supply by qualification, demand by sector, 
occupation, and possibly skills or qualification.

Both qualitative and quantitative elements can be 
mixed to develop a skills anticipation model or method 
that is suitable for a country’s or region’s purpose. 
Often missing elements of quantitative skills 
forecasting can be substituted by simplified procedures 
or assumptions that can be based on qualitative 
methodology. A methodology of skills anticipation that 

14

Guide to anticipating and matching skills and jobsis mainly based on qualitative approaches can be 
supported by quantitative inputs at various stages and 
settings.

Implementation

The guide emphasises a hands-on approach. It 
discusses the steps that need to be taken in 
foresighting and forecasting approaches. In this 
context, we do not only discuss issues of methodology 
but also of implementation processes.

Skills anticipation will only be successful if it is 
embedded into a structure in which the results are 
developed, discussed and used with the various 
stakeholders and decision-makers. It is not only the 
development and the instruments of generating 
forecasting or foresighting results that are important, 
but also networks and institutions that work with the 
data, feed back into them, and participate in the 
development of the (initial) discussion or evaluation of 
results.

Case studies and descriptions of approaches from 
across the world are included to emphasise the 

importance of learning from others. They also show 
how the basic concepts and instruments need 
continuous adaptation to the particular 
circumstances of the individual situation in specific 
countries.

The guide sees several success factors, both for 
foresights and also for forecasting: successful skills 
anticipation depends on identifying clear and realistic 
goals; it should be embedded into an institutional 
framework which includes identifying and engaging 
key stakeholders; and to broaden the use and the 
usefulness of skills anticipation, dissemination of 
results across various channels should become 
increasingly important. Tools and instruments should 
be chosen according to the infrastructure and 
resources. If the data infrastructure is good, 
quantitative skills forecasting might be considered; 
qualitative foresights are less dependent on 
well-established and long-term data collections. Skills 
anticipation should not be a one-time exercise but a 
sustained, long-term effort that is repeated regularly 
to allow both for the development of a methodology 
of generating the skills forecast or foresight and an 
understanding of how to use the results.

15

Developing skills foresights, scenarios and forecastsIntroduction

We live in a rapidly changing and uncertain world. 
Individuals, firms, and education and training providers 
have to make decisions about the kinds of education 
and training that will offer the best returns. Peering into 
the future is not straightforward, yet there is rising 
demand for information on potential developments. 
Long lead times on investment decisions such as 
education and training mean that it is necessary for all 
those who are making such choices to assess future 
prospects carefully. This includes those concerned with 
macro-level policy in these domains, as well as 
individuals, organisations and institutions making more 
personal choices. Help is needed to fill information 
deficits and avoid future imbalances and mismatches.

Foresights, skills anticipation and forecasting should 
not be seen as only necessary in high-income 
countries. Though such countries usually have easier 
access to funds for these types of projects and more 
developed statistical data available, the basic problem 
addressed by the activities within this guide remain 
important at all stages of development: to acquire an 
understanding of current and future evolution on the 
labour market, and to support the development of the 
right mix of human capital within a nation.

The main rationale for skills anticipation is avoiding 
labour market failures. This makes a case for public 
interventions to ensure a better match of the skills 
acquired through education and training and those that 
are necessary to succeed in the labour market and to 
ensure better lives for individuals. Skills anticipation 
is therefore an important element for economic policy 
decision-making. Ideally it is undertaken within a broad 
approach, with other elements of economic 
development and where investment in education, 
training and quality of jobs is viewed as part of the 
process.

Skills are crucial to prosperity. They contribute to 
economic growth both directly, through increased 
productivity, and indirectly, by creating a greater 
capacity of workers and firms to adopt new 
technologies and ways of working and spurring 
innovation. Conversely, skills shortages and 
mismatches between the supply of and demand for 
skills lower the potential for growth and may lead to a 
waste of resources.

Skills also improve the lives of individuals. The 
economic benefits of higher levels of initial education 
are well documented. Higher levels of attainment are 

16

associated with lower rates of unemployment and 
higher earnings. Research strongly suggests that 
upskilling – measured as the share of post-secondary 
graduates in the population – plays a key role in 
countering the long-term trend of growing inequality 
in earnings. Adult education and training also have 
a significant positive impact on workers’ productivity 
and wage levels. In contrast, having to support 
poorly-skilled people who are under- or unemployed 
can be very costly for governments (OECD, 2011; ILO, 
2008).

Education has a positive impact not only on economic 
performance but also on social outcomes. Adults with 
higher levels of education are more likely to report that 
their health is good, and they tend to have a greater 
interest in politics and higher levels of interpersonal 
trust. Skills – both cognitive and non-cognitive – play an 
important role in empowering individuals to follow 
healthy lifestyles and to be active citizens. The strong 
positive relationship between education and health 
outcomes has also been documented for developing 
countries and emerging economies (ADB, 2008). These 
benefits accrue to society as a whole, as they reduce 
social costs – for example, in health expenditure – and 
foster social cohesion.

Foresights and skills anticipation: Part A

The future is uncertain but, for policy goals, we need 
to try to understand what will happen. Foresight 
techniques and methods play a crucial role in 
revealing future developments. They help to deal with 
uncertainty and provide insights for identifying future 
changes and needs. In education, anticipation of skill 
needs is essential to meet future skill requirements in 
the economy and society. Even though some have 
argued that systematic anticipation of changing skill 
needs is impossible, with sophisticated approaches it 
is possible to fill information deficits and help reduce 
future imbalances and mismatches. Foresight can 
help those concerned with policy-making in 
anticipation of skill needs to make better decisions 
and support planning. It is a helpful tool for education 
policy planning in times of rapid change. It allows 
engagement with a broad range of stakeholders in 
meaningful discussions about not only what we think 
education might become, but also what we want 
education to become.

Guide to anticipating and matching skills and jobsThe concept of foresight is introduced in the first 
chapter of Part A which provides understanding on how 
foresights can be used within skills anticipation. This 
chapter details when and how this methodology is 
useful while chapter 2 introduces the various foresight 
methods and procedures. Those interested in using 
and implementing the methodology should read 
chapters 3 and 4 that intend to show how foresights 
can be implemented and adapted to local needs. The 
annex provides examples of implementation of 
foresights that have been used in various country 
settings.

Skills forecasts: Part B

Quantitative skills forecasts offer a consistent and 
detailed picture of future developments by sector, 
occupation, qualification or skills. Models that provide 
quantitative forecasts of future supply and demand on 
the labour market show – similar to foresight – how 
future outcomes can be generated to guide current 
decision-making. This allows policy-makers to 
understand mid- to long-term developments and to 
react towards expected imbalances.

Chapter 5 provides an overview of the use and usability 
of skills forecasting, the choice on duration and when 
to use quantitative forecasting. Chapters 6 to 9 provide 
detailed elements and methods to be used. Some 
specific issues, such as the problem of technological 
change and qualification requirements, the 
identification of imbalances, thoughts on indicators and 
issues of regional forecasting are explored in Chapter 
10. Chapter 11 discusses some specific examples of 
skill forecasting.

Foresights versus quantitative skills forecasts

While foresights and quantitative skills forecasts have 
similar goals in educating decision-makers and 
stakeholders about likely future outcomes and their 
probable consequences, they differ in the way they are 
implemented, their requirements in terms of input, and 
also in the types of output that they can generate.

Both qualitative and quantitative elements can be 
mixed to develop a skills anticipation model or method 
that is suitable for a country’s or region’s purpose. 
Often missing elements of quantitative skills 

forecasting can be substituted for by using simplified 
procedures or assumptions based on qualitative 
methodology. A methodology of skills anticipation that 
is mainly based on qualitative approaches can be 
supported by quantitative inputs at various stages and 
settings. Some examples are discussed in Part A.

Objectives

This guide aims to provide countries which are starting 
to develop systems of skills needs anticipation with 
information on different aspects and methodologies of 
foresights, as well as ways to model and forecast skills 
needs. The main purpose of the guide is to address the 
following key questions: why undertake skills 
foresighting and forecasting, what can be done and 
how to proceed? It is not the intention to imply that 
foresighting and quantitative skills forecasting must be 
used together: in many cases only one of the two is 
used. However, elements of foresighting will normally 
be included in the assumptions underlying quantitative 
models, and making quantitative models more explicit 
through foresight techniques might improve their 
forecasts. Further, foresights always benefit from many 
different inputs, as we will show below. Quantitative 
forecasts, if available, will always help to form better 
foresight studies, as they provide a structured form of 
information that the outcomes of the foresight can 
reflect upon.

The overall aims of the guide are to help countries to 
develop such systems by providing useful, practical and 
independent advice based on good practice from other 
countries. It aims to support awareness and the 
learning process from the experience of many other 
countries across the world (both developed and 
developing) that can be used in setting the vision and 
designing and implementing a country’s own specific 
systems. It should serve as a reference and toolkit. 

Experience of various individual countries makes clear 
that many different approaches to skills anticipation 
have been applied across the world. It is obvious that 
there is no single, simple model that could be applied 
universally. The cases considered in this study suggest 
that it is possible to draw out lessons from examples 
of good practice as well as some methods that can be 
inspirational when adopting a particular approach in a 
country: the guide provides information on what works 

17

Developing skills foresights, scenarios and forecastsin a particular context. It is possible to extract the main 
success factors common to most cases presented 
here. Although some of the approaches and ideas 
discussed come from a developed country’s 
perspective, they are also readily adaptable to specific 
circumstances of developing countries on a case-by-
case basis.

The guide focuses on mid- to long-term (5 to 20 years) 
skills foresight and forecasting approaches applied 
either at national level covering the whole economy or 
– in the case of foresight – also at sector level covering 
primarily one or more sectors within the economy (see 
annex). It aims to be a self-standing and self-
explanatory document. It is practice focused with a 
didactic step-by-step approach.

The reader

This guide is designed primarily for sponsors of skills 
anticipation activities that can initiate or promote the 
foresight processes and foster implementation of the 
necessary structures. It also intends to advise a broader 
range of stakeholders including policy-makers, education 
and training providers, public employment services, social 
partners, research and specialist organisations and other 
stakeholders that may be involved in such activities.

18

Guide to anticipating and matching skills and jobsPart A: 
Foresight

Chapter 1. 
Foresight and skills

1.1.  What is a foresight?

The term ‘foresight’ is nowadays widely used, 
increasingly so over the last two decades. Even though 
foresight, mostly a qualitative approach, is quite often 
confused with forecasting, extrapolating or planning, it 
does not have the same meaning. Foresight could be 
defined as a systematic, future intelligence-gathering 
and medium to long-term vision-building process 

aimed at identifying opportunities and areas of 
vulnerability to assist present-day decision-making (1). 
The key element in foresight activities is that ‘they are 
action-oriented, in the sense that the final aim is to 
influence, shape and act upon the future. […] Foresight 
processes and outputs should be oriented towards 
contributing to, facilitating or guiding the decision-
making process’ (2). The chain of foresight activities is 
shown in Figure 1. 

Figure 1.  Foresight activity chain

Information

Reality

Analysis

Interpretation

Perspectives

Outputs

Strategy

Mapping the current state

Understanding of the state

Possible future development

What should be done

What and how do we do that?

Source:  Authors based on Valenta (2012) and Conway (n.d.).

(1)  JRC-IPTS, For-Learn online foresight guide: an A to Z of foresight http://forlearn.jrc.ec.europa.eu/guide/A1_key-terms/foresight.htm 

 [accessed 24.6.2014].

(2)  JRC-IPTS, For-Learn online foresight guide: action-oriented http://forlearn.jrc.ec.europa.eu/guide/A1_key-terms/action.htm [accessed 

8.5.2014].

20

Guide to anticipating and matching skills and jobsForward-looking thinking is vital for policy planning to 
be able to meet future challenges proactively, including 
the area of future skill needs. ‘Foresight enhances such 
thinking by gathering anticipatory intelligence from a 
wide range of knowledge sources in a systematic way 
and linking it to today’s decision-making’ (3).

(c)  developing capabilities throughout a region or 

organisation, along with a ‘foresight culture’. The 
aim here is for people with a variety of backgrounds 
to be able to define and embark upon their own 
foresight activities and create their own foresight 
networks;

Typical objectives of foresight exercises include one or 
more of the following:

(a)  informing policy-making so that decisions taken by 
key actors in the commissioning body include more 
awareness of longer-term developments and how 
these are liable to interact with current policy 
decisions. This can involve gathering intelligence on 
possible longer-term developments and how these 
may interact with the policy decisions made today, 
or providing alerts on major future risks and 
opportunities. Often a foresight exercise will be 
stimulated by the need to take a particular decision;

(b)  building networks that bring together people from 

different sectors and institutions involved in shaping 
the future of a particular topic: they are brought 
together to work on their visions and assessments 
of the future. The purpose of this is to help them 
become better able collectively to understand the 
challenges and opportunities that they are liable to 
confront, and the strategies and objectives that 
others might pursue;

(d)  building strategic visions and creating a shared 

sense of commitment to implement the measures 
associated with these visions among foresight 
participants (4).

Foresight is not a one-shot activity but a part of 
continuous evaluation. The results of every foresight 
should be reflected in strategy and implemented in 
relevant areas. However, assessment and subsequent 
analysis should follow implementation of the strategy, 
to ensure feedback and contribute to the intended 
development (Figure 2).

At first, most foresight initiatives focused on 
technology and its social and economic impact. 
New-generation foresights have dealt also with 
demographic change, health, social welfare, transport, 
energy, environment and climate change, community 
development, culture, human resources, and skills 
anticipation and education (5).

(3)  JRC-IPTS, For-Learn online foresight guide: home http://forlearn.jrc.ec.europa.eu/guide/0_home/index.htm [accessed 8.5.2014].
(4)  JRC-IPTS, For-Learn online foresight guide: why do foresight? Objectives http://forlearn.jrc.ec.europa.eu/guide/1_why-foresight/objectives.

htm [accessed 8.5.2014].

(5)  JRC-IPTS, For-Learn online foresight guide: scoping an exercise: major decisions http://forlearn.jrc.ec.europa.eu/guide/3_scoping/dec_

scope.htm [accessed 8.5.2014].

21

Developing skills foresights, scenarios and forecastsFigure 2.  Foresight as part of a continuous process

Assesment

Analysis

Implementation

Foresight

Strategy

Source:  Translated from Valenta (2012, p. 17).

1.2.  Skills anticipation in foresight activities

The main rationale for skills anticipation is to help meet 
future skill needs by providing policy-makers and other 
actors with relevant information. Approaches to skills 
anticipation are important for economic policy 
decision-making. Ideally they are undertaken within a 
broader foresight approach with other elements of 
technology and economic developments, where the 
investment in education/training and the changes in 
structure and quality of jobs are viewed as part of the 
process. Various country cases considered here 
illustrate, however, that many broader approaches have 
resulted in less specific outcomes for skills policies, 
while isolated skills anticipation activities tend to have 
more limited success.

In its relatively brief history as a formalised policy tool 
(foresight started to evolve in the second half of the 
20th century), foresight has undergone a rapid 
evolution and has been adapted to a diverse range of 
policy domains. The generational model of the 
evolution of foresight was developed to classify an 
increasing number of foresight activities (Table 1) 
(Miles et al., 2008). It is not meant to illustrate 
transitional development but all of the approaches 
described coexist today and are used to address 
different types of issues. Therefore, it is not possible 
to assign any time references to particular 
generations of foresight; it is up to the foresight 
practitioner to select the appropriate approach given 
the nature of the issues to be addressed. The third 
and fourth generation forms of foresight, with their 
broad stakeholder involvement and their holistic 
approach to policy issues, are ideally suited to 
addressing skills issues.

22

Guide to anticipating and matching skills and jobsTable 1.  Generations of foresight

First generation Second generation Third generation

Fourth generation

Fifth generation

Scope

• 
• 

Policy-makers
Subject area 
experts

• 
• 

• 

Policy-makers
Subject area 
experts
Industry

Domain

• 

Science and 
technology

• 

Science and 
technology

Inputs

• 

Economic 
and scientific 
benefits of 
technology

• 

• 

Economic, 
social and 
scientific 
benefits of 
technology
Potential 
product 
development

• 
• 

• 
• 

• 

• 

• 

• 

Policy-makers
Subject area 
experts
Industry
Society 
representatives

• 

•  Organisation 
leaders
Subject area 
experts
Industry
Society 
representatives
Policy-makers

• 
• 

• 

•  Occurs within 
specific 
organisations
•  Organisation 
leadership
Subject area 
experts
Industrial and 
professionals 
partners

• 

• 

Science and 
technology
Social policy

Economic, 
social and 
scientific 
benefits of 
technology
Potential 
product 
development

• 

• 

• 

• 

Science, 
technology and 
innovation
Variable 
configurations 
depending on 
participants

Science, 
technology 
and innovation
Variable 
configurations 
depending on 
participants

• 

Social and 
scientific 
benefits of 
technology
• 
Strategic plans
•  Dependent on 
participants

• 

Social and 
scientific 
benefits of 
technology
• 
Strategic plans
•  Dependent on 
organisation

•  Markets

•  Markets

Outputs

• 

• 

Funding 
priorities for 
technological 
development

Funding 
priorities for 
technological 
development

• 

• 
• 

Funding 
priorities for 
technological 
development
Public policy
Education and 
training needs

•  Resource 

•  Resource 

allocation for 
technological 
development

•  Organisational 
strategies
Public policy

• 

allocation for 
technological 
development

•  Organisational 
strategies
Innovative 
services and 
products

• 

Source: Georghiou et al. (2008) cited in Thayer (2012).

23

Developing skills foresights, scenarios and forecastsSkills have been a feature of foresight from early days. 
Initially only higher qualifications were involved 
because of the connections to public research and 
development (R&D) and science and technology (S&T) 
policies. As the target of foresight has expanded to 
include a broader range of social policy concerns, all 
levels and types of education have been addressed: 
formal, non-formal and informal. Since education is a 
primary tool for structuring social futures, it is hard to 
justify omitting it from any large-scale foresight activity. 
However, the worldwide mapping of foresight activities 
carried out by international organisations such as the 
European Union (EU) suggests that skills are included 
in few foresight programmes (16% of all foresight 
programmes in the world specifically addressed issues 
relating to education) (European Commission, 2009).

Foresight might be a helpful tool for skills policy 
planning in times of rapid change. Its emphasis has 
shifted from supporting top-down interventions aimed 
at directly influencing skills produced to less direct 
influence of various stakeholders to make better 
informed decisions (employers, education and training 
institutions, students, workers). This has occurred in 
countries with a more developed foresight culture 
(generally most developed countries). The various 
current approaches to foresight provide a powerful 
toolbox for foresight practitioners to find an ideal 
method suitable for the issues to be addressed and the 
outcomes anticipated. A shift from one-off foresights 
to regular exercises of envisioning future needs has 
occurred in recent years.

A foresight exercise will lead to a number of 
tangible and intangible outcomes, and can also bring 
other benefits on various levels. However, foresight 
has its limitations and cannot be perceived as a 
panacea. 

1.3.  Foresight story

As an example of the motives, policy challenges, and 
the role of a skills foresight exercise, and its possible 
benefits, below is a fictional case study inspired by 
up-to-date worldwide experience.

1.3.1.  Bellandia’s foresight programme 

Bellandia is a medium-size emerging economy. Over 
the past five decades the country had undergone rapid 
industrialisation based on inward-looking strategy. It 
had a highly regulated planned economy at that time, 
with production intended mainly for its internal market 
and, to lesser extent, for similar countries engaged in a 
regional south-south cooperation. The economy was 
based on traditional sectors such as agriculture, natural 
resource extraction or energy production, with a 
growing share of construction, metallurgy and chemical 
industry. Over the years heavy industry production 
became the main contributor to GDP. The industrial 
sector also became the main employer after the 
primary sector (agriculture, natural resource extraction). 
Education and training were developed to provide skills 
that served industry needs. Given that there was little 
pressure on innovation, and that changes in industrial 
production were slow, there was relatively stable 
demand for skills, and education and training could be 
easily adjusted.

Due to internal political changes resulting from an 
adverse economic situation and changes in the 
international environment (dissolution of the regional 
cooperation bloc) in the early 1990s, Bellandia revised 
its development strategy and opted for closer 
integration into world markets. Opening the economy 
exposed Bellandia to intensified global competition. 
More liberalised markets for goods, services and 
investment helped to increase competitiveness and 

24

Guide to anticipating and matching skills and jobsproductivity but also changed the traditional structure 
of the economy. On the down side, the changes 
revealed some already latent social problems (large 
informal sector) and triggered new ones. New 
challenges arising from technological change, 
population growth and social innovation resulted in 
pressure for revision of public policies: industrial, 
social, healthcare, education. The country has 
experienced an unprecedented growing 
mismatch between industry needs for skills and those 
skills provided by education and training: this was 
identified by growing youth unemployment. 
Modernisation of the education and training system 
was urgently needed.

The government was aware of the fact that Bellandia 
was in transition from an industry to a service and 
knowledge-based economy and the country was facing 
unprecedented challenges. Following consultation with 
several international institutions, the government 
initiated development of a new national economic 
strategy called Plan B, aiming to outline where 
Bellandia wished to be in the mid to long term. The 
debates between the Ministry of Economy, the 
Ministry of Labour and the Ministry of Education 
ensured the skills mismatch issue was included on the 
agenda for the plan.

Since it was recognised early that the education and 
training system was unable to respond quickly to 
changes in the labour market, the initial idea was to 
develop a tool that would help to anticipate what skills 
will likely be required in the future rather than just 
recognising current skill needs. The Ministry of 
Education was authorised to launch a skills anticipation 
programme. Economic conditions, however, did not 

allow it to allocate sufficient national financial resources 
for the programme, so a grant was offered under the 
condition of shared financing, allowing the ministry to 
launch the programme. Since there was little 
experience with such programmes, implementation 
was assigned to the newly created Project 
Management Agency, which had project 
implementation for public authorities as its declared 
mission. The Ministry of Education, as the sponsor, 
outlined the programme and instructed the Project 
Management Agency to start the feasibility phase. 
The programme had to embrace all key sectors of the 
economy and their contribution to GDP and 
employment. Soon it was realised that the country 
lacked adequate research capacities and expertise, 
which required it to hire experts and consultants from 
abroad. Further, there was little experience with 
participative social dialogue, weak institutions at 
sectoral level, and weak cooperation among 
stakeholders. A quick survey among employers, 
however, revealed their willingness to participate in 
the programme. A data audit carried out by 
international experts showed there was an 
inadequate data infrastructure for applying quantitative 
methods or building a skills forecasting model. After 
collecting available information on developing skills 
anticipation systems (including studying guides 
published by international institutions and good 
practice examples) and considering the local context, 
the programme focus shifted to foresight approaches 
as the most adequate way of addressing the identified 
challenges.

The proposed foresight programme had to address two 
key questions: how new economic and social trends 
change the nature of working tasks and the demand for 

25

Developing skills foresights, scenarios and forecastsrelated skills; and what are the implications for 
education and training and other stakeholders. It was 
proposed to consider nine key growing sectors of the 
economy and their development in the mid to long 
term (5--10 years). The Project Management Agency 
assessed and summarised the potential benefits of the 
foresight programme for the stakeholders (the sponsor, 
the Ministry of Education, other relevant ministries, 
employers, education and training institutions, 
students) and prepared a feasibility report for the 
sponsor. The Ministry of Education considered available 
resources and the potential benefits of the proposal 
and instructed the Project Management Agency to 
proceed with the programme.

The Project Management Agency started to set up the 
organisational structures for the programme; it also 
began to clarify the key programme design elements, 
such as a closer specification of objectives, outcomes, 
scope, time horizon and the expected duration of the 
exercise. The objectives were adopted from the 
feasibility report. Outcomes were further specified to 
be customised and delivered to the sponsor for 
strategic decision-making as well as for education and 
training institutions, sector employers, and the public 
(students, career advice). The outputs had to cover two 
time horizons: five and 10 years. The scope of the 
programme included the delivery of specified outputs 
to stakeholders and their dissemination to the public 
but not the implementation of results, which was left 
for stakeholders. The programme duration was 
estimated to be 14 months. Special attention was 
given to the engagement of stakeholders. Six groups 
of stakeholders were identified and classified based on 
their potential interest and the likely impact of the 
programme on them: large employers, SMEs, research 
institutions, professional or sectoral associations, 
education and training institutions, and public 

authorities including the sponsor. There were different 
levels of involvement. Only the key stakeholders were 
represented in the steering committee of the 
programme but most were to be included in expert 
activities. All of them, including the general public, 
were the target group for the dissemination activities. 
Soon it was realised that some groups of stakeholders 
were not appropriately represented. For example, the 
SMEs, which represent the bulk of employment 
opportunities, did not have corresponding associations 
that could speak for them. The same was true where 
sectoral institutions did not exist at the time the 
programme was starting.

The methodology outlined in the feasibility phase was 
further developed and refined with the assistance and 
consultation of international foresight experts. This 
resulted in the definition of the best ways to find 
answers to key programme questions, with a stated 
method mix and parameters. Once all the programme 
elements (objectives, outcomes) and the methodology 
were clarified, they were combined into a coherent 
design for the programme and became a basis for 
programme planning. The Project Management Agency 
engaged international experts and the country’s top 
researchers in these activities. Three phases were 
planned.

The first comprised collection, collation and summary 
of available information. It started with a meta-analysis 
of megatrends, international challenges and the 
national environment, followed by a general industry 
analysis and a preliminary analysis of drivers of change 
in a specified sector. The main methods used were 
literature review and semi-structured expert interviews. 
Collation and summary gave the information obtained a 
structure and form and reduced its volume. 

26

Guide to anticipating and matching skills and jobsThe foresight phase comprised translation and 
interpretation of the summarised information to 
produce an understanding of its implications for the 
future. Given the national cultural background, the main 
topic and programme objectives, and the nature of 
possible methods, the Delphi method was chosen as it 
is evidence-based, and enables the identifcation of 
unexpected events, and appropriate stakeholder 
engagement. The number of experts involved was 
estimated to be 200. The phase was designed to 
produce a description of the main drivers of change for 
the sector, market opportunities and threats, new core 
tasks likely to be required in the future, existing tasks 
that will be more in demand, tasks likely to disappear 
and future skills needs (sector-related, cross-sectoral). 
The outputs also included how skills can be formed 
(initial education, on-the-job training) and what can be 
done about it today. This phase resulted in an 
understanding of what can (or cannot) be done for the 
future. 

The third phase included verification of foresight 
results in the steering committee, roundtables with 
decision-makers (sectoral and national level) and 
surveys in stakeholder communities (mail- and 
website-based) for those not directly involved in 
previous activities.

Although the Project Management Agency employed 
project managers with international experience in large 
projects, managing the foresight programme was a 
difficult task. Managing stakeholder expectations 
proved demanding because some presented 
surprisingly diverging views, not identified before, in 
the steering committee. For example, the sponsor 
expected very detailed results for strategic planning for 
a limited budget, while employers were interested 
mainly in the immediate future. The debates about 
refocusing the programme delayed key activities, 
which were also affected by unavailability of experts 
and by other unexpected events.

Once the main activities had been completed, the 
foresight programme could deliver its results to 
stakeholders, with outcomes customised for different 
stakeholder target groups. The future job maps 
(including skill profiles) developed for nine sectors 
were delivered to stakeholders/employers in the 
corresponding sectors. Recommendations for 
curriculum changes were delivered to education and 
training institutions and the Ministry of Education. 
Education and training providers (especially the private 
ones) were interested mainly in outdated skills and 
new skill needs in the mid-term future, while the 
Ministry of Education was concerned with the 
long-term overview for proposed curriculum reform. 
The general public, including students and career 
advisors, were given profiles for the jobs that would be 
most wanted in the future. The dissemination of results 
was through reports, public presentations (for 
professionals as well as the general public), media 
campaigns (press, TV, radio, website-based tools), 
distribution of printed materials (such as leaflets to 
schools and communities) and even mobile phone 
applications.

The programme was effectively able to meet 
stakeholder expectations. The Ministry of Education 
was able to use the long-term (10-year horizon) results 
for the preparation of the curriculum reform, 
introducing more flexible ways of developing school 
curricula. The programme also provided a significant 
input (sectoral trend outputs) into the debate about 
developing the Plan B that could bring Bellandia closer 
to a knowledge-based economy.

The programme also had several immediate positive 
side effects: it established networks with stakeholders 
that can promote the creation of sectoral institutions, it 
reviewed the data infrastructure for monitoring the 
labour market and skills, and it improved the country’s 
evaluation culture. The Project Management Agency 
gained considerable experience in managing the 

27

Developing skills foresights, scenarios and forecastsforesight programme. Now government structures can 
benefit from this experience when considering similar 
programmes; for example, the Ministry of Education is 
considering a foresight programme to identify research 
priorities.

The programme can yield more benefits in the long 
term. Follow-up activities are necessary to take full 
advantage of the foresight programme outcomes. The 
Ministry of Education had already announced its 
intention to monitor the impact of the foresight 
programme (changes in curricula against development 
of skills gaps). Using the results requires further 
investment: financial resources are necessary to 
reform education and training and for training teachers 
as well as to introduce new curricula or teaching 
methods. It also requires true willingness to cooperate 
from other stakeholders. The main contribution of the 
foresight programme is in making available the 
information that can guide today’s stakeholder 
decisions and lead to a desirable future.

28

Guide to anticipating and matching skills and jobsChapter 2. 
Comparative review of foresight and scenario methods 

In this section we provide a detailed description and 
typology of foresight methods and approaches of 
foresight application. The emphasis is on explaining 
how these methods are used, what the features are, 
and what are the pros and cons of using particular 
methods. Most frequently used methods are 
accompanied with an example from a selected country. 
Choosing an adequate method is critical, though choice 
often appears to be based on what is fashionable or 
which method practitioners are familiar with (UNIDO, 
2009). Therefore it is necessary to be able to 
distinguish among particular methods, determine 
which is appropriate to use, and decide how this 
method should be applied. No one particular 
method can be the solution for all problems; usually 
a combination of methods has to be applied and the 
context of a particular country must be considered: 
one size does not fit all. Different problems and 
contexts require different configurations of foresight 
approaches. It is necessary to be familiar with foresight 
methods, but more important is to know the specifics 
and economic, institutional and social background 
of particular countries. This means that evaluation 
of foresight efforts is not just a matter of examining 
the efficiency of the activities but must also consider 
their effectiveness in promoting change to meet the 
challenges confronting us.

2.1.  Categories of foresight methods

Foresight exercises offer many ways to gather 
information regarding possible future outcomes. 
Depending on the aim and nature of the results 
aspired, foresight methodology can be split up into 

several categories: exploratory and normative methods 
and supplementary methods. The latter include 
techniques which are not directly considered as 
foresight methods, but in some way support them in 
achieving their goal. This category comprises SWOT 
analysis, literature and statistics reviews, focus groups, 
and brainstorming.

2.1.1.  Exploratory methods

Exploratory methods start from the present with 
an attempt to see where events and trends might take 
us by exploring ‘what if?’ scenarios (6). This means that 
‘exploratory methods start […] with the pre-conditions, 
beliefs and social or technological possibilities which 
already exist’ (Magnus, 2012). Among typical 
techniques in this category are the Delphi method, 
scenarios or cross-impact analyses.

2.1.2.  Normative methods

Normative methods start with a vision of a possible 
or desirable future and work backwards to see if and 
how this future might be achieved, or avoided, given 
the existing constraints (skills, resources, technologies, 
institutions). Backcasting or morphological analyses 
are representative of this category.

Limited available evidence on which of these approaches 
is better and under which conditions suggests that 
normative methods are more effective where a widely 
shared vision already exists, and where foresight can 
then develop a strategy for how to achieve it; where no 
consensus on the vision has yet been achieved, 
exploratory methods may be more useful (7).

(6)  JRC-IPTS, For-Learn online foresight guide: methodology: exploratory versus normative methods http://forlearn.jrc.ec.europa.eu/guide/4_

methodology/meth_explo-norma.htm [accessed 8.5.2014].

(7)  JRC-IPTS, For-Learn online foresight guide: methodology: exploratory versus normative methods http://forlearn.jrc.ec.europa.eu/guide/4_

methodology/meth_explo-norma.htm [accessed 8.5.2014].

29

Developing skills foresights, scenarios and forecastsFigure 3.  Scheme of exploratory methods

Starting at the present,
and ask what could happen
if this event happened?

Different

futures

resulting from

different
stories

Source:  Authors, based on the For-Learn online foresight guide: exploratory versus normative methods.  
http://forlearn.jrc.ec.europa.eu/guide/4_methodology/meth_explo-norma.htm [accessed 8.5.2014]

Figure 4.  Scheme of exploratory methods

Present

What we

want to

achieve.

Desirable
future

Source:  Authors, based on the JRC-IPTS, For-Learn online foresight guide: exploratory versus normative 

methods. http://forlearn.jrc.ec.europa.eu/guide/4_methodology/meth_explo-norma.htm [accessed 
8.5.2014].

30

Guide to anticipating and matching skills and jobs2.1.3.  Other categories

Another way to classify methods is by considering 
their ability to gather or process information based 
on evidence, expertise, interaction or creativity. These 
attributes are the building blocks of the foresight 
diamond (Popper, 2008). The foresight diamond 
(Figure 5) does not imply the combinations of methods 
mentioned, but shows which attributes prevail in 
a particular method. The foresight diamond describes 
methods from the perspective of how they are created 
and developed and which attributes are determinants 
of a particular method being applied. For example, 
creating scenarios is considered as a creative activity; 
therefore it is close to the peak ‘creativity’ at the 
foresight diamond, even though the inputs are mostly 
‘evidence’-based. The foresight diamond acknowledges 
the core activity when processing the method, not the 
inputs or outputs of a particular method.

The closer the method to a peak of the diamond, 
the more it is affected by a particular attribute. Some 
methods can be considered as mostly evidence-based, 
such as literature and statistics reviews, whereas some 
are mostly focused on creativity, as with scenarios. 
In the middle of the diamond are methods whose 
results are created as a mixture of other attributes. 
Particular attributes overlap and combine in different 
proportions in each method.

Foresight methods can also be categorised as 
qualitative, semi-quantitative and quantitative methods. 

The current trend in foresight is to apply a mixture 
of different approaches; it is broadly accepted that 
foresight activities cannot be completely dominated 
by purely quantitative methods and their results. 
Quantitative approaches often apply qualitative 
elements and qualitative approaches discuss and 
provide insight into the meaning of the numbers which 
are the results of quantitative methods (see also Part B 
on quantitative methods and models).

Qualitative methods are often employed where the 
key trends or developments are hard to capture using 
simplified indicators, or where such data are not 
available. These methods ‘are often used to provide 
meaning to developments and observations. Such 
interpretations tend to be based on particular views, 
beliefs and knowledge which may be difficult to 
corroborate since methods provide a lot of room for 
creative and subjective thinking’ (European Commission, 
2009, p. 72).

‘The second category comprises semi-quantitative 
methods, applying mathematical principles to quantify 
the opinions of experts’ (European Commission, 
2009, p. 72). This approach combines both qualitative 
and quantitative methods and allows presentation of 
numerical outputs from someone’s opinions or views. 
This type of ‘translation’ of qualitative views and 
inputs also allows the use and mix of qualitative 
inputs into quantitative models (see here especially 
Section 8.4).

31

Developing skills foresights, scenarios and forecastsFigure 5.  Foresight diamond

Creativity

Scenarios

Backcasting

SWOT
analysis

Brainstorming

Roadmapping

Delphi method

Focus group

Expert
panel

Morphological 
analysis

Horizon scanning

e
s
i
t
r
e
p
x
E

I

n
t
e
r
a
c
t
i
o
n

Cross-impact analysis

Literature and
statistics review

Exploratory m.

Normative m.

Supplementary m.

Source:  Adapted from Popper (2008).

Evidence

These methods ‘are often used to monitor measurable 
variables and apply statistical techniques to process 
and analyse the often called “hard data” or indicators’ 

(European Commission, 2009, p. 72). Quantitative 
skills forecasting, as described in Part B, is an example 
of these quantitative models.

32

Guide to anticipating and matching skills and jobs2.2.  Foresight methods

There are many foresight techniques which researchers 
may choose and combine; this section describes a few 
in more detail. As identified in the case studies 
(see annex) the methods used most often in skills 
anticipation are the Delphi method, expert panel, 
scenarios, literature and statistics review, 
brainstorming and SWOT analyses. Other methods 
described in this section may also be suitable for skills 
anticipation in some country contexts but their use is 
not so common.

According to research undertaken by the European 
foresight monitoring network (European Commission, 
2009), the methods mentioned above are also those 
that are mostly used in foresight activities, though not 
only for anticipating skill needs. The survey revealed 
that in the more established democracies of Europe 
and North America, stakeholders prefer face-to-face 
forums, thus they prefer methods such as expert 
panels and scenarios. By contrast, in new 
democracies and in Japan, more anonymous 
methods, such as the Delphi method, are often used 
(European Commission, 2009, p. 39). Experiences of 
different countries make clear that many approaches 
to skills anticipation have been applied across the 
world; there is no single, simple model that could be 
applied universally. Any single method is not usually 
used by itself but is complemented or supported by 
other methods or used in foresight ‘packages’. To 
achieve desired outputs, combinations of methods 
have to be applied, where the output of one method 
serves as input for another. The emphasis in this 
section is on describing the most frequent methods 
used in foresight of skill needs and their features and 

specifics. These methods are divided into three 
categories: supplementary methods, exploratory 
methods and normative methods.

2.2.1.  Supplementary methods
2.2.1.1.  Literature and statistics review

When starting to think about the future, we need 
to achieve an understanding of the past and the 
present. One of the methods most used for this 
is a literature and statistics review, which is usually 
done at the start of a foresight project. This ‘is not 
a foresight method as such, but rather an essential 
background activity or a first step in any foresight 
exercise’ (8). A literature and statistics review is part 
of the process rather than a stand-alone method but 
it is a key process which involves observation, 
examination, monitoring and systematic description 
of the technological, sociocultural, political, 
ecological and economic contexts (Georghiou et al., 
2008). Through such a review, knowledge can 
be accumulated and raw data converted into 
information. It aims at broad exploration of all major 
trends, issues, advances, events and ideas across 
a wide range of activities. It is often based on desk 
research and involves a wide variety of sources, 
such as newspapers, magazines, internet, television, 
conferences and reports. Sometimes it can prevent 
us from ‘reinventing the wheel’, because we have 
an opportunity to learn from and build on what 
others have done, as with the literature and statistics 
review used within the SENAI model in Brazil 
(Box 1). (More information about SENAI, the National 
Service for Industrial Apprenticeship can be found 
in the annex.)

(8)  EFP, For Learn: methods: analysis: environmental scanning http://www.foresight-platform.eu/community/forlearn/how-to-do-foresight/meth-

ods/analysis/environmental-scanning/ [accessed 25.6.2014].

33

Developing skills foresights, scenarios and forecastsBox 1.  Literature review: example of Brazil

Within the SENAI foresight model in Brazil, identification of new professions and roles emerging in other countries 
signals possible changes in the labour market. It identifies and learns from the occupation structures of other 
countries to anticipate which setting may be more likely to emerge in Brazil. This drives appropriate education and 
training responses. The methodology is based on research of secondary data sources covering general and sector 
and/or occupation studies conducted in other countries and involves three stages:
1.  literature review to identify sector general and/or occupation studies conducted in selected countries. 

Australia, Canada, New Zealand, the UK and the US are used because they produce studies on occupation 
change and their occupational classifications are compatible with the international standard classification 
of occupations (ISCO) and the Brazilian classification of occupations (CBO);

2.  comparison with existing occupations in the CBO for the sectors studied. This comparison considers work 
tasks in each occupation, as described in CBO, and work activities of occupations described in studies 
from other countries. The comparison classifies identified occupations as emerging, evolving or stable. 
The definition of the US Bureau of Labour Statistics was adopted for the concept of emerging and evolving 
occupations. Emerging occupations include tasks, skills and knowledge that are completely new; they may 
not be present in existing classifications. The tasks in evolving occupations include changes which may involve 
expanding or reducing tasks. Stable occupations are those that do not display changes;

3.  detailed description of emerging, evolving and stable occupations that contains a set of aspects related to job 
content, skills and other requirements for performing these occupations. Secondary data sources provide 
reasons and/or hypotheses for classification of these occupations.

Source:  Cruz Caruso and Bastos Tigre (2004).

A literature and statistics review should be part of a 
foresight exercise as a first step to obtaining a broad 
understanding of the issues. It must then be 

complemented with other methods such as scenarios 
and backcasting. Literature and statistics reviews have 
limitations (Table 2).

34

Guide to anticipating and matching skills and jobsTable 2.  Pros and cons of literature and statistics reviews

Advantages

Disadvantages

provides an understanding of existing knowledge
• 
• 
can reveal major trends, issues or weak signals
•  method is desktop-based and so not too expensive

requires time and research skills

• 
•  must be complemented by other methods
• 

could be underestimated and some signals may 
be missed
very limited perspectives

• 

2.2.1.2.  SWOT analysis

SWOT (strengths, weaknesses, opportunities and 
threats) analysis is not a foresight approach per se but 
can be used to provide inputs in foresight activities. 
It is not usually used by itself but is complemented 
by other foresight techniques, such as scenarios, 
Delphi surveys or expert panels. SWOT analysis can 
be used in tandem with brainstorming.

SWOT analysis is an analytical tool which helps 
to identify the main internal (strengths and 

weaknesses) and external (opportunities and threats) 
factors that may shape the reality (now or in the 
future). It can be used to develop a vision for a 
country/region/sector taking into account its 
strengths/weaknesses and opportunities/threats; or it 
can be used once the vision is already available to 
develop an institutional or business strategy for how 
to achieve the vision, taking into account the 
strengths/weaknesses and opportunities/threats of 
institutions or businesses.

35

Developing skills foresights, scenarios and forecastsFigure 6.  SWOT matrix

 shtgnertS

What are your advantages? 
What do you do well? 
What relevant resources do you have access to? 
What do the others see as your strengths? 

 ssenkaeW

What could you improve? 
What do you do badly? 
What should you avoid? 

Where are the good opportunities in front of you? 
What are the interesting trends you are aware of? 

Opportunities 

What obstacles do you face? 
What is your competition doing? 
Is changing technology threatening your position? 
Do you have debt or cash flow problem? 

Threats 

Source:  Adapted from JRC-IPTS, For-Learn online foresight guide: methodology: main methods: SWOT analysis 

http://forlearn.jrc.ec.europa.eu/guide/4_methodology/meth_swot-analysis.htm [accessed 12.5.2014].

SWOT analysis is often presented as a 2 x 2 matrix, 
summarising significant internal and external factors 
influencing strategies or possible futures. A set of 
questions needs to be answered in every particular 
quadrant to create such a matrix. It is usually prepared 
by an expert team using a variety of data sources 
(UNIDO, 2009).

A SWOT analysis is based on facts or expert opinions. 
The SWOT matrices help assess the probability and 
impact of factors where a scoring system can 
be used to provide a hierarchy of the importance of 
factors. Factors that score highly on both probability 
and impact require special attention in strategy 
formulation (9).

(9)  JRC-IPTS, For-Learn online foresight guide: methodology: main methods: SWOT analysis http://forlearn.jrc.ec.europa.eu/guide/4_methodol-

ogy/meth_swot-analysis.htm [accessed 12.5.2014].

36

Guide to anticipating and matching skills and jobsTable 3.  Pros and cons of SWOT analysis

Advantages

Disadvantages

• 
• 
• 
• 

simplicity and flexibility
looking at the problem from a different perspective
opportunities can be exploited
the possibility to overcome weaknesses

• 
• 
• 

inadequate definition of factors
oversubjectivity in generation of factors
no suggestions for solving disagreements

Source:  JRC-IPTS, For-Learn online foresight guide: methodology: main methods: SWOT analysis.  

http://forlearn.jrc.ec.europa.eu/guide/4_methodology/meth_swot-analysis.htm [accessed 12.5.2014].

2.2.1.3.  Brainstorming

Brainstorming belongs among creative methods 
and is often used at early stages of foresight 
activities, usually prior to scenario building or within 
SWOT analysis. Brainstorming as a method 
encourages group thinking and supports the 
generation of ideas. By encouraging individuals to 
discuss their own ideas, brainstorming is a powerful 
tool which may help to increase the group’s 
ownership of the result, prevent conflict and 
achieve consensus. Brainstorming has to follow 
basic principles, if it is to be done correctly:

(a)  no criticism and judgements. To encourage 

creativity and improve the value of ideas, all ideas 
are presented and accepted without any negative 
comment or negative evaluation;

(b)  all opinions are equal. All participants should feel 
free to present their ideas, regardless of their 
status or position in the social hierarchy. Nothing 
is considered as undesirable;

(c)  quantity exceeds quality. Generating as many 

solutions as possible to tackle defined problems 
is the required output of this technique; 

(d)  evaluation after discussion. To prevent a distortion 
or prioritisation of some ideas, evaluation should 
be done after the brainstorming. It is recommended 
to wait a couple of days for this (Potu˚cˇek, 2006).

Effective brainstorming sessions involve seven to 12 
participants. There are examples of using brainstorming 
in the latter phases of a foresight process, as is shown 
by the case highlighted in Box 2.

37

Developing skills foresights, scenarios and forecastsBox 2.  Brainstorming: US example

Brainstorming is often used in the initial phases of foresight projects to gather a wide pool of ideas and to identify 
main future trends and/or key issues that should be in the scope of further research in the next stages. It may be 
also used as a means of assessing and interpreting the results in a later stage of the process. This is how it was 
used by the Institute for the Future (IFTF) based in the US during their Future work skills 2020 project. After the 
pieces of intelligence were gathered, while drawing on IFTF’s previous foresight exercises and their ‘signals 
methodology’, a group of experts was brought together to participate in the brainstorming exercise. They were 
experts from various fields and with diverse professional backgrounds. Their task during brainstorming sessions 
was to identify key drivers of change and how these will shape future skill requirements. In the next step, the 
outcomes were further analysed and filtered and the final six key drivers and 10 skills areas for the next decade 
were determined.

Source:  IFTF (2011).

This example is an exception, because brainstorming 
is usually used before the foresight process, 
for example prior to scenario analysis. The results 

of brainstorming are not used separately, but are 
part of the assessment process (10).

Table 4.  Pros and cons of brainstorming

Advantages

Disadvantages

• 

• 
• 

• 

brings new ideas on how to tackle a particular 
problem
free thinking encourages creativity
problems can be defined more clearly as new 
questions arise
brainstorming helps to reduce conflicts

• 

• 
• 

the importance of the moderator is often 
underestimated
sometimes the ideas produced are unworkable
criticism often appears and ‘kills’ creative ideas

Source:  Adapted from JRC-IPTS, For-Learn online foresight guide: methodology: main methods: brainstorming. 
http://forlearn.jrc.ec.europa.eu/guide/4_methodology/meth_crea_brainstorming.htm [accessed 
12.5.2014].

2.2.1.4.  Focus group

A focus group supports the generation of ideas and 
encourages group thinking. ‘A focus group is a form 
of qualitative research in which a group of people are 
asked about their perceptions, opinions, beliefs, and 
attitudes towards’ (11) issues of interest. The main 
distinction between brainstorming and a focus group 
is that the purpose of brainstorming is mainly to 
generate ideas, whereas a focus group concentrates 

on improving, validating or complementing existing 
ideas. Participants in a focus group are encouraged not 
only to express their own opinions, but also to respond 
to other members and questions posted by the facilitator 
(12). Brainstorming must have ground rules, whereas 
a focus group is more open-ended, though both 
methods bring together people in an effort to gain 
their ideas and opinions.

(10) JRC-IPTS, For-Learn online foresight guide: methodology: main methods: brainstorming http://forlearn.jrc.ec.europa.eu/guide/4_methodolo-

gy/meth_crea_brainstorming.htm [accessed 12.5.2014].

(11) Wikipedia http://www.cse.lehigh.edu/~glennb/mm/FocusGroups.htm [accessed 25.6.2014]
(12) Focus groups or similar techniques are also often used in the context of quantitative forecast. In evaluating and discussing detailed results 
with key stakeholders in a focus group setting, the necessary qualitative background and feedback can be generated that help to improve 
quantitative models (see also Section 2.2 on foresight methods).

38

Guide to anticipating and matching skills and jobsFor a focus group, it is necessary to emphasise:

(a)  defining the purpose: this should be clear and 
specific to avoid being too broad and general. 
The objectives of a focus group are either validating 
or clarifying findings or filling information gaps;

(b)  establishing a timeline. It is necessary to identify 
the participants, develop questions, locate a site 
and gather materials for the session;

(c)  identifying participants. First, it should be 

determined how many participants are needed 
(usually 6--12, it is better to keep the number of 
participants low) and who will be suitable ones. 
Participants may be all from one field or it could 
be a combination of different types of stakeholders. 
Identifying participants should be carefully planned 
to create a non-threatening environment;

(d)  generating the questions. The focus group may last 
from one hour to one day but usually does not take 
more than a couple of hours; therefore the 
emphasis should be put on a limited number of 
principal questions. Questions should be open-
ended and move from the general to the specific. 
Sometimes it is appropriate to send relevant 
materials or even distribute the questions to 
participants in advance, so they have time to get 
familiar with the topic and all issues.

(e)  selecting the facilitator: this is an essential role. 

The facilitator should keep the discussion on track 
and make sure every participant is heard. 

The focus group session should be well organised; 
all equipment and materials should be prepared and 
time should not be exceeded. The meeting should 
be summarised, the summary analysed and the final 
report should be created. Careful and systematic 
analyses of the discussions provide information on 
how the discussed issue is perceived by the group 
and perhaps what could be done better or improved. 
The advantages and disadvantages of focus groups 
and brainstorming are the same.

2.2.2.  Exploratory methods
2.2.2.1.  Expert panel

Expert panels are widely used in skills foresights 
to stimulate sharing knowledge and ideas among 
experts. Panels usually consist of about 10--20 
experts but may involve many more if the discussion 
is well structured and occurs in fairly small groups. 
Panels are useful platforms for generating and 
debating ideas on the future, gathering and 
validating information, and formulating priorities 
and actions. 

The key element in this method is to develop a profile 
of the panel, i.e. to identify the sorts of expertise and/
or stakeholders that should be represented in light of 
the panel’s remit. The composition of expert panels 
should serve its objectives: a good mix of knowledge, 
views, institutional representation, political roles, 
values and disciplines is required to achieve useful 
and balanced outcomes.

39

Developing skills foresights, scenarios and forecastsPanel composition is of the utmost importance 
because, to achieve an outcome which would be 
perceived to be reliable and legitimate, experts 
on the panel have to be technically valued and 
politically recognised. Reliable and legitimate 
outcomes and panel recommendations for the 
future are built on factual analyses of the past and 
present with reasonable confidence of accuracy. 
This is why research and data analysis, carried out 
by members of the project team, external 

consultants, or even panel members, often 
precedes the panel discussion (13).

Typically experts meet face-to-face at regular intervals 
over a specified time. However, as a variation there is no 
need to meet face-to-face and some panels never meet 
at all: interaction may occur online, and the number of 
panel members does not need to be limited (14).

An example of an expert panel in practice is described 
in Box 3.

Box 3.  Expert panel: example of Germany

During the Federal Ministry of Education and Research (BMBF) foresight process a sequence of expert panels 
is used to identify new future research and technology fields. This helps to specify and refine topics of interest 
and innovation. The cooperation with experts is based on face-to-face workshops, alternating with online surveys, 
which lead to a step-by-step reclustering of topics.
The cooperation with experts includes three tools:
•  cooperation with a closer group of experts through workshops and interviews;
•  a broad online survey of experts to provide a differentiated evaluation of relevance and the need for action  

(2 659 valid responses);

•  using the specific tool for inventor scouting (targeted surveying of young researchers).
The default base is created by nominated topic coordinators who prepare detailed maps of the developments 
expected over the next 10 to 20 years in 14 of the research fields drawn from the German high-tech strategy.

The goals of the expert panel are to reach consensus 
on key issues or to identify priorities and to come up 
with specific suggestions on how these can be 

implemented. Ideally, a clear plan with accountability 
of organisations for the implementation of actions 
is part of the panel’s ‘political’ objectives.

(13) JRC-IPTS, For-Learn online foresight guide: methodology: main methods: expert panel http://forlearn.jrc.ec.europa.eu/guide/4_methodology/

meth_expert-panel.htm [accessed 12.5.2014].

(14) Ibid.

40

Guide to anticipating and matching skills and jobsTable 5.  Pros and cons of expert panels

Advantages

Disadvantages

•  wide perspective on issues
• 
• 
• 
• 

fast feedback
convergent and divergent thinking
good for evidence building
encourages active engagement

• 
• 

• 

experts can be wrong and miss weak signals
a different group of experts may offer different 
advice
a dominating personality can take over the panel 
process

Active engagement of experts is a clear value of this 
method. Specific forms of the interaction are mostly 
driven by complementary methods, such as 
brainstorming, scenarios or SWOT analyses.

Delphi analysis comprises several activities, where 
one activity is followed by another to achieve a 
desired goal. All activities to be performed are shown 
in Figure 7.

2.2.2.2.  Delphi method

Delphi is another widely used exploratory technique 
with the aim of structuring group thinking and 
communication to reflect on complex issues. ‘It is 
particularly used by experts in a series of iterative 
learning rounds. Delphi first establishes the group’s 
initial view, presents instant feedback on differing 
opinions, and seeks an agreed position in the final 
round. Contributors to the group analysis do not have 
to meet in person and can see the results as they, and 
their colleagues, add their views in real time. At the 
beginning, the organiser formulates questions about 
the future and presents these to the contributors. 
Contributors respond by adding their rankings and 
comments. The organisers then modify the anonymous 
comments received to formulate better questions. The 
process is run again, in a series of rounds, until a 
consensus answer is arrived at’ (Jackson, 2013, Section 
3.7). The Delphi method can be defined as a relatively 
strongly structured expert group communication 
process on a topic, where there is incomplete 
knowledge (Häder and Häder, 1995).

Delphi is an expert survey implemented in two or more 
rounds, where in the second and later rounds of the 
survey, the results of the previous round are provided 
as feedback (Cuhls, n.d.). ‘Therefore, the experts 
answer from the second round on under the influence 
of their colleagues’ opinions, and this is what 
differentiates Delphi from ordinary opinion surveys. 
The idea is that the respondents can learn from the 
views of others, without being unduly influenced by 
the people who talk loudest at meetings, or who have 
most prestige, etc. Ideally, significant dissenters from a 
developing consensus would be required to explain 
their reasons for their views, and this would serve as 
useful intelligence for others’ (15).

The method is particularly useful for assessing the 
distant future (up to 30 years). The Delphi survey is 
a qualitative tool and is not meant to be representative (16), 
so the sample typically includes a limited number of 
respondents. The restricted quantity, however, 
is compensated for by the quality of respondents 
whose knowledge and opinions are important, either 
because of their political status (when they represent 
important policy institutions or membership 
organisations) or because of their recognised expertise 
in the subject. 

(15) JRC-IPTS, For-Learn online foresight guide: methodology: main methods: Delphi survey http://forlearn.jrc.ec.europa.eu/guide/2_scoping/

meth_delphi.htm [accessed 12.5.2014].

(16) Readers interested in the methodology of representative surveys can consult Volumes 5 and 6 on development establishment surveys and 

tracer studies.

41

Developing skills foresights, scenarios and forecastsFigure 7.  Activities employed in Delphi analysis

Source:  Adapted from Potu˚cˇek (2006).

Delphi is useful ‘where there is no empirical 
database, where external factors are likely to have 
a determining effect and where social arguments 
may dominate economic or technical considerations. 
As it implies identifying topics that are relevant for 
the future, it reduces the tacit and complex 

knowledge to a single statement and makes it 
possible to judge. On the other hand, in more 
complex issues, when the themes cannot be 
reduced that much or when thinking about and 
discussing alternatives are the major target, 
Delphi is not the method of choice’ (17).

(17) JRC-IPTS, For-Learn online foresight guide: methodology: main methods: Delphi survey http://forlearn.jrc.ec.europa.eu/guide/2_scoping/

meth_delphi.htm [accessed 12.5.2014].

42

Guide to anticipating and matching skills and jobsTable 6.  Pros and cons of Delphi analysis

Advantages

Disadvantages

• 
• 
• 
• 

the possibility of avoiding large group gatherings
virtual participation
handles single or multiple questions
brings together a large number of experts and thus 
different opinions

• 
• 
• 
• 

time-consuming process
labour intensive
participant expertise may reduce results
team leaders can bias the results

The Delphi method is usually complemented or 
supported by brainstorming or scenario development in 
the preparatory phase to define principal statements. 
Data from desk-based studies such as a literature and 

statistics review can also be added (18). A detailed 
description of using Delphi in Japan for technology 
foresight is described in Box 4.

Box 4.  Delphi survey: example of Japan 

Japan is known as a pioneering country in science and technology (S&T) foresight. Since the early 1970s such 
surveys were implemented by means of the extensive Delphi survey and only since the eighth S&T foresight 
(2003-04) has the Delphi also been accompanied by other methods. In 2009 the ninth S&T foresight was 
conducted by the National Institute of Science and Technology Policy (NISTEP), an organisation affiliated with the 
Ministry of Education, Culture, Sports, Science and Technology (MEXT). The whole foresight exercise ‘employed 
a combination of Delphi survey, based on interdisciplinary considerations with future targets in mind, scenario 
method using several techniques, and region-based discussions (NISTEP, 2010, p. 1). In the Delphi survey, for the 
first time an attempt was made to outline possible future developments in major areas that were defined 
irrespective of existing S&T disciplines. The leading question was set as ‘what we should do from now on’ to 
reach future goals and resolve the global and national challenges. The main stages of the Delphi survey were the 
following: 
1.  four preliminary panels were organised, defined by broad themes: security, safety, international collaboration, 

and international competitiveness. Experts (from humanities and social sciences, plus natural sciences) 
discussed future targets that can be attained with the contribution of S&T, and the main global and national 
challenges. Through the extensive discussions, 24 critical issues were identified;

2.  a total of 12 interdisciplinary panels were established to determine relevant future issues irrespective of the 
existing scientific disciplines. The panels consisted of 135 experts in the humanities, social sciences and 
natural sciences (from universities, industrial sectors, and research organisations). During their discussions 
the main topics and areas for the survey emerged. The topics covered elements of, for example, future 
science, technology, and social systems while the areas represented a group of interrelated topics.

(18) Idem.

43

Developing skills foresights, scenarios and forecastsThrough the discussions, the primary focus was on the sciences and technologies that can contribute to 
resolving the global and national challenges. The relationship of the topics and areas with the critical issues 
was also taken into consideration. The names of disciplines or technological fields were intentionally left out 
of the panel designations. The scope and central theme of the discussion was determined by each panel. 
A general framework for subsequent discussions was considered. It was pointed out that the paths through 
which future science and technology will be assimilated into society are of great importance. Therefore it was 
necessary to bear in mind the following aspects: systematic research and development involving the 
interrelated areas of S&T; a point of view that grasps multiple and interrelated areas of S&T as a unified 
system; research of the methodology for implementation in a society; and an understanding of the social 
system as an inseparable part of S&T.

Relationship with 12 interdisciplinary panels and four object-specific panels

Cross-cutting discussion on science and 
technology

Discussion by the members from various disciplines

No. 1

No. 2

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No. 12

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collaboration

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competition

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3.  two rounds of the extensive questionnaire survey were conducted among experts who offered responses 

on the future perspective of the topics (832 in total) from the viewpoint of a time span of 30 years until 2040 
(focusing on 2025). In the first round, additional questions were designed by each panel about Japan’s desired 
approach to resolving global and national challenges. By the end of the second round, 2 900 responses had 
been gathered. 

4.  analyses of the final results from the second round were carried out. 

The Delphi survey fed into the multi-methodology ninth S&T foresight that serves as a major recent source 
of comprehensive intelligence regarding what areas the future policies should focus on. The project outcomes 
showed that a mission-oriented and interdisciplinary approach is effective for finding out the direction of 
innovation. An interdisciplinary approach and system-oriented thinking, that includes not only technical systems 
but also social systems and provision of services, are necessary for grasping the future and embedding 
technologies in real society, with human resources as one of the key accompanying areas. The results have been 
used to create a draft of the S&T basic plan of the government and they are also being considered in the process 
of developing the R&D plans in individual organisations.

Source:  NISTEP (2010, pp. 1--4).

44

Guide to anticipating and matching skills and jobs 
 
 
 
 
 
 
 
 
 
 
 
2.2.2.3.  Horizon scanning

Horizon scanning is both a name given to broad 
processes of future thinking and also a specific 
foresight or future tool. As a tool it ‘is a technique 
[designed] to detect early signs of potentially important 
developments through a systematic examination of 
potential threats and opportunities’ (19). One of the 
definitions of horizon scanning characterises this 
method as a systematic examination of opportunities 
and likely future developments which are at the 
margins of current thinking and planning: ‘it explores 
novel and unexpected issues, as well as persistent 
problems and trends’ (20). Potential changes are usually 
identified over a long timescale and it is possible to 

explore the impacts of these changes in terms of what 
skills will be required in the future.

Horizon scanning is a structured evidence-gathering 
process based on desk research and expert opinions. 
It engages experts who are at the cutting edge of their 
areas of specialisation and makes them look ahead 
beyond usual timescales and settings. Horizon 
scanning is a form of collective thinking, but, unlike 
the Delphi method, diversity in the insights of the 
group is encouraged. The method works well with a 
large group of participants (at least 20). The insights 
discovered can provide the basis for decision-making 
and for programme development. Horizon scanning 
has been used in the UK for identifying trends (Box 5).

Box 5.  Horizon scanning: example of the UK

The UK Commission for Employment and Skills carried out the National Strategic Skills Audit for England (UKCES, 
2010) with the aim of providing valuable insights into the country’s strategic skills needs. Within this project, a 
‘horizon scanning and scenario’ foresight exercise was commissioned to assess the future drivers, challenges and 
opportunities for UK skills. The St Andrews Management Institute carried out the exercise and produced the 
report that was one of the key information sources for the National Strategic Skills Audit. Horizon scanning was 
one of the core methods used, divided into two stages:
1.  general scanning.

The purpose of the horizon scanning was to identify the trends and drivers that may potentially affect the UK’s 
long-term employment and skills landscape and future skills needs. A variety of published information sources 
was scanned to identify global and national key factors of future development. The factors were drawn 
basically from the four ‘PEST’ areas (political, economic, social and technological factors) but also included 
legal, regulatory and environmental factors. It considered not only issues with a potential impact up to the 
project’s horizon of 2020, but also beyond (over the next 25 years), to stretch the thinking about the future. 
This extended time horizon could also provide useful material for the eventual further development of the time 
lines of the scenarios that were drafted in the other steps of the project. 
The general scanning phase identified 101 trends. The raw information extracted during this activity, together 
with the associated sources, was gathered in a separate ‘clippings file’. It has been made available to the 
client, but not distributed publicly with the final report as it was covered by copyright;

(19) OECD, Overview of methodologies: horizon scanning http://www.oecd.org/site/schoolingfortomorrowknowledgebase/futuresthinking/over-

viewofmethodologies.htm [accessed 25.6.2014].

(20) Ibid.

45

Developing skills foresights, scenarios and forecasts 
 
2.  prioritisation.

In the second step, a project team workshop was held to review the 101 trends. The trends were sorted 
according to the probability that they would be important to the world in general and impact the UK skills 
landscape, and grouped into interrelated clusters. A four quadrant chart was created, based on these two 
dimensions. 
The trends with low estimated relative importance for the world and with low impact for the UK skills at the 
same time (bottom left quadrant) were excluded from further analysis. This resulted in 23 trends and drivers 
identified as likely to be the most influential factors of future development. These trends were further analysed 
in the next stages of the project with the help of scenario methodology.

Prioritisation of the drivers

Source:  Duckworth et al. (2010).

46

Guide to anticipating and matching skills and jobsIncreasing impact on the UK skills landscapeIncreasing importance on the worldInfrastructure and networksRise of ChinaResource shortagesDevolution, EU and trade liberalisationSecurity concernsEconomic growth in the UKKnowledge economyCost and availability of capitalNew technologiesDeath of distance in working practicesMigrationGeneration YReducing carbon emissionsRegulationThe future of educationNew jobs and new industriesExisting industryAdvances in social scienceImproving the workplaceDeclining civic valuesOuter spacePromisesMeeting growing consumer expectationsNew ways of workingDelivery of training onlineDesign and mediaAgeing workforce in the UKGovernment interventions to 2020 
 
As can be seen in Box 5, outputs of horizon scanning 
are further filtered and refined and often are fed into 

or linked with other methods such as scenario 
development or the Delphi method.

Table 7.  Pros and cons of horizon scanning

Advantages

Disadvantages

• 
• 
• 

identifying future challenges and trends
drawing insights from participants
reviewing a broad spectrum of information, beyond 
the usual timescales and sources

• 

• 
• 

outputs must be fed into other methods to gain 
reasonable results
requires time and research skills
there is always a possibility of missing some weak 
signals

2.2.2.4.  Scenarios

‘Scenario planning is one of the most well-known and 
most cited as a useful technique for thinking about the 
future’ (Jackson, 2013, Section 3.20). ‘In foresight 
projects, the scenario method is a policy analysis 
tool that helps to describe a possible set of future 
conditions’ (21). The scenario method is also often used 
within quantitative forecasting projects to analyse and 
describe the outcomes of a skills forecast. An example 
is the pan-European skills forecast by Cedefop 
(Section 11.1).

‘At national, regional and local level scenarios can be used 
to improve planning capacity, to enrich strategic public 
policy decisions and to guide major capital investments. 
Scenarios are a preparation for potential future 
challenges, not predictions of what will happen. They help 
us to identify future option spaces and give us confidence 
to act in a world of uncertainty [...] The method creates 

plausible views of the future that decision-makers can 
use to determine their best response and how to react 
to alternative plays. Scenarios are qualitatively distinct 
visions, told as stories, of how the future looks. They 
make explicit the assumptions of how the world works’ 
(Jackson, 2013, Section 3.20).

The main contribution of this method is helping the 
decision-makers to consider possible options and to 
choose the preferred vision for future policy decisions. 
It motivates creative thinking beyond the usual 
short-term perspective. The following criteria are 
important for scenario development:

(a)  plausibility: a scenario must fall within the limits of 

what might conceivably happen;

(b)  consistency: the logical flow in a scenario must not 

undermine its credibility;

(21) JRC-IPTS, For-Learn online foresight guide: methodology: main methods: scenario building http://forlearn.jrc.ec.europa.eu/guide/4_method-

ology/meth_scenario.htm [accessed 25.6.2014].

47

Developing skills foresights, scenarios and forecasts(c)  decision-making utility: the purpose is to tell us 

today what might happen in the future, so we can 
act upon it (22).

There are many different possible ways of developing 
scenarios. Although they might differ in some features, 
and use different terminology, they still bear many 
similarities (Potu˚cˇek, 2006). Most of them follow a 
similar process that includes the following stages:

(a)  identification of the key issues: this requires a good 

and fairly narrow focus with the appropriate 
time-horizon;

(b)  identification of key drivers of change that influence 
the defined issues both at macro/global (social, 
technological, political, economic, legislative and 
environmental) and micro level;

(c)  prioritisation of the importance of the key issues 

and the identification of uncertainties surrounding 
the influence of the drivers of change: this stage 
leads to developing scenarios with factors with high 
importance/low uncertainties and high importance/
high uncertainties;

(d)  formulation of a limited number of scenarios 
(plausible, consistent and usable by decision-
making) that are narratives of how events might 
unfold between now and the selected time-horizon 
and what might happen to each key trend or factor 
in each scenario.

(e)  turning scenarios into strategies and/or 

determining the opportunities and threats that 
each scenario brings. It is better not to categorise 
scenarios as either the most or least likely: keep 
an open mind to all possibilities. Choosing only 
one scenario as a goal may blind other 
developments and possibilities. However, all 
stakeholders should operate with one common 
scenario to harmonise their strategies to achieve 
desirable outcomes (23).

Scenarios were used by the Australian Workforce and 
Productivity Agency to determine what the key drivers 
of demand and supply are in the Australian labour 
market to 2025, and which scenarios should be 
considered – see Box 6.

(22) JRC-IPTS, For-Learn online foresight guide: methodology: main methods: scenario building http://forlearn.jrc.ec.europa.eu/guide/4_method-

ology/meth_scenario.htm [accessed 12.5.2014].

(23) Ibid; Potu˚cˇek, 2006; case studies.

48

Guide to anticipating and matching skills and jobsBox 6.  Scenarios: example of Australia

In 2012 the Australian Workforce and Productivity Agency developed four possible, plausible scenarios for 
Australia to 2025 to deal with the uncertainty and limitations of making projections about the future in developing 
the Future focus, 2013 national workforce development strategy. Scenarios are alternative visions of the possible 
future and provide a means to make decisions that take account of uncertainty. 
The intention is that, by comparing these alternative scenarios, the significance of different uncertainties can 
be better appreciated. A comparison of the model results based on these scenarios helps to identify how much 
difference possible alternative future developments are likely to make to the demands for different skills, plus 
why, and what responses might then be most appropriate.
The four scenarios are: 
1.  long boom: this scenario is based on a quick recovery by the world economy from the uncertainty of 2011--12. 
Australia prospers through strong demand for resources, agricultural products and services to Asia, particularly 
China and India. In a restructuring economy, firms adopt productivity-enhancing strategies to remain 
competitive;

2.  smart recovery: the key characteristic of this scenario is instability in global financial markets, which continues 
until 2014--15. Economic growth accelerates after governments in the United States and Europe constrain the 
rate of growth of government debt to a path that markets accept as sustainable. Australia moves out of a low 
growth economy thanks to the power of technology and the adoption of knowledge work as a critical factor 
in productivity gains and new job creation;

3.  terms of trade shock: in this scenario new global sources of mineral and energy resources lead to oversupply 
and strong downward pressure on prices. Australia’s terms of trade fall, returning to historical levels, and the 
dollar loses value. However, technology and innovation drive industry restructuring and competitiveness, 
and other non-mining industries do better;

4.  ring of fire: this scenario is a world of continuing uncertainty and volatility characterised by sovereign debt, 
bankrupt governments and a string of political crises involving many small shocks. Australia becomes more 
conservative and protectionist, leading to lower growth in productivity and in the economy.

In developing these scenarios a key question was: what are the main drivers of demand and supply for skills in the 
Australian labour market to 2025? The drivers identified were: 
•  social, demographic and cultural trends;
•  economic and financial trends and globalisation;
• 
labour force, industrial and workplace trends;
•  science, technology and innovation;
•  governance and public policy;
•  sustainability (focus on water, energy, population). 

49

Developing skills foresights, scenarios and forecastsAll four scenarios identified common elements which will impact on Australia’s future workforce: the ageing 
population, globalisation, the importance of Asia, the increasing take up of technology and an urgent need for 
Australia to grow its stock of qualified workers.
To support the work on the scenarios, a range of activities were undertaken:
•  a joint forum with the Academy of Social Sciences which included papers from six experts each addressing 

one of the drivers identified above;
interviews with 24 experts to inform the development of the scenarios;

• 
•  stakeholder workshops to test the emerging scenarios;
•  a webinar to provide information about the scenarios and the opportunity for questions and comments;
•  a discussion paper followed by a period of consultation;
•  provision of inputs to the 2013 national workforce development strategy.

Source:  AWPA, Scenarios for Australia to 2025 http://www.awpa.gov.au/our-work/Workforce%20development/

national-workforce-development-strategy/2013-workforce-development-strategy/Pages/Scenario-
Development.aspx

Scenarios are usually preceded and supported by 
SWOT analysis. Also roadmapping (see later) can be 

used to test the consistency and plausibility of 
scenarios.

Table 8.  Pros and cons of using scenarios

Advantages

Disadvantages

• 
• 

• 
• 

helps to avoid unpleasant surprises
helps to ‘see’ the future and so make better 
decisions today
helps to inspire, engage and enable shared action
helps to identify issues for further horizon scanning

can be constructed as the ‘official future’
people may not be able to suspend their disbelief

• 
• 
•  may suffer from cultural/cognitive myopia
• 

cannot be validated

2.2.2.5.  Cross-impact analysis

‘Cross-impact analysis is the general name given to 
a family of techniques designed to evaluate changes 
in the probability of the occurrence of a given set of 
events consequent on the actual occurrence of one 
of them’ (24). Probabilities of the occurrence of events 
‘can be adjusted in view of judgements concerning 

potential interactions among the forecasted items’ 
(Gordon, 2003, p. 4). ‘The model was introduced as 
a means of accounting for the interactions between 
a set of forecasts, when those interactions may not 
have been taken into consideration when individual 
forecasts were produced’ (25).

(24) JRC-IPTS, For-Learn online foresight guide: methodology: main methods: cross-impact analysis http://forlearn.jrc.ec.europa.eu/guide/4_

methodology/meth_cross-impact-analysis.htm [accessed 13.5.2014].

(25) Ibid.

50

Guide to anticipating and matching skills and jobsBecause future developments may be defined as the 
result of interactions between trends and events, the 
first step in this method is to define events that can 
play a role in the characterisation of future 
developments of the defined issue (26). This is crucial 
to the success of the exercise. The initial set of 
events is usually compiled from a literature and 
statistics review and/or opinions of experts; it may 
also come from actions to collect opinions, such as 
the Delphi method. Once the event set is determined, 
the next step is to estimate the initial probability 
of each event (Gordon, 2003, p. 4). Afterwards, 
conditional probabilities in a cross-impact matrix ‘are 
estimated in response to the question: “If event 
A occurs, what is the new probability of event B?” 
The entire cross-impact matrix is completed by asking 
this question for each combination of occurring event 
and impacted event (Gordon, 2003, p. 5). The best 
way to illustrate this method is by example. Let us 
assume that there is an event A with initial probability 
of occurrence 0.5. The other event, B, has an initial 
probability of occurrence judged to be 0.1. If event 

B occurs, the probability of event A would change 
to 0.65, if event B does not occur, the probability 
of event A would still be 0.5. The relationship between 
these events can be also reciprocal; this means that 
not only does B affect A, but A can affect B. This is the 
simplest model including only two events; if there are 
more than two events, all possible relationships and 
impacts should be determined. All probabilities of the 
occurrence of a particular event are captured in 
a cross-impact matrix.

A cross-impact model aims at producing scenarios. 
It is necessary to employ statistical software, 
because ‘in a model with n events 2n possible 
scenarios are generated, each differs from all the 
others in the occurrence of at least one event. For 
example, if there are 10 events to be considered, 
there are 1024 possible scenarios to estimate’ (27). 
The output from the statistical software is usually a 
list of scenarios ordered from the most probable to 
the least probable. Generated scenarios need to be 
interpreted and referred back to the original set of 
events (28).

Table 9.  Pros and cons of cross-impact analysis

Advantages

Disadvantages

• 

• 

• 

forces attention into chains of causality; a affects b,  
b affects c
estimates dependency and interdependency among 
events
clarifies and increases knowledge of future 
development

• 
• 

• 

limitation in the number of included events
difficult to understand consistency and validity of the 
technique
difficult to explore the future of a complex system 
with a limited number of hypotheses

Source:  JRC-IPTS, For-Learn online foresight guide: methodology: main methods: cross-impact analysis 

http://forlearn.jrc.ec.europa.eu/guide/4_methodology/meth_cross-impact-analysis.htm [accessed 
13.5.2014].

(26) Ibid.
(27) IFP, Cross-impact analysis http://www.foresight-platform.eu/community/forlearn/how-to-do-foresight/methods/analysis/cross-impact-analy-

sis/ [accessed 13.10.2014].

(28) JRC-IPTS, For-Learn online foresight guide: methodology: main methods: cross-impact analysis http://forlearn.jrc.ec.europa.eu/guide/4_

methodology/meth_cross-impact-analysis.htm [accessed 13.5.2014].

51

Developing skills foresights, scenarios and forecasts2.2.3.  Normative methods
2.2.3.1.  Backcasting

This is one of the most used normative methods 
and is employed in complex situations where there is 
a normative objective and fundamentally uncertain 
future events that influence these objectives. 
Backcasting defines a desired future and then works 
backwards to identify the major events and decisions 

that generated the future, to consider what actions, 
policies and programmes are needed today that will 
connect the future to the present. Backcasting 
reminds participants that the future is not linear, and 
can have many alternative outcomes depending on 
decisions made and the impact of external events 
on an organisation (Jackson, 2013). The backcasting 
method is shown in Figure 8.

Figure 8.  Scheme of the backcasting method

Source:  Matthew (2008).

52

Guide to anticipating and matching skills and jobsAs Figure 8 shows, backcasting comprises several 
actions needed to achieve a desirable output. 
According to Wilson et al. (2006) backcasting consists 
of four steps: 

(a)  defining the problem context. This ‘involves 

bounding the problem context, setting normative 
assumptions, identifying stakeholders, considering 
scale issue and so on’;

(b)  characterisation of desirable futures. This focuses 
on construction of visions of desirable futures. 
These futures reflect the values and wishes of the 
stakeholders;

(c)  backcasting trajectories from future to present. 
As soon as the desired futures are designed, 
trajectories are backcast from the future to the 
present. ‘Backcast trajectories are typically 
described in terms of first-order economic, social, 

technological and institutional milestones and 
changes’;

(d)  identifying interventions to initiate trajectories. 

‘Backcasting provides a framework for identifying 
the interventions or actions [policy and strategy 
measures] required to implement the trajectories 
which would lead to the desirable future’ 
(Wilson et al., 2006, p. 144).

It is important to involve stakeholders at an early stage in 
the process and develop a future long-term vision of the 
desired scenario. Once the collective desirable vision is 
developed, alternative paths to achieve it are proposed 
and scrutinised in terms of potential advantages, 
disadvantages, bottlenecks and problems. Stakeholders 
then select one pathway, formulate an action plan 
defining their roles, and commit to them. Box 7 shows 
how backcasting has been used in practice in the UK. 

Box 7.  Backcasting: example of the UK

In 2006 the Tyndall Centre for Climate Change Research in the UK used a backcasting approach within the project 
Decarbonising modern societies: integrated scenarios process and workshops. This project focused on creating 
strategies ‘to reduce UK CO2 emissions by 60% by 2050 relative to 1990, a goal adopted by the UK Government. 
The study took a backcasting approach which has been applied in two stages’ (p. 1). Before applying backcasting, 
brainstorming was used with the aim of generating ‘a list of issues which […] would drive the future of the UK’s 
energy system to 2050’ (p. 13).
‘Then a set of credible and consistent end points describing the energy system was devised, outlining alternative 
visions of a substantially decarbonised society in 2050. […] These end points were used as the basis for 
a backcasting workshop where experts and stakeholders articulated pathways for the transition to the defined 
futures’ (p. 1). 
‘Stakeholders were recruited from the policy community and from those with expertise in policy formulation and 
implementation. The backcasting was structured into a series of steps so that participants initially thought about 
the critical factors required for a particular end point to be achieved and subsequently elaborated these to define 
how they might be achieved. The project team took the output from this workshop and combined the end points 
and pathways to form the Tyndall integrated scenarios’ (p. 4).
There were ‘five detailed scenarios which described the transition from the present day to the contrasting end 
points. The scenarios outline demand and supply side characteristics and policy steps through which change has 
been brought about in the most important sectors’ (p. 4). 
Developed scenarios described ways to reach future end points, and also dealt with the impact of particular 
scenarios and related changes on employment and future skill requirements. It is clear that each scenario 
contained a different impact on the labour market, and thus on the skills and competencies required in the future.

Source:  Anderson et al. (2006).

53

Developing skills foresights, scenarios and forecastsTable 10.  Pros and cons of backcasting

Advantages

Disadvantages

• 
• 
• 

• 

lightweight and creative
avoids extrapolating present conditions
the proactive character of backcasting – shaping the 
future by designing the action today
accessible and engaging

assumes the desirable future will occur

• 
•  may need constant updating
can be resource intensive
• 

The main outputs of backcasting are future visions and 
ways to achieve them, discussed and agreed among 
key stakeholders.

system, product or process is considered as 
a compound of attributes. New ideas are found 
by searching the matrix for new combinations 
of attributes that do not yet exist’ (31).

2.2.3.2.  Morphological analysis

Morphological analysis can be based on five steps: 

‘Morphological analysis [… is] a normative method, 
which starts with future needs or objectives, and then 
seeks to identify the circumstances, actions, 
technologies, etc., required to meet them’ (29).

The purpose of morphological analysis is to organise 
information to solve a problem or stimulate new 
ideas. It has widespread use for new product 
development but it is particularly useful in foresights 
for building scenarios (30). ‘Morphological analysis is 
a method, which breaks down a system, product 
or process into its essential subconcepts, 
each concept representing a dimension in 
a multidimensional matrix. In this context every 

(a)  defining and formulating a problem; 

(b)  defining and analysing the parameters that might 
be of importance for the solution of the given 
problem; 

(c)  constructing a multidimensional matrix with 

possible solutions; 

(d)  evaluating the outcome based on feasibility and 

achievement of desired objectives;

(e)  analysing the best solutions being selected and 
applied, provided the necessary means are 
available (32).

(29) JRC-IPTS, For-Learn online foresight guide: methodology: main methods: morphological analysis and relevance trees http://forlearn.jrc.

ec.europa.eu/guide/4_methodology/meth_morpho-analysis.htm [accessed 13.5.2014].

(30) Ibid.
(31) DIEGM, University of Udine, Morphological analysis http://www.diegm.uniud.it/create/Handbook/techniques/List/MorphoAnal.php
(32) JRC-IPTS, For-Learn online foresight guide: methodology: main methods: morphological analysis and relevance trees http://forlearn.jrc.

ec.europa.eu/guide/4_methodology/meth_morpho-analysis.htm [accessed 13.5.2014].

54

Guide to anticipating and matching skills and jobsTable 11.  Pros and cons of morphological analysis

Advantages

Disadvantages

• 

• 

• 

can discover new relationships, which may not 
be so evident
encourages the identification and investigation of 
boundary conditions
systematic analysis of future structure of the system

•  may be too structured and that could inhibit creative 

thinking

•  may yield too many possibilities
• 

human error: method requires critical judgement

2.2.3.3.  Roadmapping

The term ‘roadmapping’ (usually understood in terms 
of technology roadmapping) refers to various kinds 
of foresight studies including visions and detailed 
projections of possible future developments. It is 
a normative method that aims to look at the future for 
a chosen field and to seek the most important drivers 
of change in that field (33). It provides inputs for the 
formulation of policies and strategies (UNIDO, 2009). 

There are many approaches to roadmapping, although 
generally the effort is a graphical depiction that 
provides a strategic view of the given subject (Phaal 
and Probert, 2009). This depiction can be used by 
presenting the way forward and informing decisions 
about possible future options. An example of visual 
representation, comprising a multilayered, time-based 
chart, bringing together various perspectives, is shown 
in Figure 9.

Figure 9.  Graphical representation of technological roadmapping

Source:  EIRMA (1997).

(33) Ibid.

55

Developing skills foresights, scenarios and forecastsRoadmaps can be considered as dynamic business 
or system frameworks, which enable the evolution 
of systems to be explored and mapped, supporting 
innovation and strategy development and deployment 
at all levels (UNIDO, 2009). The different ‘ways’ in 
roadmaps are designed by experts. 

Even though roadmapping is usually used in 
technology foresight, it can also be applied when it 
comes to other research areas or issues, linked 

directly to questions that are applied in any strategic 
context:

•  Where do we want to go? Where are we now? 

How can we get there?

•  Why do we need to act? What should we do? 

How should we do it? By when?

The approach is quite flexible and can be adopted to 
suit a wide range of goals and context (UNIDO, 2009).

Table 12.  Pros and cons of roadmapping

Advantages

Disadvantages

• 

• 

• 

graphical representation is an effective way 
to demonstrate relationships
a means to develop consensus about a set of needs 
and steps required to satisfy those needs
can help to identify key elements within a complex 
system

• 

• 

• 

due to the broad complexity of the issue 
roadmapping can be challenging
no single format is suitable for all situations: the 
approach has to be customised
it is a well-structured method which does not allow 
for large participation

2.3.  Conclusion: how to choose a method, 

or methods

‘The variety of foresight experiences demonstrates 
that no one method or structure is best: the choice 
must reflect both fitness for purpose and the national 
culture in which it is situated’. According to Georghiou 
et al. (2008), any foresight exercise ‘is neither a single 
approach to a single problem nor a panacea for all 
national problems’ (Georghiou et al., 2008). 

Key questions to meet the objective(s) of the 
foresight are ‘How to choose proper foresight 
methods?’ and ‘How to combine foresight 
methods?’. Choosing an appropriate method or, 
better, a combination of methods is a challenge. 
According to Jackson (2005) the selection of 
foresight methods should match the project’s 
objectives: the desired project outcomes and the 
information needs of stakeholders. A combination 
of methods is usually used in foresight exercises, 

quite naturally given that some methods can relate 
to each other and are used in sequence. According 
to Keenan combining foresight methods should be 
based on these criteria: 

(a)  ‘available resources (time, money, expertise, etc.); 

(b)  nature of desired participation; 

(c)  suitability for combination with other methods; 

(d)  desired outputs of a foresight exercise; 

(e)  quantitative/qualitative data requirements of 

methods; 

(f)  methodological competency (34) (Keenan, 2006, 

slide 4).

The cultural, economic, political, social and institutional 
contexts play a crucial role and have to be taken into 
consideration as well. Table 13 summarises all foresight 
methods and describes some of their specifics.

(34) The terms competency(ies) and competence(s), although slightly different in meaning, are used interchangeably throughout this publication.

56

Guide to anticipating and matching skills and jobsTable 13.  Foresight methods

Method

Type

Country 
examples*

Suitability for 
skill needs 
anticipation**

Backcasting

Normative

–

+++

Brainstorming

Supplementary

Japan, the US

++++

Cross-impact 
analysis

Exploratory

–

++

Delphi method

Exploratory

Expert panel

Exploratory

Brazil, Germany, 
Finland, Japan, 
Korea

+++++

Brazil, Canada, 
Germany, Finland, 
Japan, Korea

+++++

Usually used 
together with

Important features 
of method

Literature and 
statistics review

Provides a clear path 
forward

Expert panel, 
Delphi method

Can reveal unexpected 
developments

Literature and 
statistics review, 
Delphi method

Evaluates the 
probabilities of the 
occurrence of a set of 
events

Literature and 
statistics review, 
brainstorming, 
scenarios

Good for spotting 
the unexpected, and 
for engagement of 
stakeholders

Scenarios, 
brainstorming, 
SWOT analysis

Eliciting expert 
knowledge, helping to 
identify priorities

Focus group

Supplementary

–

++++

Scenarios

Exploratory

the UK

+++

Scenarios

Horizon 
scanning

Literature 
and statistics 
review

Morphological 
analysis

Supplementary Korea

++++

Normative

–

++

Scenarios

Exploratory

Brazil, Germany, 
Japan, Korea, 
the UK

+++++

S&T 
roadmapping

Normative

Russia

+++

SWOT analysis Supplementary

–

++++

Improving or 
generating ideas

Identifying future 
challenges and trends

Evidence-based

Breaks down a 
system and identifies 
important factors

Good for spotting 
the unexpected, and 
for engagement of 
stakeholders

Provides a clear path 
forward

Lists factors with 
impact on issue

Scenarios, 
backcasting, 
Delphi method

Scenarios

Literature and 
statistics review, 
SWOT analysis, 
science and 
technology 
roadmapping

Scenarios, 
brainstorming, 
expert panel

Scenarios, expert 
panel, Delphi 
method

NB: 

* Reference from case studies; ** the more +, the more suitable the method is for anticipation 
(maximum five+).

Source: Authors.

57

Developing skills foresights, scenarios and forecastsA survey by the European foresight monitoring network 
mapped 866 different foresight exercises (European 
Commission, 2009) and examined how various 

techniques were combined in practice. The outcome of 
this survey is summed up in the methods combination 
matrix (MCM), shown in Table 14.

Table 14.  Methods combination matrix (MCM) 

Ranking by frequency of use

1

2

3

4

Methods Combination Matrix 
(MCM)

d
n
a

e
r
u
t
a
r
e
t
i
L

i

w
e
v
e
r

s
c
i
t
s
i
t
a
t
s

l

s
e
n
a
p
t
r
e
p
x
E

Literature and statistics 
review

477

H

Expert panels

VH

440

1

2

3

Scenarios

4 Brainstorming

5 Delphi method

6

SWOT analysis

7 Roadmapping

8 Backcasting

9 Cross-impact analysis

10 Morphological analysis

H

VH

VH

VH

VH

H

VH

VH

H

VH

VH

H

VH

H

VH

VH

6

i

s
s
y

l

a
n
a

T
O
W
S

7

8

i

g
n
p
p
a
m
d
a
o
R

g
n
i
t
s
a
c
k
c
a
B

5

d
o
h
t
e
m

i

l

h
p
e
D

M

i

g
n
m
r
o
t
s
n
a
r

i

B

M

M

M

169

M

M

H

H

M

VH

VH

137

M

101

72

M

M

VH

M

47

M

s
o
i
r
a
n
e
c
S

H

M

372

H

M

H

M

H

VH

VH

9

i

s
s
y

l

a
n
a

t
c
a
p
m

i
-
s
s
o
r

C

M

36

H

10

i

s
s
y

l

a
n
a

l

l

i

a
c
g
o
o
h
p
r
o
M

M

21

NB: 

(VH) very high, (H) high, (M) moderate, (blank) low. 
Focus groups and horizon scanning were not included in the MCM.

Source: Adapted from Popper (2011).

58

Guide to anticipating and matching skills and jobs 
 
 
 
 
 
 
 
 
The diagonal shows the total number of times the 
method was used in the sample of 866 cases. 
The methods most used in foresight exercises are 
literature and statistics reviews, expert panels and 
scenarios. ‘The outcome of this operation shows in 
each cell the proportion in which two methods are 
combined with respect to the number of times the 
method on the row was used. Nevertheless, to present 
the results in a more “digestible” way, the following 
categories have replaced the percentages: “blank” for 
low combinations (i.e. figures below 19%); “M” for 
moderate combinations (i.e. 20-39%); “H” for high 
combinations (i.e. 40--59%); and “VH” for very high 
combinations (i.e. figures above 60%)’ (Popper, 2011, 
p. 24). Although, only some of the quoted cases were 
for anticipation of skill needs, the matrix shows which 
methods are combined with others more generally.

59

Developing skills foresights, scenarios and forecastsChapter 3. 
Key steps in implementing foresight programmes

3.1.  Define foresight area to be considered

Defining the focus of the foresight exercise is essential 
before starting. It usually tackles one core issue but 

different types of focus may coexist (e.g. future skills 
shortage in a selected sector). 

Box 8.  Finding research fields and topics: BMBF foresight in Germany 

The BMBF foresight is a strategic instrument of the Federal Ministry of Education and Research (BMBF) that 
provides technology foresight with a time horizon of approximately 10 to 15 years. It is characterised by combining 
two approaches in a cyclic process model: a cycle strongly influenced by the technology push approach is 
followed by a cycle that is mainly demand pull orientated. One cycle lasts about two years and leads to a 
long-range look into the future needed for early agenda-setting and prioritisation in German research and 
innovation policy, including prospects for change in the education system. Research fields defined by the 
high-tech strategy and by other foresight activities of the Ministry are being examined and refined within this 
cyclic process. 
The process in each cycle is carried out in several phases: search and analysis, transfer, and preparation of the 
next cycle. Cycle 1 started in 2007 by mapping 17 proposed fields. They were discussed by experts at  
workshops, followed by initial reclustering, an online survey and second reclustering. By mid-2009, ‘future topics’ 
were identified (neurosciences, optical technologies, water infrastructures) along with seven ‘new future fields’ 
(including production consumption 2.0, sustainable energy solutions). In cycle 2 (since 2013) broad trends in 
demand pull will be determined and evaluated. Results from research in social sciences, humanities, political 
sciences and economics will be included along with the results from interviews with lead users and people who 
exhibit a particular openness to sociocultural changes.
On the basis of this cycle, hidden trend developments may be added into the analysis which also extends to 
education policy, skills and labour market changes. The results of cycle 1 and cycle 2 will be updated, combined 
and linked to scenarios to point out future areas within research and science which have the greatest solution 
potential to be considered in Ministry policy. 

Source:  Cuhls et al. (2009). See also the case study in the annex.

60

Guide to anticipating and matching skills and jobsIdeally the focus should be discussed and defined 
together with main sponsors, implementers and 
participating partners. This is a useful time investment 
in managing expectations and to avoid potential 
disagreements on desired outcomes. Formulating a 
guiding question for the foresight exercise may be 
useful (35). 

3.2. 

 Clarify the purpose of the  
foresight exercise

The first step when thinking about a foresight 
programme is to see if it can provide the kind of 
information sought and fulfil expectations: what role 
can a foresight exercise play in meeting key social 
challenges for stakeholders? The main motive for 
foresight should be enriching the understanding of 
likely future circumstances and potential strategies to 
bring about a desired future. Foresight generates 
shared visions. Some national exercises have 
succeeded in achieving widespread consensus on 
these visions but consensus is difficult to achieve in 
areas where there are profound underlying 
disagreements. Further, employing foresight activities 
may not always be appropriate. If the purpose is to 
bring quantifiable knowledge of future skill needs, 
foresight is perhaps not suitable (36); projections or 
extrapolating activities can produce desired outputs 
provided robust data are available.

Although the foresight process has the value of making 
stakeholders interact and discuss, it is not an end in 
itself: implementing a foresight exercise can only be 
justified by its desired impact. Whether the latter can or 
cannot be achieved depends not on the quality of 
foresight but on many conditions beyond the scope of 
the foresight team. It is necessary, therefore, before 
committing time and resources, to undertake a 
feasibility check on whether these conditions will allow 
the desired impact to be achieved beyond the foresight 
project life. 

3.3. 

 Clarify the key programme  
design elements

Once the focus of the foresight exercise is clear and 
the decision to proceed is taken, a number of 
important issues need to be planned: objectives, 
expected outcomes, foresight time horizon, partners, 
stakeholders, participants, scope, methods and 
formats to apply, and time and resources to allocate for 
the exercise. 

Ideally, objectives should be clearly formulated but, 
unlike the foresight focus, do not have to be too 
specific, at least at the initial stage of the process. 
Gaining as broad support as possible early on is 
important but, again, managing expectations is 
necessary to avoid promising too much to too many 
players (37). The general objectives of foresight 
comprise four main activities:

(a)  stimulating common discussion about the future 

and support for long-term thinking;

(b)  collecting and combining widespread information 

by gathering important protagonists and improving 
shared knowledge in consequence;

(c)  supporting creation of networks and stimulating 

common action;

(d)  providing information and improving the quality of 

present-day decisions.

There is no single objective in foresight activities. The 
main output of foresight is to influence policies to 
maintain quality of life and social and political cohesion. 
This can be achieved only through accomplishing 
several partial objectives. Foresight should tend to set 
priorities and create ‘visions’, gather a range of 
opinions from stakeholders, enhance networking and 
provide information. 

(35) JRC-IPTS, For-Learn online foresight guide: major decisions: defining the focus http://forlearn.jrc.ec.europa.eu/guide/3_scoping/dec_focus.

htm [accessed 14.5.2014].

(36) Part B deals with quantitative forecasts.
(37) JRC-IPTS, For-Learn online foresight guide: scoping an exercise http://forlearn.jrc.ec.europa.eu/guide/3_scoping/index.htm [accessed 

14.5.2014].

61

Developing skills foresights, scenarios and forecastsCare in determining scope helps achieve the objectives 
of the foresight exercise. Defining the scope means 
choosing the appropriate level of intervention and 
relevant topics, and combining them with the agreed 
focus into a coherent design for the exercise. The case 
of Russia (see annex) demonstrates that the scope of 
the project Skills 2030 foresight was defined as the 
sectors where technology has been the primary driver 
of change in skills demand. The topics focus on 
working tasks and related skills.

Key stakeholders need to be identified and involved. 
Stakeholders involved in foresight are groups of 
people that affect, are affected by, or have an interest 
in particular foresight activity. One apparent, although 
not always mandatory, stakeholder is the sponsor of 
the foresight. Other stakeholders may include direct 
and indirect users and beneficiaries of the foresight 
results, who should all be considered in the design of 
the foresight exercise to maximise its benefits. 

Mapping out the stakeholders includes considering 
the degree of their interest in particular issues. The 
different foci of a foresight exercise help to define the 
format of the desired outcome in relation to possible 
users. Stakeholder groups can help to define the 
outcomes being aimed for. Typical stakeholders in 
skills foresight are government and its bodies 
(ministries, agencies) at national/regional/local level, 
employers and their bodies, trade unions, research 
centres and specialised institutions, public 
employment services, and education and training 
providers. Skills foresights include elements of social 
dialogue between government and the private sector 
in its core activities. More information on 
stakeholders to be involved in skills anticipation and 
matching activities are provided in Volume 1. For work 
on the sectoral level, the involvement of stakeholders 
and the role of social dialogue are discussed in-depth 
in Volume 3.

Box 9. 

Involving stakeholders

The composition of participants will depend upon the orientation of foresight activities; several practical 
approaches can be used to identify appropriate individuals. One of the main aims of foresight activities is to 
ensure the maximum involvement of leading players because this will help determine the final outcome, whether 
foresight is focused on formal activities, or on the activation of learning processes and development of specific 
skills. The correspondence between foresight objectives and stakeholder needs depends greatly on the number 
of key players involved and their effective participation, as well as their ability to intensify relationships of 
exchange within the issue and with regard to external contexts. 
The success of the foresight programme – and thus its ability to attract sponsors, engage stakeholders, and put 
the results to effective use – will depend on its ability to mobilise sources of energy. As the effectiveness of each 
foresight activity is strongly influenced by the number of key players involved and their degree of participation, in 
order to avoid casual or misguided choices, the identification of these players must follow in-depth analysis of the 
local system and refinement of the general objectives of foresight activity.
Participants must be identified according to the focus of the foresight activity, which will, in turn, be dependent on 
the objectives of the initiative and country circumstances. It is necessary to consider the main components of the 
system: objectives focusing on improvement of the training system will involve leading public decision-makers at 
both local and national level, as well as the public and private training supply systems, and players representing 
the demand for vocational skills.  

62

Guide to anticipating and matching skills and jobsThree ways to recruit members and participants

Arguments to encourage participation in the exercise should emphasise the potential benefits of establishing 
foresight activities. They should primarily focus on the types of benefit to be gained from the setting up of a 
foresight activity. Such benefits can be divided into three types: 
• 

‘entry point’ benefits connected to the preparation of specific programmes for submission to national and 
European financing. In this case, both local institutions and the various members of the training and social 
systems will have specific interests in defining projects and programmes to attract resources into the system;

•  benefits connected to long-term objectives, aimed at improving performance of the system. These objectives 

depend on the structural features and may address aspects such as general economic development, 
improvement of sector competitiveness, and greater dissemination of technologies in production and social 
systems. Obviously, the most suitable arguments for involving players will depend on the objectives identified 
and the relative benefits. For example, in the case of a foresight activity aimed at achieving better skills 
matching by adjusting education and training provision, the arguments to be used with public authorities 
should revolve around improvements in terms of public budgets (less unemployment and related social 
problems). Citizens, on the other hand, should be made aware that they will benefit from such improvements; 
arguments might focus on the benefits from making better informed choices about a future career. Employers 
might benefit from the better availability of a ready workforce;

•  benefits connected to the creation or strengthening of harmony between players. In this case, the arguments, 
especially those aimed at political decision-makers and employers and entrepreneurial associations and trade 
unions, should highlight the potential impacts of foresight activity. These could include improved interrelations, 
cohesion and the generation of a shared vision between players. This type of benefit also has a direct effect 
on the advantages to be gained from the foresight activity by individual participants. For example, individual 
enterprises can improve their knowledge of the technological and economic scenarios in which they compete, 
while individual citizens can increase their preparedness for new technologies and jobs, and so improve their 
quality of life.

63

Developing skills foresights, scenarios and forecastsProject management practice can help to prioritise stakeholders: there may be a long list of people and 
organisations that are affected by the foresight exercise. The power/interest grid shown below classifies key 
players by their power over and interest in the programme. 

The power/interest grid

Source:  Kot, Requirements technique: stakeholder power/interest analysis: http://requirementstechniques.

wordpress.com/stakeholder-analysis/stakeholder-powerinterest-analysis/ [accessed 25.6.2014].

The positions on the grid suggest action to be taken:
•  high power, interested players: these must be fully engaged and the greatest efforts have to be made to 

satisfy them (sponsors, government representatives);

•  high power, less interested players: enough effort has to be made to keep them satisfied;

• 

• 

low power, interested players: they should be kept adequately informed about any major issues that may arise; 

low power, less interested players: they should be monitored but not burdened with excessive 
communication.

Source:  JRC-IPTS et al. (2001).

64

Guide to anticipating and matching skills and jobsThe next step involves translating the defined 
objectives into specific results expected to be achieved 
through the foresight exercise. Such results could be 
tangible, such as reports, books, websites, 
institutionalised networks, strategic documents, or 
intangible, related to the process itself, such as 
informal networking, consensus on issues discussed, 
collaboration among stakeholders and social dialogue. 
Expected outcomes from the foresight exercise have 
to be clearly defined and related to specific groups of 
stakeholders: part of this process is describing the 
outcomes in language relevant to the audience (38). 

Defining a time horizon is the next step: this has to be 
done taking into account the subject of the foresight 
and its dynamics. Many methods only work for certain 
time horizons and so the decision on how long into the 
future the foresight should look has to be taken before 
selecting methods (39).

3.4. 

 Clarify key questions and the  
way to find answers

Selecting an appropriate methodology should be done 
early in the foresight design process. The chosen 
methodology should be problem-solving driven and 
result-oriented. It should also take into account 
feasibility of the implementation and resources needs/
constraints. The resources include availability of time 
including that of participating stakeholders, money, 
expertise including that of a facilitator, venue, 
equipment and access to data. These considerations 

should help in the selection of a set of methods to be 
applied at different stages of the process in the 
appropriate sequence, taking into account their 
suitability for combination with other methods, the 
desired outputs of the foresight exercise, quantitative/
qualitative data requirements of the methods and 
methodological competency (Keenan, 2006). The 
selected sequence of methods will normally evolve 
and might be altered or refined throughout the 
process. The methods have to be discussed with the 
sponsor, the team and key stakeholders (40).

3.5.  Manage the foresight exercise

The foresight implementation plan has to include a 
number of organisational issues, such as forming the 
implementation team and assigning roles and 
responsibilities to its members, setting up a steering 
committee, outlining a communication strategy, 
estimating and securing financial and non-financial 
resources, contracting external collaborators, and 
forming expert working groups if necessary. 

Once these design issues are resolved the programme 
can be launched. Managing the foresight exercise 
poses a number of challenges. Due to the highly 
participatory and interactive nature of the approach, 
continuous adjustment of the process without 
jeopardising the overall outcome is a challenge. Other 
issues are managing time and people, including 
relationships with the sponsor and the stakeholders, 
and the engagement of participants (41).

(38) JRC-IPTS, For-Learn online foresight guide: major decisions: defining the outcomes http://forlearn.jrc.ec.europa.eu/guide/3_scoping/dec_

outcomes.htm [accessed 14.5.2014].

(39) JRC-IPTS, For-Learn online foresight guide: major decisions: defining the time horizon http://forlearn.jrc.ec.europa.eu/guide/3_scoping/

dec_time-horizon.htm [accessed 14.5.2014].

(40) JRC-IPTS, For-Learn online foresight guide: methodology: methodological framework http://forlearn.jrc.ec.europa.eu/guide/4_methodology/

framework.htm [accessed 14.5.2014].

(41) JRC-IPTS, For-Learn online foresight guide: running an exercise http://forlearn.jrc.ec.europa.eu/guide/3_running/index.htm [accessed 

14.5.2014].

65

Developing skills foresights, scenarios and forecastsBox 10.  Foresight programme team skills

Carrying out a foresight exercise requires appropriate skills among members of the foresight implementation 
team. Their competency profiles and prior foresight experience are critical. Therefore, selection of the project 
team and division of roles and responsibilities need careful consideration.
Many actors are involved in a foresight exercise: universities, businesses, chambers of commerce, media, 
industry associations, sponsors, experts, NGOs. Foresight exercises can potentially involve dozens of participants 
from a wide variety of organisations and backgrounds. Identifying participants depends on the orientation of the 
foresight exercise. Objectives focusing on anticipation of skill needs will involve political representatives, public 
decision-makers, employers and their associations, research centres and specialised institutions, public 
employment services, trade unions and education and training providers. 
Depending on the objectives of the foresight exercise and on the methods employed – a number of competencies 
are required during the process. It may be that the right people with the right competencies are not available 
within the foresight team (for example facilitators for workshops, rapporteurs for panel discussions, designers for 
online surveys, foresight experts). There are strategies for solving this problem: by training the project team, by 
bringing external competencies into the project for specific tasks or by outsourcing parts of the project (e.g. using 
professional consultancy for facilitating discussions). It is recommended to think carefully from the start about 
what competencies will be needed during the foresight process and what strategies are needed to recruit the 
right people. 
Special attention should be given to foresight experts and their skills and competencies. Especially experts with 
knowledge of specific foresight methods are rarely available from local sources; often they are not available even 
in the country. In practice, accessible local experts use methods they are familiar with or which are fashionable, 
but not in correspondence with the objectives of the foresight exercise. This can cause serious problems in 
achieving the foresight objectives. In such cases it is necessary to recruit experts from abroad and use their 
expertise and knowledge. 

Sources:  Case studies (see annex); JRC-IPTS, For-Learn online foresight guide: methodology: methodological 
framework http://forlearn.jrc.ec.europa.eu/guide/4_methodology/framework.htm; scoping an exercise: 
setting up: team member profiles http://forlearn.jrc.ec.europa.eu/guide/3_scoping/set_team_profiles.htm;  
[links accessed 14.5.2014].

66

Guide to anticipating and matching skills and jobsImplementation of a foresight programme consists of 
two phases. The first phase incudes collection, 
collation and summary of available information. 
Collection of ‘information on future themes, trends, 
ideas, early signs, etc., is gained from a wide range of 
sources such as experts, universities, business 
networks, personal networks, the literature, 
government, other foresight reports, research and 
surveys by means of different methods such as Delphi, 
surveys, systematic reading, or brainstorming 
sessions’. Collation and summarisation give the 
information a structure and form and its volume is 
reduced. ‘Again, there are general methodologies and 
processes available, such as scenario building, list 
writing and prioritising, graphical comparisons, 
[matrix production], and cross impact analysis’ 
(Horton, 1999, p. 2).

Actual foresight comprises the translation and 
interpretation of this information to produce an 
understanding of its implications for the future from a 
specific point of view (such as for education and 
training). It involves activities, tools and skills, as well 
as people to do the translation and interpretation work 
for education, training and skill needs and to assess 
what can be done about it today. This is the value- 
added phase, generating an understanding of what can 
(or cannot) be done for the future. 

3.6.  Ensuring the use of results 

The life of foresight does not stop at the end of the 
implementation of the foresight activity itself; it 
includes tasks related to the use and dissemination of 
the results, their evaluation, and reflection on the 
lessons for future foresights (42). 

Often, insufficient attention is given to follow-up, and 
recommendations produced never get to the 
implementation stage. The key to success is to include 
implementation into the foresight implementation plan 
and treat the task as part of the exercise. This does not 
mean that the foresight implementation team will have 
to implement the strategy that results from the 
foresight themselves. They may initiate broader 
collaboration, engage other institutions and delegate to 
other authorities, but the plan for the foresight afterlife 
has to be in place.

The responsibility of foresight programme 
implementers does not usually go beyond the delivery 
of programme outcomes to stakeholders or end users. 
Stakeholder expectations should be continuously 
managed during the programme so that their needs 
are met adequately. Benefitting from programme 
results is a matter of stakeholder engagement and 
ownership over the programme. Foresight 
programmes that have highly engaged stakeholders 
with strong ownership will more likely result in 
follow-up actions that will implement foresight 
outcomes. But these activities are usually ‘out-of-the-
scope’ of the foresight exercise. 

Evaluation has to be an integral part of foresight 
exercises. Evaluations ideally look not only at products, 
results and outcomes but also processes. Evaluating 
outcomes is essential for accountability to client and 
users, whereas evaluating processes is important for 
future foresights (43). Understanding what worked and 
what has to be changed will help to draw lessons and 
share good practices, with a view to systematising 
foresight activities. 

(42) JRC-IPTS, For-Learn online foresight guide: follow-up of the exercise http://forlearn.jrc.ec.europa.eu/guide/6_follow-up/index.htm [accessed 

14.5.2014].

(43) Idem.

67

Developing skills foresights, scenarios and forecastsChapter 4. 
Adapting foresighting to local needs

4.1.  Key context factors

This guide argues that rather than presenting a ‘one 
size fits all’ approach universally or within a group of 
countries, it is more convenient to consider 
opportunities and barriers that arise from country 
specifics. Reviewing the cases, a number of general 
circumstances that have influence on foresight 
activities have been identified. The context factors 
provide a framework for the application of any foresight 
approach and need to be considered at national level. 
Understanding them helps in making better strategic 
decisions about foresight activities. 

The list of following themes is not exhaustive but 
indicative of the areas where initial assessment is 
useful when thinking of developing or adopting a 
foresight approach. Though considered separately, 
these areas are closely interrelated.

4.1.1.  Country size

Country size, represented by the number of inhabitants 
or members of the workforce, influences the scope of 
foresight activities. For example, the population of 
Brazil represents two-fifths of the whole EU population, 
making foresight activities at national level 
incomparable with small European states which are 
the size of Brazilian states. The size is reflected in the 
complexity of the management of foresight activities 
(such as the size of expert panels). The country area 
also matters. As seen in the Brazilian case (see annex), 
spatial skills mismatch is an issue that calls for 
attention. Small countries may also find some national 

foresight approaches difficult to apply because they 
may be too closely linked to their bigger neighbours (so 
that skills gained are used elsewhere due to cross-
border commuting).

4.1.2.  Economic and social context

Developing or transition countries have several 
common features related to their level of social and 
economic development, so the main skills issues may 
vary from developed countries. As demonstrated in the 
Brazilian case, illiteracy and basic education quality can 
undermine efforts to develop and better match skills 
with the labour market.

Economic growth is important but does not necessarily 
make foresights easier to implement, influencing as it 
does context factors (such as political and institutional 
stability). An unfavourable economic situation may 
disconnect new technologies from the main social 
problems of a country and polarise the workforce. The 
Brazilian case shows (see annex) that social progress 
and increased social inclusion linked with good 
economic conditions (reduction of poverty, informality, 
inequality) directly affects the number of workers 
included in government-monitored activities and makes 
foresight exercises more valuable.

There is a link between the level of development and 
foresight experience in countries. From the cases 
considered here, Japan, Germany and South Korea 
have gathered most foresight practice and can provide 
more examples on the use of specific methods. The 
relevance of the foresight methodology to other 

68

Guide to anticipating and matching skills and jobscountries, including transition and developing 
countries, is high but the feasibility of applying specific 
methods must always be carefully considered (taking 
into account for instance the existing statistical 
infrastructure or availability of expertise and foresight 
competencies). 

4.1.3.  Political stability and culture

Developing foresight culture or carrying out a 
meaningful foresight exercise requires an environment 
in which a certain degree of continuity of public 
processes and policies is allowed. Instability of public 
institutions due to the changing focus of political 
leaders and governments is a risk element for 
developing foresight activities and for their success.

Success of foresight activities requires a degree of 
forward-looking thinking by political leadership. Many 
countries where reactive short-term thinking 
penetrates all areas of public administration may find it 
difficult to start developing a foresight system that by 
its nature serves mid- or long-term goals. 

The perception of the necessity of change is an 
important policy aspect. In countries where substantial 
political or cultural changes are common, foresight is 
more likely to become part of the policy response 
since it helps to guide today’s decisions for the desired 
future.

4.1.4. 

 Openness and vulnerability to  
external changes

Generally, smaller countries tend to be more open and 
vulnerable to external changes. Economic structures 
and labour market adjustment are more prone to global 
trends. However, the degree of openness can be 
influenced by political decisions, as in the 1990s when 
Brazil embarked on an outward-looking development 
strategy and opened up its economy. Other countries 
may be satisfied with more inward-looking strategies, 
sticking to the low-skill equilibrium where little pressure 
is put on increasing skills. In such cases foresight 
interventions may be pointless. 

4.1.5.  Cultural context

Different cultures (western, eastern, regional, 
institutional) and their values can affect the way 
foresight is developed and implemented (such as the 
transferability of some practices). Differences in the 
operation of the labour market (key actors, hiring and 
firing regulations, level of informality, working 
conditions) or the mode of acquisition of skills 
(education systems and values) influence the choice of 
approaches. 

69

Developing skills foresights, scenarios and forecastsBox 11.  Cultural context in Japan

Japan, during long-term use of foresights, developed a considerable ‘foresight culture’. Foresight and its results 
become widely known and used for various purposes at all levels (from national policies to individual institutions, 
including stakeholders in R&D and education).
Japan has built its economic success predominantly on a skilled population, government-industry cooperation and 
intensive science and technology development, mainly within technology-based industries. It has a culture of 
employees staying with one employer for a long time, if not for life, which can result in a relatively less flexible 
labour market and education system. Individual companies are motivated to invest in the development of their 
workforce and draw benefits from various foresight exercises.
Foresight methodology choices are related to the Japanese cultural context. Anonymous methods such as Delphi, 
where the respondents’ answers cannot be influenced by factors such as deference to staff seniority, are 
preferred over exercises that include face-to-face contact. 
The results of the regular extensive foresight exercises, with a Delphi method at their core, carried out for the 
Japanese government, are regularly used by a variety of stakeholders from both the public and private sectors. 
Since the 8th S&T foresight in 2003--04 the results have also been used as a direct input for S&T and R&D policy 
formulation (the science and technology basic plan). Individual institutions also draw their plans from the national 
foresight results. Various other foresight (as well as other future-looking) activities are undertaken in Japan, 
ranging from sector foresight and meso-level exercises to specific industrial association foresight and micro-level 
foresight within individual companies. 
Often it is stressed that in Japanese foresight culture the main value of the foresight is seen not only in the direct 
outputs like subsequent policies, but in the process itself, expressed as the ‘five Cs’: communication, 
concentration on the longer term, coordination, consensus and commitment (UNIDO, 2005, p. 11). From this point 
of view, education, other policies and individual companies directly or indirectly benefit from foresights, even if no 
direct regular institutional linkage is established.

Sources:  Georghiou et al. (2008); NISTEP (2010); Cuhls (n.d.).

70

Guide to anticipating and matching skills and jobs4.1.6. 

Institutional background 

4.2.  Success factors 

Adequate institutional infrastructure is essential for 
producing successful foresight results and 
corresponding responses. This covers not only 
institutions that sponsor or promote foresight activities 
(usually central or local governments and their 
agencies) but also relevant stakeholders (in particular 
employers and their associations, research centres, 
sector bodies, employees and their representative 
bodies, education and training institutions and others) 
who are possibly engaged or committed to such 
activities.

A lack of adequate institutional framework can mean a 
need to develop a consensus on reforms of existing 
institutions or development of new ones, which 
becomes a challenging issue. Establishing new 
institutions may take a long time. Partnerships and 
networking are often crucial contributors to success. 

Existing institutional infrastructure and capability in a 
country affect the potential application of particular 
approaches. Those involved need to have the capability 
not only to produce foresight results but also to 
transform them into practical policies, programmes and 
measures at different levels. This is a limiting factor for 
many countries that would be able to produce foresight 
outcomes in some way (sponsored by international 
programmes, for example) but are likely to fail in 
producing a policy response due to weak institutions 
(Georghiou and da Fonseca, 2009).

4.1.7.  Resources

Resources that are needed to perform foresight 
exercises include not only funds but also an existing 
statistical infrastructure, previous research or human 
resources with relevant knowledge and expertise. The 
quality and availability of these resources influence 
every foresight activity.

Cases considered in the worldwide review of skill 
foresight approaches suggest that a number of 
common factors are found in successful cases. These 
are generalised key conditions for the success of skill 
foresight activities in every country.

4.2.1. 

 Setting reasonable goals and  
scope of activities 

For countries beginning to develop skills foresight 
approaches it is essential to set a reasonable scope of 
activities. Experience in various countries shows that 
considerable, if more modest, progress can be made 
using elements of good practice rather than an 
ambitious, large-scale programme. Setting goals and 
scope should be based on an initial assessment of 
country context which reveals the main opportunities 
and barriers.

4.2.2.  Adequate institutional framework 

Having an adequate institutional framework in place is 
a key success element. Countries that are able to use 
an existing framework seem to gain a considerable 
advantage. Some countries have a long history of 
evolution of organisations involved in skills, education 
and training development and policy. This has shaped 
the way skill needs are anticipated, and how possible 
skills mismatches can be dealt with. The case of Brazil 
shows (see annex) that the institution involved in skill 
anticipation (SENAI) has an extensive network of 
branches across the country, established over several 
decades. It is obviously easier to build on existing 
structures than to reach a consensus on the reform 
and development of new ones, which may take years. 
Usually, the framework comprises well-established 
institutions along with those that have been recently 
created in response to new challenges. 

71

Developing skills foresights, scenarios and forecastsBox 12. 

Institutional framework in Russia

Skills foresight activities in Russia are sponsored by two government structures: one well established and a 
second recently created to promote new business, young professionals and social projects. 
1.  The Ministry of Education and Science of the Russian Federation launched a study of demand for new skills 
and competencies in high-technology industries as a part of its third science and technology foresight of the 
Russian Federation. 

2.  The Strategic Initiatives Agency (established in 2011) has launched the Competency 2030 foresight as part of 
its initiative to create a national system of qualifications and competencies. This is a comprehensive project 
coordinated through the roadmap that involves activities undertaken by the Ministry of Economic 
Development, Ministry of Labour, Ministry of Education and Science, Ministry of Telecommunication and 
Mass Media, and others.

The Moscow School of Management Skolkovo (established in 2006) has been responsible for the execution of 
both projects. The scope of the project for the Ministry of Education and Science involved key high-tech sectors, 
taking in  biotechnology (including agriculture and food industry applications), healthcare, ground transportation 
systems, aerospace, energy generation and transmission, information and telecommunications, extraction and 
processing of mineral resources, environmental protection and waste management. Additional sectors for the 
Strategic Initiatives Agency project involved construction, finance, education, government and public services.

Source:  Annex, case study on Russia.

More information on the role of social dialogue and 
stakeholders’ involvement and institutional setting in 
anticipating and matching skills are discussed in 
Volumes 1 and 3.

4.2.3.  Engaged stakeholders

the involvement of industry (employers) is essential. 
The SENAI system in Brazil is managed by an executive 
group formed by SENAI technical representatives, 
academia and business representatives who are both 
the producers and users of the information generated.

Crossing disciplinary boundaries is essential to be able 
to address emerging real-world problems. The 
involvement of a wide pool of expertise, and often of 
stakeholders more generally, is needed to access 
relevant knowledge, to engage more participants in the 
policy process, and to establish networks for 
coordinating action and sharing information. Experience 
from various countries shows commitment from 
stakeholders is necessary: this does not mean that all 
stakeholders must be engaged, but for skills forecast 

4.2.4.  Availability of resources 

While the issue of insufficient financial resources can 
be partially resolved by sharing costs in international 
foresight programmes or reducing the scope of 
activities, lack of expertise (across institutions involved) 
and statistical data are more difficult to resolve in a 
short time. Networking and knowledge transfer from 
abroad can help overcome expertise deficiencies. 
Successful foresight exercises are able to balance 
available resources and expected outcomes. 

72

Guide to anticipating and matching skills and jobs4.2.5.  Choice of methods

The choice of methods should reflect country contexts, 
the foresight exercise focus, and the nature of the 
methods. International surveys have revealed that in 
Europe and North America, in circumstances where 
stakeholders have longer experience in participatory 
social dialogue, actors prefer face-to-face forums such 
as expert panels and workshops. In Asia, more 
anonymous methods, such as a Delphi, are preferred. 
There is no prescription for an optimum method mix 
but rarely is a single method used.

4.2.6.  Effective dissemination of results

The foresight results should support informed 
decision-making by the target audience, so an effective 
mechanism for delivering results is a key element. The 
SENAI Thematic Antenna in Brazil is a workshop-like 
event where the executive group discusses the 
outcomes and generates recommendations for SENAI 
in relation to vocational training and provision of 
technical and technological services for the sector in 
focus. Results are ultimately made available to the 
target audience through the website and fed into the 
monitoring process to support SENAI in developing 
skills strategies.

4.3.  Building foresight culture

The country cases considered here show continuing 
efforts have to be made to ensure the long-term 
success of foresight activities. Such efforts can create 
a foresight culture, an environment in which foresight 
exercises produce meaningful results, inform decisions 
and initiate corresponding responses.

A foresight programme/project is a time and resource-
limited exercise which is only one step in building the 
required culture. Isolated activities with little follow-up 
tend to have limited success, so broader support 
activities/processes should be also considered:

(a)  raising awareness of the importance of foresight in 
improving a country’s economic and social situation. 
Awareness-raising activity should help to build 
foresight into the thinking of future generations of 
decision- and policy-makers. This can be done 
through conferences, forums, publications, 
electronic books, the Internet and the media. 
Promotional materials and events should familiarise 
stakeholders with the concept, the practice and the 
results of foresight activities. Special attention 
should be given to motivating industry to participate 
in foresight initiatives as part of a general 
demonstration of the value of foresight results to 
stakeholders; 

(b)  developing capacities and adapting foresight tools 

to a country-specific context. This involves 
improving the skills of foresight practitioners 
through courses, workshops, seminars, fellowships 
and study tours. The involvement of and the support 
for national centres of excellence on foresight 
process, which could be mobilised for the 
preparation of foresight studies, is needed. Making 
use of exchange programmes with regional centres 
and institutions in other regions and engaging 
international experts on foresight and relevant areas 
of knowledge are also appropriate actions. 
Practitioners should be supported in developing and 
adapting foresight tools to a country-specific 
context and in gaining knowledge and experience to 
ensure good foresight products are created;

73

Developing skills foresights, scenarios and forecasts(c)  undertaking foresight exercises for selected areas 
or themes with proper dissemination of results. 
This involves conducting selected foresight studies 
as cases to demonstrate the applicability of 
foresight approaches and their added value for the 
development of national and regional policies; 

(d)  strengthening national knowledge and the capacity 

to use foresight for designing and improving 
policies and strategies. More foresight exercises 
create a more solid knowledge base for guiding 
current decisions towards creating the desired 
future. To take full advantage of foresight results it 
is important to support their delivery to 
corresponding decision-makers.

Stakeholders should be motivated to adopt common 
foresight objectives and aided to articulate foresight 
needs: this demands awareness and knowledge. 
Setting up appropriate organisational structures for 
foresight programmes helps generate consensus on 
programme focus, including goals, methodologies, 
infrastructure and management.

Since foresight results should feed into decision-
making processes (policy cycle), developing channels 
to deliver the results to target audiences is necessary. 
These includes individuals making career choices, 
practitioners in education, training and skill 
development, and those involved in labour market 
management systems: all those who know what is 
going on in the labour market and can see how the 
information gained can best be translated into relevant 
policy actions and other decisions. Relevant 
organisational structures foster a sense of ownership 
and make implementation of results easier. 

4.4. 

 Questions on preparation and 
implementation of a  
foresight exercise

This section offers an overview of some typical 
questions asked when deciding about the application 
of a foresight exercise and during the course of it. 

Some of the following questions are extracted from the 
annex, and information about typical issues is taken 
from a set of presentations given during the ILO-
Skolkovo (Moscow School of Management Skolkovo) 
International expert workshop ‘Using technology 
foresights for identifying future skills needs’ held in July 
2013. Another useful source of typical questions about 
foresight is the FOREN network’s Practical guide to 
regional foresight (JRC-IPTS et al., 2001) although this is 
primarily aimed at the regional level and is not focused 
on skills anticipation. What it offers can be regarded as 
generally applicable in foresight exercises and relevant 
to future skills and competencies foresight. 

There are two main groups of questions: general items 
to think about before beginning the foresight exercise 
and those related directly to the process. 

To which issues can a foresight exercise be applied?

While most often applied to ‘hard’ scientific and 
industrial topics, foresight can be applied more widely, 
as with skills and competencies, although practical 
examples of these exercises are not too common.

The exercise should determine what challenges the 
country/region will face over the coming decades: 
these may be technical (buildings/location/discovery of 
new sources of energy, building of road networks) or 

74

Guide to anticipating and matching skills and jobssocial (ageing of society, baby boom, greening 
economy). The need for the skills foresight is also part 
of changing technologies, processes and managerial 
approaches in the economy resulting from 
technological progress and social innovations, 
increasing global competition for consumer markets, 
and efforts to modernise industries and to launch the 
‘knowledge economy’. 

New challenges and activities are frequently 
accompanied by labour market changes expressed as 
changes in occupation structure, working tasks and 
skills. There are tasks that can be resolved with 
existing skills and knowledge, those that require new 
skills and knowledge (hence the demand for new 
competencies) and those that are no longer needed. 
The foresight should show which skills the country 
needs to develop over the mid or long term and to 
determine how these skills can be generated by the 
education and training system. This means assessing if 
the existing capacity of education (initial and 
continuing) is up to developing newly-detected 
professions, working tasks and skills.

What are the main rationales for  
foresight activities?

(a)  To inform policy-making, so that decisions taken by 

key actors in commissioning bodies reflect 
longer-term developments and how these are liable 
to interact with current policy decisions. Often a 
foresight exercise will be stimulated by the need to 
take a particular decision, but the knowledge 
developed, and the foresight capabilities that have 
hopefully been embedded in the organisation, 
should have a wider significance.

(b)  To help build networks among the people centrally 

involved with shaping the future of a particular topic. 
They will be brought together to work on their 
visions and assessments of the future. The purpose 
of this is to help them become better able 

collectively to understand the challenges and 
opportunities that they are liable to confront, and the 
strategies and objectives that others might pursue.

(c)  To develop capabilities to support the development 
of a ‘foresight culture’. The aim of this is for people 
of various kinds to be able to define and embark 
upon their own foresight activities, to forge their 
own foresight networks. 

In practice, a mixture of these three reasons is 
often in play. The third rationale is probably the one 
that has been slowest to be recognised as a 
practical goal, but it is often very relevant. There may 
also be other goals that stimulate interest in 
foresight; it may start from a national exercise or an 
effort to make the region’s voice heard within the 
context of such an exercise (see annex, especially 
sections on the main challenges and policy objectives 
of case studies). 

When done well, foresight is more a process than an 
academic study; involvement and mobilisation of the 
actors and the use of social dialogue with the private 
sector are key success factors and can be seen as an 
objectives in themselves. Some typical objectives set 
for foresight can be plotted using the axes:

•	

•	

the mobilisation of actors and consensus building;

the ability to inform and shape policy-making and 
decision-making processes.

How can formal methods be used in foresight?

Any foresight exercise involves a range of methods. 
Some of these relate to the management of the 
process, the securing of requisite support from its 
constituencies, and achieving results among its 
intended ‘users’. Other methods are used for the 
generation of informed visions of long-term futures, 
and various formal techniques are employed for this 
purpose (44). 

(44) More detail on these methods is provided in Chapter 2 and in the annex, especially in the sections on key institutions, processes, approach-

es, methodologies and results.

75

Developing skills foresights, scenarios and forecastsWho should be involved in a foresight exercise?

This totally depends on the specific foresight 
objectives, but actors such as governments, 
universities, businesses, chambers of commerce, 
employers’ and industry associations, trade unions, 
media, and NGOs are normally involved. In the case of 
skills key actors should be ministries of education, 
labour, social affairs, economic and finance, as well as 
government agencies (public employment services, 
the agencies for vocational education and training and 
for higher education), the research community, social 
partners, and civil society organisations (see annex, 
especially the sections on key institutions, processes, 
approaches, methodologies and the results of case 
studies).

How can I go about identifying the foresight 
approach appropriate for my situation?

The issue here is one of choosing the appropriate 
balance of approaches, and this will be influenced by 
the problems at stake, the resources at hand, and the 
political context.

How could foresight be ‘positioned’ vis-à-vis 
existing policies and programmes?

Foresight activities can be arranged as relatively 
‘stand-alone’ exercises organised by public authorities 
or they can be embedded in existing policies, 
programmes and strategy-making processes. 

What would be the most suitable time  
horizon for the foresight to adopt?

Time horizons tend to vary from five years up to 
20 years, mostly reflecting the varying identities of 
project beneficiaries, expected foresight products and 
processes.

How long will it take to conduct a foresight 
exercise?

An exercise lasts typically between six months and 
three years.

How much will it cost? 

Costs can vary. They will depend on location of 
activities, scope of the exercise, number of people in 
the project management team, organisation of events, 
and approach selected.

76

Guide to anticipating and matching skills and jobsREFERENCES AND RESOURCES  
ON PART A

Cedefop, ETF, ILO. (2015). Guide to anticipating and matching skills and jobs:

Rihova, H. (2015). Guide to anticipating and matching skills and jobs – volume 1: Using labour market information. 
Luxembourg: Publications Office. Jointly published by Cedefop, ETF and ILO.

Wilson, R.A.; Tarjani, H.; Rihova, H. (2015). Guide to anticipating and matching skills and jobs – volume 3: Working 
at sector level. Luxembourg: Publications Office. Jointly published by Cedefop, ETF and ILO.

Andersen, T.; Feiler, L. (2015). Guide to anticipating and matching skills and jobs – volume 4: What is the role of 
employment service providers? Luxembourg: Publications Office. Jointly published by Cedefop, ETF and ILO.

Corbella, T.; Mane, F. (2015). Guide to anticipating and matching skills and jobs – volume 5: Developing and running 
an establishment skills survey. Luxembourg: Publications Office. Jointly published by Cedefop, ETF and ILO.

Schonburg, H. (2015). Guide to anticipating and matching skills and jobs – volume 6: Carrying out tracer studies. 
Luxembourg: Publications Office. Jointly published by Cedefop, ETF and ILO.

ADB (2008). Education and skills: strategies for accelerated development in Asia and the Pacific. Mandaluyong 
City, Manila: Asian Development Bank. http://www.adb.org/documents/education-and-skills-strategies-accelerated-
development-asia-and-pacific

Anderson, K. et al. (2006). Decarbonising modern societies: integrated scenarios process and workshops. 
Norwich: Tyndall Centre. Technical report; No 48. http://tyndall.ac.uk/sites/default/files/t3_24.pdf 

Cedefop (2010). The skill matching challenge: analysing skill mismatch and policy implications. Luxembourg: 
Publications Office of the European Union.

Conway, M. (n.d.). An overview of foresight methodologies: thinking futures. http://www.forschungsnetzwerk.at/
downloadpub/An-Overview-of-Foresight-Methodologies1.pdf

Cruz Caruso, L.A.; Bastos Tigre, P. (2004). Modelo SENAI de prospecção: documento metodológico [SENAI’s 
prospecting model: methodological document]. Geneva: ILO. http://www.oitcinterfor.org/sites/default/files/file_
publicacion/papeles_14.pdf 

Cuhls, K. (n.d.). Japanese S+T foresight 2035. EFMN Foresight brief No 35. http://www.foresight-platform.eu/
wp-content/uploads/2011/04/EFMN-Brief-No.-35-Japanese-S+T-Foresight-2035.pdf 

Cuhls, K. et al. (eds) (2009). Foresight process on behalf of the German Federal Ministry of Education and 
research: new future fields. Karlsruhe: ISI, Fraunhofer Institute for Systems and Innovation Research. 
http://www.bmbf.de/pubRD/Foresight-Process_BMBF_New_future_fields.pdf 

Duckworth, M. et al. (2010). Horizon scanning and scenario building: scenarios for skills 2020: a report for the 
National Strategic Skills Audit for England 2010. London: UKCES, UK Commission for Employment and Skills. 
Evidence report; No 17. http://webarchive.nationalarchives.gov.uk/20140108090250/http://www.ukces.org.uk/
assets/ukces/docs/publications/evidence-report-17-horizon-scanning-and-scenario-building.pdf 

EIRMA (1997). Technology roadmapping: delivering business vision. Paris: European Industrial Research 
Management Association. Working group report; No 52.

77

Developing skills foresights, scenarios and forecasts 
European Commission (2009). Mapping foresight: revealing how Europe and other world regions navigate into the 
future. Luxembourg: Publications Office. EFMN series. http://ec.europa.eu/research/social-sciences/pdf/
efmn-mapping-foresight_en.pdf 

Georghiou, L.; da Fonseca, R.S. (2009). Foresight for developing countries and economies in transition: UNIDO’s 
role as a policy advisor promoting technology foresight: present and future. http://ictt.by/eng/portals/0/FS2009_
UNIDO_Foresight%20for%20developing%20countries.ppt 

Georghiou, L. et al. (2008). The handbook of technology foresight: concepts and practice. Cheltenham: Edward 
Elgar Publishing.

Gordon, T.J. (2003). Cross-impact analysis. In: Glenn, J.C.; Gordon, T.J. (eds). Future research methodology. 
Washington: AC/UNU Millennium project. http://www.lampsacus.com/documents/CROSSIMPACT.pdf 

Häder, M.; Häder, S. (1995). Delphi und Kognitionspsychologie: ein Zugang zur theoretischen Fundierung der 
Delphi-Methode [Delphi and cognitive psychology: a theoretical foundation to the delphi-method.]. ZUMA-
Nachrichten, Vol. 37, 19 November 1995.

Horton A. (1999). A model for a successful foresight process. http://www.cgee.org.br/atividades/redirKori/340

IFTF (2011). Future work skills 2020. Palo Alto, CA: IFTF, Institute for the Future. http://www.iftf.org/uploads/media/
SR-1382A_UPRI_future_work_skills_sm.pdf 

ILO (1997). Private employment agencies convention No 181. Convention concerning private employment 
agencies, adopted by the International Labour Organization at Geneva on 19.6.1997. http://www.ilo.org/dyn/
normlex/en/f?p=NORMLEXPUB:12100:0::NO::P12100_ILO_CODE:C181

ILO (2009). ILO support for the role of public employment services in the labour market. Paper submitted for 
debate and guidance at the Committee on Employment and Social Policy, Geneva, on 09/10/2009. 
http://www.ilo.org/wcmsp5/groups/public/@ed_norm/@relconf/documents/meetingdocument/wcms_115454.pdf 

ILO (2012). International standard classification of occupations: structure, group definitions. ISCO-08, Vol. 1. 
Geneva: International Labour Organization. http://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/---publ/
documents/publication/wcms_172572.pdf

Jackson, A. (2005). Strategic futures planning: suggestions for success. London: Office of Science and Technology 
UK. 

Jackson, M. (2013). Practical foresight guide. Bishops Wood: Shaping Tomorrow Ltd. http://www.shapingtomorrow.
com/media-centre/pf-ch03.pdf.

JRC-IPTS et al. (2001). A practical guide to regional foresight. Brussels: the FOREN network, European 
Commission. http://foresight.jrc.ec.europa.eu/documents/eur20128en.pdf 

Keenan, M. (2006). Overview of foresight methods. Manchester: PREST, University of Manchester. http://www.
ictt.by/En/Docs/UNIDOTechForesight/221106/1-Overview%20of%20Methods%20Gebze%202006.ppt 

Magnus, S. (2012). Exploratory or normative: new show. Adventure future: Stephmag, 27 February 2012. http://
adventurefuture.wordpress.com/2012/02/27/exploratory-or-normative-new-show/

78

Guide to anticipating and matching skills and jobs 
Matthew, M. (2008). Backasting 101: collaborative strategy development for information architects. ASIS&T IA 
summit pre-conference, 10 April 2008, Silver Spring, MD. http://www.slideshare.net/mmilan/backcasting-101-final-
public 

Miles, I. et al. (2008). The many faces of foresight. In: Georghiou, L. et al. (eds). The handbook of technology 
foresight: concepts and practice. Cheltenham: Edward Elgar Publishing, pp. 3--23.

NISTEP (2010). The 9th science and technology foresight: the 9th Delphi survey (summary). Tokyo: National 
Institute of Science and Technology Policy. NISTEP report; No 140. http://www.nistep.go.jp/achiev/sum/eng/
rep140e/pdf/rep140se.pdf 

Phaal, R.; Probert, D. (2009). Technology roadmapping: facilitating collaborative research strategy. Cambridge: IfM, 
Institute for Manufacturing. http://www.ifm.eng.cam.ac.uk/uploads/Research/CTM/Roadmapping/roadmapping_
overview.pdf

Popper, R. (2008). Foresight methodology. In: Georghiou, L. et al. (eds.) The handbook of technology foresight. 
Cheltenham: Edward Elgar Publishing, pp. 44--88.

Popper, R. (2011). Critical factors influencing the selection of foresight methods. myForesight: Malaysia’s national 
foresight magazine. April 2011, pp. 13-26. http://community.iknowfutures.eu/action/file/download?file_guid=13923 

Potu˚cˇek, M. (2006). Manuál prognostických metod [Manual of prospective methods]. Prague: Sociologické 
nakladatelství SLON. 

Thayer, T. (2012). Foresight and educational planning: engaging stakeholders to construct preferred futures. http://
www.education4site.org/blog/2012/foresight-and-educational-planning-engaging-stakeholders-to-construct-
preferred-futures/ 

UKCES (2010). Skills for jobs: today and tomorrow: the national strategic skills audit for England 2010. Volume 2: 
the evidence report.

UNIDO (2004). Foresight methodologies: training module 2. Vienna: UNIDO, United Nations Industrial 
Development Organisation. http://www.tc.cz/files/istec_publications/textbook2revisedcf_1171283006.pdf 

UNIDO (2005). UNIDO technology foresight manual. Volume 1: organisation and methods. Vienna: UNIDO, United 
Nations Industrial Development Organisation. http://www.unido.org/fileadmin/import/45322_Vol._I_Ebook.pdf 

UNIDO (2009). Practice on roadmapping. Vienna: UNIDO, United Nations Industrial Development Organisation.

Valenta, O. (2012). Foresight. Prague: Technology Centre ASCR. http://www.slideshare.net/KISK/ondej-valenta-
foresight-l-2012

Wilson, C. et al. (2006). Integrating backcasting and decision analytic approaches to policy formulation: a 
conceptual framework. Integrated assessment journal, Vol. 6, No 4, pp. 143--164. http://journals.sfu.ca/int_assess/
index.php/iaj/article/download/253/225

79

Developing skills foresights, scenarios and forecastsWebsites 
[URLs accessed 7.5.2014]

Agenda for new skills and jobs 
http://ec.europa.eu/social/main.jsp?catId=958&langId=en

European foresight platform 
http://www.foresight-platform.eu/community/foresightguide/practicing-foresight-taking-stock-and-advancing-
knowledge/how/methodology/main-methods/meth_environmental-scanning/ 

Futures academy 
http://www.thefuturesacademy.ie/ 

JRC-IPTS, For-Learn online foresight guide 
http://forlearn.jrc.ec.europa.eu/guide/0_home/index.htm

Morphological analysis 
http://www.diegm.uniud.it/create/Handbook/techniques/List/MorphoAnal.php 

New skills for new jobs initiative 
http://ec.europa.eu/social/main.jsp?catId=568

Science and Technology Foresight Office 
http://www.techforesight.ca/description.html

United Nations Industrial Development Organisation: Technology foresight 
http://www.unido.org/foresight.html

World future society 
http://www.wfs.org/node/421

80

Guide to anticipating and matching skills and jobsPart B:  
Skills forecasts

Chapter 5. 
Introduction to skills forecasts

In the first part of the guide, qualitative methods of 
skills anticipation have been thoroughly reviewed. 
Methodologies have been discussed and 
implementation and its pitfalls have also been 
described. While qualitative approaches have their 
merit, a quantitative approach to skills forecasting can 
also be useful. Though qualitative approaches are 
usually less demanding in terms of input and budget 
requirements, quantitative approaches excel when 
complex interactions are to be examined, or 
comparability based on a fixed methodology is crucial 
in repeated forecasting and use of results. 

In the following sections, we aim to provide a brief 
overview of skills forecasting and to point readers 
towards additional literature where they can find more 
information about the huge amount of work going on in 
this area (45). 

Skills forecasting is not an attempt to plan the system 
from the top down. It aims to inform all participants in 
the labour market and education and training arena, to 
help make markets work better. Some have argued 
that systematic anticipation of changing skill needs is 
unnecessary and impossible and that nobody can 
predict the future with certainty. But everybody can 
prepare or plan for it, including governments, 
employers, education institutions and individuals. To do 
this involves some element of forecasting: either 
implicitly or explicitly. In this sense, not only is 
forecasting possible, it is also inevitable. The only 
meaningful questions are how it should be undertaken, 
by whom, and with what end in mind. The revealed 

preference of governments worldwide suggests that 
there is general acceptance of the case for carrying out 
such work as a ‘public good’.

While it is now generally agreed that in a market 
economy it is not possible to make precise predictions 
that can be used for detailed ‘manpower planning’, the 
need to make strategic plans and choices which can 
influence and shape the future path taken by the 
economy and labour market is widely accepted. Such 
plans need to be guided by robust labour market 
information and analysis, including a forward-looking 
element. This needs to be based on regular, systematic 
and quantitative approaches to forecasting and 
scenario development.

Skills are a key part of the infrastructure of the 
economy; the choices made by policy-makers, 
enterprises and individuals on investment in education 
and skills can help to determine the path the economy 
takes. These individual choices also need to be guided 
by good labour market information and analysis. 

It is telling that the US, one of the most capitalist 
economies in the world, devotes more resources to 
this activity than anywhere else. The approach is 
backed up by huge investment in labour market 
information, including forecasting, aimed at ensuring 
that students are well informed about the choices they 
face. Europe as a whole has some way to go to catch 
up, although there are many examples of good practice 
in individual Member States.

(45) In this text we incorporate experience and information from the programme of research led by the Warwick Institute for Employment 

Research, funded by Cedefop. This report is only one part of the project. As a whole, the project focuses on the implications of structural 
changes in European economies and labour markets on the demand for and supply of formal qualifications (Cedefop, 2010).

82

Guide to anticipating and matching skills and jobsThe experience of the US and other countries across the 
world suggests that forecasting can provide systematic 
analysis of the implications of continuing past and 
current trends and patterns of behaviour. It can help to 
map out different scenarios, based on alternative 
assumptions, which can form a basis for intelligent and 
informed debate and further research, as well as helping 
to inform individuals making career decisions.

Although there is strong evidence of causal links 
between investment in education, training and skills 
and economic success (both at the whole economy 
level and for individuals), there is no guarantee of a 
positive return. It is crucial to invest in the right areas. 
This highlights the importance of understanding where 
the best opportunities will arise.

Box 13.  Rationale for skills projections

The rationale for producing regular and detailed projections includes:
•  filling existing information deficits and preventing future labour market imbalances;
• 
•  supporting policy-making at national and European level in employment and social protection, lifelong learning, 

informing various actors on future labour market needs, as an aid to their choices and decision-making;

guidance and counselling and migration.

in which sectors and occupations will employment be growing?
for which qualifications will demand increase or decrease?

Answering key questions:
• 
• 
•  what about replacement needs?
•  how will this compare with supply?

5.1.  Skills forecasting: for whom?

There are many different audiences for skills analysis 
and forecasting, and their specific and detailed needs 
for labour market information and analysis may be very 
different. In the current guide we focus on the longer 
time horizons of 5- to 20-year forecasts. While 
shorter-term forecasts have a merit of their own, we 
will not discuss them in this guide extensively. The 
discussion will partly be informative for other time 
horizons as well. 

A key set of questions to be addressed when 
assessing such needs and systems are: 

(a)  when: what is the timeframe over which the skills 

forecast will be performed and for how long;

(b)  by whom (funding and execution): who is the 

funding body of the skills forecast, and by whom is 
the skills forecast executed; 

(c)  for whom: what is the target group or the goal of 

the skills forecast; 

(d)  how: which approach and/or methodology is chosen 

to generate and present the skills forecast. 

83

Developing skills foresights, scenarios and forecastsThe main audiences include:

(a)  government, at national and regional level 

(policy-makers);

(b)  stakeholders, including local bodies, industry 

training organisations, employers, trade unions, 
education and training institutions, and careers 
guidance organisations;

(c)  individuals making occupation choices.

The interests of the different audiences may be very 
different. They include:

Limited resources mean that the main national 
projections are intended to serve many different users 
and purposes. While this cuts costs, it may mean 
compromises in terms of meeting the specific needs 
of different users. We will be discussing best practices 
in most cases, which show what can be achieved; this 
does not imply the simplification of modules or a 
combination of quantitative and qualitative approaches 
(Part A). On the quantitative side, we have tried to 
include alternative approaches that might supplement 
best practices whenever this was straightforward to 
do. 

(a)  links to more general economic policy and analysis, 

including productivity and growth;

5.2.  Duration of forecast

(b)  occupation demand: future employment levels by 

occupation/skill;

(c)  replacement demands: job openings (recognising 

the need to replace those leaving);

(d)  education and training requirements: qualifications 

typically needed;

(e)  supply/demand balances;

(f)  terms and conditions of employment (pay).

What is done and how it is done, therefore, depends at 
least in part on who it is being done for and why. 
Different audiences have very different requirements 
for both detail and general content. Policy-makers are 
more interested in overall supply/demand balances and 
the general areas where investment in skills is needed. 
Training providers and individuals are interested in 
much more detailed information about prospects in 
particular areas.

A particular ministry may have special requirements. 
For example, the ministry of the economy may have 
more interest in developing alternative scenarios 
showing links to macroeconomic drivers and policy 
interventions.

The chosen duration of the forecast is often 
determined by the specific goals. Long-term planning 
and insights into developments in the labour market by 
sectors and occupations, qualification and skills 
demand, are often well served by longer time horizons 
(10--20 years). More specific questions on information 
regarding study choice might be better served by more 
detailed, but mid-term (5-9 years), forecasts. 
Employment service providers are often interested in 
short-term detailed forecasts which are not discussed 
in detail in this volume of the guide (see Volume 4). 

Generally, the longer the forecasting period, the less 
detailed and precise the forecast necessarily becomes. 
It is useful in identifying important developments in the 
labour market and long-term shortages that policy 
might address, but less so for immediate study 
choices. In the latter case, students are interested in an 
answer to the question: what qualification will help me 
find a job quickly after I graduate? 

Both are necessarily interrelated. While it might be 
true, that longer-term forecasts might not be able to 
isolate certain developments to within a particular year, 
the outcomes are likely to be important even for study 
choices, as graduates will eventually not only find a 
first job, but also keep being employed over their entire 
working life. 

84

Guide to anticipating and matching skills and jobs5.3. 

 A brief history of labour market 
forecasting

One early step in labour market forecasting was the 
work around the Mediterranean regional project in the 
early 1960s (Parnes, 1962; OECD, 1965). The aim of 
these initiatives was to understand the changing 
economic structures in six Mediterranean countries: 
Greece, Italy, Portugal, Spain, Turkey and Yugoslavia. It 
involved outlining detailed educational requirements 
that would stimulate desired growth through the 
optimum planning of education output. Around the 
same time, similar projects were initiated elsewhere; 
all involved the belief that detailed education planning 
and manpower analysis could provide detailed 
forecasts of required manpower. 

Path-breaking work was done at the US Bureau of 
Labor Statistics. For more than 50 years, they worked 
on projections that helped to highlight the future 
challenges of the labour market. The first projection of 
the labour force, industry output and employment, and 
occupational employment and job openings, was 
presented as early as 1966. 

Out of these detailed manpower planning models and 
approaches developed, in the 1980s, a much more 
modest analysis of expected future demand and supply 
of skills, mainly used for policy planning and information 
provision (Van Eijs, 1994). While the models build on 
similar data infrastructures, the methodology, outcome 
and interpretation was more careful. Supply and 
demand were modelled to interact to some degree, and 
resulting imbalances were not interpreted at face value 
or in exact numbers. Different approaches to the 
confrontation and presentation of supply/demand 
outcomes evolved in different countries, but the 
common factor in most countries was the emphasis on 
uncovering imbalances rather than predicting exact 
requirements. 

The usefulness of skills forecasts nowadays should not 
be tested on their direct predictive power but in terms 

of how useful the information is in guiding the 
decision-makers to tackle future expected imbalances. 
The critical point is to evaluate whether the information 
provision in itself provides sufficient value for the costs 
of establishing these models. 

5.4. 

 Best practices: when to use  
quantitative skills forecasts

We believe that quantitative approaches to skills 
anticipation have helped to shape the analysis of future 
skills needs. We use what we consider best practices 
as a guideline in this report and, in doing so, we will 
also mention and discuss alternatives. These 
discussions will necessarily lack the depth and detail 
that we can provide for methodology that we have 
adopted ourselves. 

Wilson (2008; 2012) et al., 2004) has provided 
general reviews of quantitative approaches to skills 
anticipation, drawing lessons from work in the UK 
and at pan-European level. The reviews provide a 
summary of the models, tools and other approaches 
used to help identify future skill needs across the 
world, including an assessment of their strengths and 
weaknesses.

The conclusions of such reviews are that ‘best practice’ 
worldwide involves quantitative methods, based on the 
use of large-scale, multisectoral models to produce a 
comprehensive overview of how structural economic 
and technological changes are affecting the demand for 
skills. This is not the only approach to skills anticipation: 
such methods need to be complemented by other 
quantitative and more qualitative approaches, 
especially where data for building quantitative models 
are limited. However, quantitative projections based on 
such methods can be a cornerstone in developing 
systematic approaches, and limitations in data or 
models can be overcome by using the qualitative 
methods discussed in Part A of this guide. 

85

Developing skills foresights, scenarios and forecasts5.4.1.  Limitations of quantitative models

It is important to recognise what such quantitative 
models can and cannot do. On the positive side, 
projections can:

(a)  help to make assumptions about the future explicit 

and transparent;

(b)  help to enforce systematic and logical thinking;

(c)  act as a focus for intelligent debate;

(d)  provide a useful counterfactual to assess policy 

impacts (what would have happened in the absence 
of the policy intervention).

But they cannot provide:

(a)  mechanistic ‘manpower’ planning;

(b)  precise indications of education and training 

requirements.

Sophisticated econometric techniques can be used 
where data are adequate. This is a feature of much 
macroeconomic modelling. The implications for 
occupations and qualifications are then developed in 
other modules using extrapolative techniques, for 
example modelling and projecting employment shares 
within industries for occupations and qualifications.

Such results are intended to provide a sound statistical 
foundation for the deliberations of all those with an 
interest in the supply of, and demand for, skills. This 
includes individuals, employers, and education and 
training providers, as well as the various agencies and 
departments of government. While the future cannot 
be predicted with precision or certainty, all the 
participants in the labour market make plans for it, even 
if these are simply based on the default assumption 
that the future will be the same as the past. The 
rationale behind such projections is that a 

comprehensive, systematic, consistent and transparent 
set of projections can help to inform everyone about 
the world they are likely to face.

The views presented are not the only possible future. 
They represent a benchmark for debate and reflection 
and an aid to informing policy development. The 
detailed projections present a carefully considered 
view of what the future might look like, assuming that 
past patterns of behaviour and performance are 
continued over the longer term.

The results should be regarded as indicative of general 
trends and orders of magnitude and are not intended 
to be prescriptive. If policies and patterns of behaviour 
are changed, then alternative (perhaps more desirable) 
futures can result.

Combining quantitative forecasts with foresighting 
techniques (Part A) allows elaboration on the strengths 
and weaknesses of quantitative forecasts. By including 
alternative scenarios and varying underlying 
assumptions, different forecasts can be produced. 
These variants allow deeper analysis of the underlying 
factors influencing an economy. 

Quantitative models are regarded as essential to obtain 
a robust and consistent sectoral employment scenario, 
which is the starting point for any comprehensive 
assessment of changing skill needs.

The advantages of such an approach include:

(a)  the sectoral and other detail it provides;

(b)  the fact that it is typically comprehensive, covering 

the whole economy;

(c)  logical consistency;

(d)  the imposition of accounting constraints;

86

Guide to anticipating and matching skills and jobsNobody can predict with precision the demand for, or 
supply of skills. Nor is the future predetermined. 
However, many trends are robust and these can be 
used to inform all those involved about the world they 
are likely to face.

Data quality is a key issue: results from such models 
cannot be more robust than the data upon which they 
are based. 

The development and use of quantitative models is a 
resource-intensive process, requiring substantial prior 
investments in data and analysis, often taking many 
years. However, it is argued here that by building on 
existing models and methods, it is possible to develop 
something quite quickly that can help to begin the 
process of building a robust approach to such 
requirements.

Several other tried and tested methods complement 
the quantitative approaches and are less dependent 
upon such data; these can be implemented in a much 
shorter time. However, there is no substitute for robust 
quantitative information on the current position and 
trends. 

(e)  the recognition of economic constraints and 

influences;

(f)  the fact that it helps make underlying assumptions 

explicit;

(g)  consistent scenarios across all sectors.

Such methods have some disadvantages and 
problems, including:

(a)  technical limitations, within fixed resource limits;

(b)  limits to current understandings of the way labour 

markets work;

(c)  the possibly limited relevance of the past, such 
models being based on an assumption of a 
continuation of past patterns of behaviour;

(d)  the data requirements of quantitative modelling 
approaches are substantial: long time series of 
consistent sectoral data on a range of economic 
and labour market indicators, especially 
employment, lie at the heart of any multisectoral 
modelling approach to assessing changing skill 
needs, ideally linked to other economic indicators 
within a system of national accounts. They also 
require many years of substantial investment;

(e)  data limitations: often the data used to build models 

were not collected with modelling in mind;

(f)  resource costs of development and maintenance.

Quantitative models should not, therefore, be seen as 
a panacea, but in most countries that conduct regular 
national assessments of future occupational and skill 
requirements they are regarded as an essential 
cornerstone. Such models are increasingly being 
adopted in developing, as well as developed, countries, 
as the availability of data and the capacity for model 
building improves.

87

Developing skills foresights, scenarios and forecastsChapter 6. 
General approaches in skills forecasting

There have been many different approaches to 
anticipating changing skill needs worldwide. These 
have tended to reflect perceptions of what is desirable, 
as well as the practical limitations of what is feasible. 
Both of these have changed substantially over the past 
50 years.

From the earliest attempts, those engaged in such 
work have adopted model-based, quantitative methods 
wherever possible, simply because quantitative results 
have been seen as a key output required by potential 
users. The use of formal models has been advocated 
on various grounds, as detailed below. However, the 
merits of alternative, more qualitative methods have 
also been recognised (Lindley, 1994).

The most frequently used approaches to anticipating 
future skill needs can be grouped under four main 
headings:

(a)  ask employers (such as employer skill surveys – see 

Volume 5);

(b)  quantitative models (formal, national-level, 
quantitative, model-based projections);

(c)  sectoral studies (see Volume 3);

(d)  primarily qualitative methods (focus groups/

roundtables and other Delphi-style methods, and 
scenario development. These may include some 
quantitative aspects but are generally more 
qualitative – see Part A of this volume).

Each of these approaches has its own strengths and 
weaknesses which are summarised in Table 15.

88

Guide to anticipating and matching skills and jobsTable 15.  Some alternative approaches to anticipating skill requirements

Alternative approaches

Advantages

Disadvantages

Surveys of employers or other 
groups, asking about skill 
deficiencies and skill gaps

Direct ‘user/customer’ involvement
Focuses on behaviour

Formal, national-level, quantitative, 
model-based projections

Ad hoc sectoral or occupational 
studies (using a variety of 
quantitative (model-based) and 
qualitative tools)

Comprehensive
Consistent
Transparent
Quantitative

Strong on sectoral specifics

May be very subjective
Inconsistent
Myopic
Can focus on the margins too easily 
(i.e. current vacancies) rather than on skill 
gaps within the current workforce

Data-hungry 
Costly
Not everything can be quantified
May give a misleading impression of 
precision

Partial
Can be inconsistent across sectors

Focus groups/roundtables and 
other Delphi style methods

Holistic
Direct ‘user/customer’ involvement

Non-systematic
Can be inconsistent
Can be very subjective

6.1.  Quantitative modelling

The basic methods adopted for quantitative modelling 
can be classified under the following headings:

(a)  extrapolation of past trends;

(b)  more complex time series methods;

(c)  introducing behavioural content.

Extrapolative techniques are often used, especially 
where only very limited time series information is 

available. In many cases, only one or two observations 
are available on occupation structure and this clearly 
limits the sophistication of what can be done! Where 
more time series observations are available, much 
more sophisticated analysis is possible; this attempts 
to find patterns in a time series that can be used to 
predict its future path. Such approaches are widely 
used in the business and financial world, although they 
are much better at predicting short-term change than 
longer-term patterns. 

89

Developing skills foresights, scenarios and forecasts6.2.  Qualitative methods

Most of the qualitative approaches have been 
described in Part A of this guide. They include the use 
of in-depth interviews with key stakeholders, including 
employers, detailed case studies (especially of 
particular sectors), scenario development, focus 
groups, roundtable discussions and similar 
mechanisms. These enable ‘soft’ qualitative data to be 
incorporated into analysis and thinking about future skill 
needs alongside the ‘harder’ statistical information on 
which most quantitative analysis is based (see 
especially Chapter 2 in which these methods are 
discussed in depth).

6.3. 

 Combining quantitative and  
qualitative methods

Bringing together and combining quantitative and 
qualitative methods is desirable in skills anticipation. 
Quantitative results can inform qualitative research and 
vice versa. Such holistic approaches, along with 
innovative solutions in research and analysis, can better 
cater for the time gap between the actual change in 
demand and the policy and implementation response.

History suggests that most linear (or more complex) 
trend patterns eventually come to an end and so 
should not be relied upon for medium- to long-term 
forecasting. Behavioural analysis is an attempt to move 
beyond patterns in observed time series data and to 
provide some understanding of how it is that these 
patterns have arisen and, more important, why they 
may change in the future. Analysis draws on disciplines 
such as economics and sociology for an understanding 
of what influences the behaviour of the key actors in 
the economy and how this is reflected in the main 
economic and social indicators that can be 
measured (46). 

Such understanding finds representation in 
computerised ‘models’, which take the form of 
algebraic equations linking key variables. A model is an 
attempt to provide a simplified representation of reality 
that can help the researcher understand the 
phenomenon of interest (in this case changing patterns 
in demand for skills in the labour market). The idea of 
engineers building models in order to test out their 
ideas, for example testing model aircrafts in wind 
tunnels, is familiar. Models in the social sciences suit a 
biological analogy better than an engineering one; as an 
example, scientists have built models of dinosaurs in 
order to try to understand how they could fly. Social 
scientists attempting to understand how societies and 
economies work face problems such as:

(a)  lack of fixed laboratory conditions;

(b)  lack of good experimental data (they can only 

observe outcomes).

Social science models are typically built using quite 
sophisticated statistical and econometric techniques. 
They are based on data drawn largely from official 
sources, including national accounts and related 
estimates of employment based on surveys of 
employers and of households. 

(46) Expected paradigm shifts or historic data series that are deemed not to be adequate to estimate or feed into the quantitative model could be 
supplemented by qualitative methods such as scenarios (Chapter 2) and their translation into quantitative effects. Vogler-Ludwig et al. (2013) 
use such a method. 

90

Guide to anticipating and matching skills and jobsChapter 7. 
Data

It is hard to predict the future without an understanding 
of either the past or the present. Data, especially 
labour market data, are crucial to do skills forecasting. 
In the following, we describe the key data 
requirements for quantitative modelling as it is 
advocated in this report. In some sense, it does not 
matter whether the data are collected on a regional, 
national or transnational level, if that is the level in 
which the forecast is to be made. In general, it is 
assumed that the data are collected on a national level. 

7.1.  Key data

The following list summarises the key data that can be 
used for skills forecasting. The degree and duration for 
which these data are necessary depend on the specific 
series and aims (see individual descriptions of the 
data):

(a)  national accounts time series on output by sector 
and factor inputs on capital and, more important, 
labour. More detailed data on these 
macroeconomic figures, as well as detailed data on 
sector, employment and labour market participation 
rates by age and gender, and price and wage 
information, help specify many aspects of the 
macroeconomic model in more detail;

(b)  historic data on employment by occupation, 

education and industry. These are usually collected 
in either labour force surveys (LFS), as with the 
surveys underlying the EU-LFS, or as part of a 
(partial) census. As many aspects of the 
estimations are determined using historic data to 
identify both relationships and time trends, longer 
time series with many waves are preferred; the 
normal case would be detailed data based on an 
annual labour force survey. The estimation of 

expansion demand basically determines the 
allocation of sector employment demand across 
occupations, for which we need information on the 
occupation by sector employment and 
development; 

(c)  historic data on employment by occupation and 

age-gender groups is necessary for the estimation 
of replacement demand. Ideally, individual changes 
(gross flows) of workers would be observed, but 
repeated cross-sections with employment in 
occupations by age-gender groups are mostly used 
to identify net flows to estimate replacement 
demand; 

(d)  employment by occupation and qualification helps 
in bringing together supply and demand. Having 
historic information on the development of this 
relationship helps to identify and interpret the 
reactions of the economy to imbalances in supply 
and demand; 

(e)  historic data on population development. Often, 

these are already factored into population forecasts, 
which are important for the labour supply forecast; 

(f)  consistent information on participation and 

graduation in education is ideal to model more 
detailed supply processes. However, these data are 
often at least partly missing or historic experience is 
severely disturbed by administrative changes. This 
can be in terms of financing education, which is 
important for opportunities and decisions on 
enrolling in education, but also in terms of the 
types of degree. Examples are the introduction of 
the bachelor and master system in continental 
Europe, for which there is no direct (data) 
precedence. 

91

Developing skills foresights, scenarios and forecasts7.2.  Data collection

Surveys are the most common form in which the key 
data described above are collected. Data quality needs 
to be good for reliable forecasts. Many countries 
already have some form of labour force survey, a good 
source for the key numbers and relationships 
necessary to generate skills forecasts. Labour market 
surveys usually sample a group of respondents to 
generate statistics that are sufficiently reliable and 
robust (47). 

Census data usually take stock of the entire population 
in a country. Given the magnitude of this task, a 
census cannot be repeated as often as surveys. In 
some cases a country uses a sample to fill information 
gaps between full censuses. A census is often less 
detailed about qualifications and labour market 
information, but could supply the basic information to 
enable skills forecasts if they cover information on 
employment by occupation and sector as well as 
demographic information on age, gender and 
qualifications. 

Employer surveys usually collect information on 
employment within firms, qualifications, skills and/or 
task requirements, and firm- or sector-related 
information. They have the advantage that information 
about demand by skills or qualifications can be 
identified directly. However, they are not commonly 
used or integrated as a key data source in quantitative 
labour market forecasts. 

New data sources that add to the above modes of data 
collection come from the increasing ease with which 
administrative data that are collected for a different 
purpose can be used. For example, the social security 
contributions or tax payments of workers in 
employment relationships are already collected in 
many countries in such a way that key information can 
be identified: the employment (numbers) by sector, 

age and gender. Adding or requiring additional 
information such as qualifications or occupation makes 
such administrative data useful for quantitative 
forecasts by detailed levels of occupation and 
qualification. 

Other sources potentially useful in providing detailed 
information about skills or qualifications are various 
skills surveys. These usually sample groups of adult 
respondents and enquire about the utilisation of skills 
in different contexts. To be beneficial for quantitative 
forecasts these surveys need to be sampled by 
occupational group.

Data sources suitable for skills anticipation and 
matching are discussed in more detail in Volume 1.

7.2.1.  Classifications

Classifications of sectors, qualifications and 
occupations are important in running quantitative 
forecasting models. These classifications allow the 
combining of different data sources and set the level to 
which forecasts can generally be made. If existing 
classifications do not reflect the level of detail to which 
a forecast is to be done, alternative classifications 
would need to be determined. 

Within the European context, the international 
classifications for industry (NACE), occupations (ISCO) 
and education (ISCED) are used. These allow using the 
time series of the data under the assumption that the 
classification into the categories has not changed (48). 
Changes in classification pose additional challenges to 
forecasting, as they usually introduce breaks in time 
series that have to be considered in the (estimation) 
models of the quantitative forecasts. Ideally, the 
classification change can be applied retrospectively to 
data that have been collected earlier, to have long time 
series using the new classification. In practice, this 
ideal case is not often observed. 

(47) The size of the sample to be drawn depends on the variation in the variables of interest. The more variation in the key variables and the more 
heterogeneous the population, the bigger the sample has to be. Many countries draw samples that come close to 1% of the working popula-
tion. Using stratification along several key dimensions of the population allows scaling down the size of the sample. 

(48) That this assumption is not always valid can be seen in the discussion on data quality of the Cedefop project. Shifts in the ‘interpretation’ of 

classifications can be found for many countries within the EU-LFS. For forecasting it can be important to identify those implicit breaks in data 
series and adjust them so that they do not interfere with the estimation methodology. 

92

Guide to anticipating and matching skills and jobsNational data providers often do not adhere (directly) to 
international classifications. This allows them to 
introduce detail in aspects that are important for the 
economy – such as specific information on the 
vocational education and training qualifications in 
Germany that are not always easy to fit within the 
ISCED categories – but it poses the danger that the 
classifications cannot easily be compared.

Specific aspects of the use of standard classifications 
for skills analysis are also discussed in Volume 1.

93

Developing skills foresights, scenarios and forecastsChapter 8. 
Modelling approaches

8.1.  Macro models 

A key building block in quantitative forecasting can be 
macro models. These models calculate the effects of 
changes in key parameters and endogenous reactions 
and feedback loops based on economic theory or 
estimates from historic data. They build models of key 
interactions in an economy by including those 
elements that are expected to drive the economy as a 
whole, and specifically the outcomes that the model 
has been developed for. 

The advantage of macroeconomic models over 
econometric techniques of (trend) forecasting is in their 
ability to include key interactions. This also allows the 
researcher to ‘run’ the model to evaluate different 
scenarios (views) of the outcome of some key 
drivers of economic activity, or to include specific 
elements for which the outcome is of particular 
interest to the policy-maker (policy simulations). 

Many established national and international macro 
models have been developed for various purposes. The 
Dutch Planning Office, for example, uses macroeconomic 
models to develop a short- to medium-term forecast and 
analyse the Dutch economy (Saffier II), the Athena model 
for short to long-term forecasts that include an analysis of 
sectors and form the basis for long-term scenarios, and 
Worldscan, the model used to investigate and forecast 
international issues such as European integration or 
climate change impacts.

Inforge is a macroeconomic model that is used to 
simulate developments on the German labour market 
and which has been used for labour market forecasts 
by the German Employment Services (Lutz et al., 
2003).

8.1.1. 

 The macro model for the Cedefop  
skills project: E3ME

E3ME is a well-established model of the 
European economy and labour market (49). It 
delivers detailed projections of employment by sector 
and country and of the economically active labour force 
by age (and gender) that drive the more detailed skills 
modules. E3ME treats Europe as a multiregional area, 
with each country treated as a region within the whole. 
The entire system is covered for all sectors and regions 
(countries).

The model is based on empirical relationships 
and econometric estimation. The main data source 
is Eurostat and the model has recently been 
updated for NACE revision 2. The revised model 
distinguishes 69 industrial sectors (to be aggregated 
for presentation to around 40 industries). It is 
compatible with ESA-95 accounting classifications. 
Labour supply is split by gender and five-year age 
bands.

E3ME includes a detailed treatment of the labour 
market, with sets of equations for employment  
(treated as labour demand), labour supply, average 
earnings and hours worked. The equations for labour 
demand, wages and hours worked are estimated and 
solved for each economic sector, defined at NACE 
two-digit level. Labour participation rates are 
disaggregated by gender and five-year age band, and 
multiplied by Eurostat population data to obtain labour 
supply information.

(49) The discussion here is based on the E3ME website (http://www.e3me.com). A full technical manual is also available on this website.

94

Guide to anticipating and matching skills and jobsEmployment is modelled using national accounts data, 
with a total headcount for each industry and country as 
a function of industry output, wages, hours worked, 
technological progress, and energy prices. Industry 
output is assumed to have a positive effect on 
employment, while the effect of higher wages and 
longer working hours is assumed to be negative. The 
effects of technical progress are ambiguous, as 
investment may create or replace labour; this will vary 
between sectors.

On the supply side, labour market participation is 
estimated as a rate between 0 and 1 for each gender/
age group and country. Labour market participation is a 
function of output, wages, unemployment and benefit 
rates. Participation is assumed to be higher when 
output and wages are growing, but falls when 
unemployment is high, or social benefits create a 
disincentive to work. There is also a measure of 
economic structure and the relative size of the service 
sector of the economy; this has been found to be 
important in determining female participation rates. 
Participation rates determine the stock of employment 
available (by multiplying by working-age population, 
which is exogenous). Unemployment, both voluntary 
and involuntary, is calculated by taking the difference 
between labour supply and demand.

In E3ME, wages are determined by a complex union 
bargaining system that includes both worker 
productivity effects and prices and wage rates in the 
wider economy. Other important factors include 
unemployment, tax rates and cyclical effects. Generally 
it is assumed that higher prices and productivity will 
push up wage rates, but rising unemployment will 
reduce wages. A single average wage is estimated for 

each country and sector: estimates are a key input to 
both the employment equations and the price 
equations in E3ME. In the absence of growing output, 
rising wages will increase overall unit costs and 
industry prices. These prices may get passed on to 
other industries (through the input-output 
relationships), building up inflationary pressure.

Average working hours in each sector are determined 
by its position in the economic cycle and technological 
progress. The estimate of hours worked is an 
explanatory variable in the employment equation (see 
above). The number of hours worked is defined as an 
average across all workers in an industry and 
incorporates the effects of higher levels of part-time 
employment in certain regions and industries.

8.1.2. 

 A model suitable for transition  
countries: Hermin

The structural Hermin model was developed by Bradley 
(2000). It represents a simplified version of the more 
complex multisector Hermes model, originally 
designed by the European Commission in the early 
1980s for an international comparison of structural 
funds (community support framework) effects on the 
national economy (d’Alcantara and Italianer, 1982). The 
simplification in the Hermin model allows its use of 
this model in countries lacking statistical data (such as 
the pre-accession EU countries). The methodology is 
also suitable for modelling small open economies. The 
original Hermin model structure is composed of four 
main sectors (manufacturing, agriculture, market and 
non-market services) and contains both the supply and 
the demand side of the economy (Bradley et al., 1995; 
Barry et al., 2003).

95

Developing skills foresights, scenarios and forecastsIn several applications modified model designs were 
introduced and comprised five main sectors: 
manufacturing, market services, construction, 
non-market services and agriculture. Such modified 
versions of the Hermin model have been used to 
evaluate national strategic reference frameworks 
effects in, for example, Czech Republic, Estonia, 
Romania, Slovenia (Bradley et al., 2005), and Slovakia 
(Kvetan et al., 2006). The Hermin model was initially 
developed to estimate effects resulting from the 
allocation of structural funds (Zaleski et al., 2004; 
Kvetan et al., 2006) and was further developed for 
analysis of different problems. The Hermin model 
should also be applied to long-term labour market 
forecasting based on the demand side of the economy.

The Hermin model framework focuses on key 
structural features of a cohesion-type economy, of 
which the following are important:

(b)  specification of the mechanisms through which an 
economy is connected to the external markets. The 
external (or world) economy is very important to the 
economic growth and convergence of lagging 
economies, through trade of goods and services, 
inflation transmission, migration and inflow of 
foreign direct investment;

(c)  the need to recognise possible conflicts that may 
exist between the actual situation in the economy 
(captured in a Hermin model calibrated with the use 
of historical data) and the expected future 
development of the economy. Estimation of 
parameters based only on past data is likely to be 
inappropriate (even where it is feasible); it may be 
necessary to calibrate some parameters based on 
expert estimations and the existing current 
economic situation should be evaluated with 
caution.

(a)  the degree of economic openness, exposure to 

world trade, and response to external and internal 
shocks;

To satisfy these requirements it is inevitable to 
evaluate the overall economic development of a given 
economy and to possess the necessary skills. 

(b)  the relative sizes and characteristics of traded and 

non-traded sectors, together with their 
development (factors such as production 
technology and structural change);

(c)  the mechanisms of wage and price determination;

(d)  the behaviour and flexibility of the labour market 

with the possible role of labour migration;

(e)  the role of the public sector and the possible 

consequences of public debt accumulation, as well 
as the interactions between public and private 
sector trade-offs in public policies.

The Hermin model and its modifications were 
developed to analyse medium-term policy impacts of 
EU structural funds implementation. To fulfil this goal 
some basic assumptions need to be borne in mind: 

(a)  disaggregation into a small number of crucial 

sectors that allows identifying and modelling the 
key sector shifts in the economy over the future 
years of development;

The Hermin model structure has proved its flexibility 
and applicability to many countries (ranging from 
developed to post-communist countries); it has been 
used for different types of analyses, ranging from 
evaluation of EU funds (Radvanský and Frank, 2010) to 
labour market analysis in Macedonia (FYROM). Current 
methodological progress is oriented towards regional 
application of the Hermin model, particularly to NUTS-2 
Polish (Zaleski, 2009) and NUTS-3 Slovak regions.

8.2.  Other economic modelling

Econometric forecasting modelling cannot progress 
much further without fundamental shifts in the 
availability and quality of data available. There are 
arguments for developing computable models to fill in 
some of the gaps where the existing data limit what 
can be estimated econometrically. For many years 
economists have been developing computable general 
equilibrium (CGE) models. These now fall into many 
different classes, including those that explicitly 
consider disequilibrium situations.

96

Guide to anticipating and matching skills and jobsIn the European project, Meagher et al. (2012) have 
demonstrated the feasibility of developing such CGE 
models, although the examples developed are neither 
general (considering just a small part of the skills 
demand and supply nexus) nor necessarily equilibrium-
based (focusing on how the labour market adjusts to 
resolve imbalances). However, the CGE type 
approaches enable a much more in-depth examination 
of the key drivers of change in different sectors, and an 
understanding of the underlying reasons behind the 
quantitative decline or expansion of particular skills.

8.3.  Simpler models of employment change 

Transition and developing countries often face 
problems with data availability and quality, in particular 
when longer time series are necessary. Less 

data-hungry models based on input-output tables or 
social accounting matrices can be useful in such cases.

Input-output models are simpler than those described 
above. Their purpose is not an economic forecast: they 
are not designed to project GDP but to model impacts 
of changes in final demand for products on the 
production of different sectors and on employment. 
They are used to demonstrate how a specific scenario 
on the demand side – for example higher demand for 
some products or services by households, a change in 
direct foreign investment, or a change in government 
spending – will influence the volume and structure of 
employment and, by implication, the demand for 
education and skills. For example, an input-output 
model for the Philippines was used to assess the 
impact of austerity measures on its labour market 
(El Achkar Hilal et al., 2013).

Box 14.  Use of input-output data

The OECD regularly publishes input-output tables for a number of countries (www.oecd.org/sti/inputoutput). 
Where data is missing for a particular country or region, input-output tables from similar closely-related contexts 
can be used.
In the PERI study on the employment effects of green energy investments, the input-output tables for Ontario 
were not available in sufficient detail. The national Canadian data were therefore used as an approximation.

97

Developing skills foresights, scenarios and forecastsThe data requirements for the input-output models are 
sufficiently low to be applicable in different country 
contexts. A study of 31 developed and developing 
countries across the world from 2011 showed that, 
with rare exceptions, data for this type of models 
are available, although their level of detail and 
periodicity differs significantly. For example, published 
input-output tables in the US provide detail for 
420 sectors, in Thailand for 180, and in the Netherlands 
for 25 (ILO-PERI, 2011).

The core data needed are:

(a)  input-output tables for the last available year;

(b)  data on volume and structure of employment 

(typically from a labour force survey or eventually 
from an establishment survey).

The core of the model is an input-output table which 
shows how much each sector produces, how much of 
this production is used by other sectors and the 
amount of final demand for each sector’s production. 
Final demand is usually distinguished by households, 
government, export and for capital creation and 
inventories; the model can translate the final demand 
for products of a specific sector into inputs from other 
sectors which support it. For example, increasing 
demand for cars will not only influence production in 
the automotive industry, but also in sectors which 
deliver direct inputs such as plastic and rubber, metal 
or electromechanical engineering, as well as indirect 
inputs such as chemical products or other types of 
material. Lower government expenditure on healthcare 
will also influence the demand for pharmaceutical 
products or catering services in hospitals.

Figure 10.  National accounts data by expenditure category and by sector

Source:  Authors.

98

Guide to anticipating and matching skills and jobsUsing the information on productivity in different 
economic sectors, production can be translated into 
total demand for labour by economic sector. Further 
disaggregation of the employment data by occupation 
or other characteristics can be done if we have data on 
the structure of occupations in each employment 
category by sector. Labour force data on employment 
structure are usually used for this but some 
establishment data could provide similar 
information (50).

The model procedure can be summarised in the 
following steps (Meade, 2010):

(h)  multiply sector employment by the occupation 

coefficient matrix to obtain total employment by 
occupation by sector.

Input-output models are a useful tool and are relatively 
transparent. However, like other models they have 
several limitations following from the assumptions on 
which they are built. 

Basic input-outputs models assume that:

(a)  prices are fixed and do not change with changing 
demand. But in the real world increasing demand 
for a product may raise its price; 

(a)  calculate the value of each final demand vector 

(b)  productive relationships remain stable. The same 

(demand of households, government, for products 
of various industries), using either econometric 
equations or assumed values;

(b)  solve for sectoral output, using the total final 

demand and input-output matrix;

(c)  project labour productivity in sectors using time 

trends or assumptions; 

(d)  calculate employment in sectors from production 

and labour productivity;

(e)  project compensation per employee in sectors 

using equations or assumptions;

(f)  determine total compensation by sector as a 
multiple of compensation per employee and 
number of employees in the sector;

(g)  form macroeconomic aggregates, including totals 
of each final demand and value-added vector, GDP, 
personal disposable income, total employment and 
the unemployment rate;

final products need the same inputs from the same 
sectors all the time. But change in technology may 
require inputs from different sectors in the future, 
or an international supplier can take over inputs 
which had been delivered by a domestic sector in 
recent years;

(c)  there are no constraints on the supply side. Any 
change in demand will result in a prediction of 
proportional change in employment. But in 
conditions of rapid economic growth, in countries 
with a high share of the population at pre-
retirement age, there may not be enough labour 
and/or skills to meet rising demand. Skills mismatch 
can cause a significant supply side constraint;

(d)  everything is happening at the same time; the 
changes in demand influence the inputs and 
employment in the same period. But it is more 
realistic that, for example, the effect of changes in 
government expenditures would be sequential.

(50) However, in developing economies coverage of establishment surveys may be limited to the formal economy.

99

Developing skills foresights, scenarios and forecastsBox 15.  The Lotus model for Vietnam

The Lotus model for Vietnam was developed by Inforum (University of Maryland) in 2010 as part of the activities 
of an ILO project with the Ministry of Labour, Invalids and Social Affairs in Vietnam. It is a good example of the 
development of a quantitative employment model in a country where the labour market information and analysis 
has clear limitations. It shows that the development of a model can, at the same time, be an incentive for the 
development of an effective labour market information and analysis system linked to policy development 
(Sparreboom, 2013).
An inter-industry macro model is a one that builds up macroeconomic totals from industry detail, and uses the 
input-output solution at its core. The Lotus model currently produces a forecast for 10 years, up to 2020. It 
provides forecasts of input-output accounts at the level of 16 industries, the macroeconomic aggregate of final 
uses in current and constant prices, and provides employment projections for 21 industries comprising the 
Vietnam economy. Detail by type of occupation is produced for 53 occupational categories, including several 
aggregate categories.
Example Lotus model results: manufacturing, which is the second largest sector in terms of employment 
(6.6 million in 2008), is not projected to grow as fast as in the recent past. This sector is very much influenced by 
the growth of net exports. The projection is for both export and import growth to slow compared to 2000--07. 
This is due partly to a slowing in global trade as a result of the global economic crisis, but also to increases in 
import requirements. Increases in domestic investment stimulate a strong demand for imports, as does strong 
growth in household and government final consumption. The slower net export growth results in slower projected 
growth in manufacturing output. This factor, along with fairly strong projected productivity growth, results in an 
anaemic employment forecast for manufacturing. However, it should be noted that this projection is extremely 
sensitive to the growth of net exports, particularly the exports component, during the period 2014--20.

Employment growth rates (Vietnam Lotus model)

Sources:   Meade (2010) and Vietnam Ministry of Labour, Invalids and Social Affairs (2011).

100

Guide to anticipating and matching skills and jobsDepending on data availability, a more advanced core 
of input-output models can be developed to overcome 
these limitations and produce additional analytical 
outputs. For example, changes in technology over time 
can be modelled using input-output data from several 
years, while prices can be made endogenous. More 
detailed input-output tables allow us to develop 
scenarios for more specific products and sectors. 
Using social accounting matrices instead of input-
output tables makes it possible to distinguish between 
different types of household or analyse the impact of 
taxes and government spending in more detail. The 
dynamic variants of input-output or social accounting 
matrices models allow us to consider the time 
dimension. 

The relatively high flexibility of input-output models 
makes them applicable in both developing and 
developed countries. Additional developments of 
input-output models, such as price dynamics or supply 
side constraints, are already moving towards more 
sophisticated CGE models.

Breaks in series, missing data or insufficient coverage 
of the economy are very often obstacles to robust 
modelling in transition and developing countries. The 
results of quantitative forecasts can only be as good as 
the input data, and the development and calibration of 
a quantitative model takes time. Expert evaluation and 
qualitative approaches can be a valid and useful tool to 
fill these gaps, at least during the earlier stages of 
development of the anticipation system.

Both these reasons provide relevant justification for 
combining quantitative modelling with qualitative 
methods and expert judgements. Collopy and 
Armstrong (cited in Moniz, 2008) argue that the 
accuracy of forecasts improves if they include some 
judgement elements, for example:

(a)  combination of judgement forecasts with 

extrapolation;

(b)  adjustment of current status by judgement;

(c)  use of judgement, not computer, to adjust outliers;

(d)  adjustment of trend by judgement (52).

8.4. 

 Qualitative inputs into quantitative 
forecasts and expert evaluation (51)

In practice, there are many approaches to combining 
quantitative forecasts with qualitative methods.

Given the inherently uncertain nature of future 
developments, there are some doubts about the value 
of historic information for future forecasts. ‘This time is 
different’ is a slogan that has been used many times to 
indicate paradigm shifts in the economy, in labour 
markets or within technologies. This critical view of the 
extrapolative capacity of history for future 
developments is an important reason to think about 
expert evaluation in skills forecasting. 

A frequently used option is a qualitative evaluation 
and interpretation of quantitative forecasting results. 
In the Netherlands quantitative results are discussed 
within a group of (external) experts from employer 
associations, sector councils and government 
agencies. This feedback is included in the 
deliberations about the final interpretation of the 
quantitative results. 

In some countries qualitative methods are used to 
provide directs inputs into a quantitative forecast. The 

(51) This section has benefited from consultations provided by SkillsNet members, especially by Hector Politt (Cambridge Econometrics), António 

Moniz (ITAS-Karlsruhe Institute of Technology) and Annette Cox (Institute for Employment Studies).

(52) In addition to the use of judgements in forecasts, the paper also suggests other tips on how to make a forecast more accurate

101

Developing skills foresights, scenarios and forecastsvolume and form in which the qualitative aspects 
influence the final outcomes of the projection vary. 

The E3ME model used for Cedefop European forecasts 
produces the initial projections, based on the 
estimated model parameters, historical data and 
assumptions about economic and population growth. 
Then the initial results are shared with a group of 
national experts who provide their feedback; they may 
also bring information on likely future trends which the 
model was not able to take into account. 

The Canadian COPS model includes several entries 
based on qualitative judgements. In the school-leavers 
component of the model, analysts prepare a set of 
matrices which only permits employment of a graduate 
in a related occupation. ‘The constraints are quite 
lenient and workers are allowed to move to any 
occupation within the same skill level (as defined by 

the level of study usually required) as well as numerous 
related occupations in other skill levels. The goal of the 
constraining process is to limit the number of 
post-secondary graduates who move into low-skilled 
occupations’ (Ignaczak, 2011). Comparing this ex ante 
scenario, prepared by analysts, with the ex post 
scenario which models actual outcomes that 
postsecondary graduates encounter in the labour 
market, can support analysis of education-occupation 
mismatch. Other expert inputs in the COPS model are 
related to the vetting of projected trends in expansion 
demand and to the imbalances indicators. In the latter, 
a qualitative recent labour market condition 
assessment is applied to adjust the implicit assumption 
of the model that the occupation is balanced in the 
base year (Table 16).

Table 16. 

 Canadian COPS model: the outlook for labour market conditions by occupation is 
determined using observed conditions in recent years and projected trends in  
jobseekers and job openings

Projected gap 
between future 
job openings and
 jobseekers

Job openings  
>  
jobseekers

Job openings  
=  
jobseekers

Job openings  
<  
jobseekers

Recent labour  
market conditions

Shortage

Balance

Surplus

Source: HRSDC http://www23.hrsdc.gc.ca

Shortage

Shortage

Balance

Shortage

Balance

Surplus

Balance

Surplus

Surplus

102

Guide to anticipating and matching skills and jobsSometimes the qualitative inputs are used to form a 
whole component of the forecast. There is no robust 
model for forecasting employment by sectors in the 
Czech Republic, so expert scoring of future productivity 
and employment in the sectors was used in 
combination with extrapolation to break down total 
employment into sectors. The weight of the expert 
opinion for the final projection was generally 50%; it 
was higher in sectors more likely to be influenced by 
the economic crisis (53) or more sensitive to political 
decisions.

The ultimate decision on how to combine quantitative 
forecasting with expert inputs depends on the country 
context. Data gaps, models at earlier stages of 
development, forecasting in rapidly changing conditions 
or ambition to cover new phenomena in the forecast 
may require more direct qualitative inputs. The 
availability of high-quality long time series and well 
developed quantitative tools allows quantitative and 
qualitative results to be brought together at a later 
stage of the anticipation process, when the 
recommendations are formulated.

Within the German forecasting model for the Ministry 
of Labour, several qualitative studies on key drivers of 
future economic changes form the basis of qualitative 
scenarios that are used to adjust and guide quantitative 
models (Vogler-Ludwig et al., 2013).

A final example shows the use of expert judgements 
as an important integral component of a highly 
developed quantitative forecasting system. The US 
Bureau of Labour Statistics (BLS) uses expert 
evaluations in determining expected developments in 
individual occupations. While sectoral and economic 
trends use economic (forecasting) models, experts 
within the BLS are responsible for a view on the 
development of their occupations. These are experts 
working at the BLS who track occupations and their 
development over time as well as technology and 
organisational trends related to these occupations. The 
shares of occupations in sectors are adjusted for the 
target projection year based on the experts’ 
judgements.

(53) The forecast was prepared in 2009 the past employment data did not reflect the impact of the recession.

103

Developing skills foresights, scenarios and forecastsChapter 9. 
Components of skills forecasting

9.1.  Supply

Ideally, analysis and projections of the changing 
numbers of people holding formal qualifications should 
be built around a stock-flow model (Bosworth and 
Wilson, 2011), focusing explicitly on the inflows to the 
active population of individuals with different levels of 
qualifications. Such models allow for medium- to 
long-term trends in rates of acquisition of qualifications 
and economic activity rates, while also allowing for the 
outflows of individuals from the active population as 
they come to the end of their working lives. 

In the Netherlands, supply is generated based on 
demographic flow models of the Ministry of Education. 
These flow models determine the transition probability 
between the different stages of the education pathway, 
i.e. the proportion of students that leave one form of 
education for another, where these movements are 
separated by having obtained a degree or not. These 
flow forecasts by the ministry are, however, 
differentiated only by the level and type of education, 
not the fields. To allow for the fields of education the 
Research Centre for education and the labour market, 
ROA, has developed a module that allocates the flow 
outcomes of the ministry to the final education states 
by level and education (for up to 110 education types). 

Education choices are taken at various moments in life, 
influenced by, for example, economic motives, 
demographics, abilities and socioeconomic 
background. At national level, the distribution of a 
country’s population and labour force across the 
various levels of qualification is also affected by the 
state of the economy and government policy on 
education expenditure. 

However, such models are very demanding, 
requiring consistent data on stock and flows as 
well as information on the factors that drive 
behavioural choices. Development of a stock-flow 
model is often beyond the scope of skills forecasting 
projects; analyses are often restricted to a focus on the 
stocks of education attainment utilising simple 
methods (54). 

At macroeconomic level, other studies have explained 
the importance of more general economic factors. 
A study by the European Commission focused on 
average aggregate macroeconomic trends (Livanos and 
Wilson, 2008). It suggests that, at this level, the main 
factors affecting educational attainment can be 
summarised by the following equation. 

X = f(G,W,E,R,T,U,S)

(54) In the Netherlands, the use of supply flows is included in their forecasting model. Separate analysis on flows has been conducted by ROA, 

and the results from the two methods compared (Cörvers et al. 2010).

104

Guide to anticipating and matching skills and jobsHere X denotes average educational attainment, 
G denotes GDP growth, W denotes wages, E denotes 
the employment rate, R denotes returns to education, 
T denotes personal taxes, U denotes unemployment 
benefits, and S denotes education subsidies to 
encourage private investment in education (Livanos 
and Wilson, 2008). It is suggested that these factors 
have an impact on a country’s overall levels of 
education. However, there is some ambiguity in 
specifications, such as in the direction of causality: 
does education lead to GDP growth or is it an 
outcome from it? 

9.2.  Employment change

The overall level of employment by industry is usually 
derived from a macroeconomic model that takes 
feedback-loops into account. Overall employment by 
sector can then be translated by a separate model or 
module into occupations using shares of employment 
by occupation within each industry. In a similar fashion, 
translation of overall employment can be translated 
into qualifications demanded. In both cases, this can 
be done in various ways, ranging from holding the 
shares fixed, to fitting more complex non-linear 
regression trend models. 

Modelling employment shares can include:

(a)  fixed shares;

(b)  OLS logistic trend estimates;

(c)  OLS linear trend estimates;

(d)  OLS linear trend (moving) average estimates;

(e)  multinomial logistic regressions.

Which type of econometric model is chosen to 
translate sector development into demand by 
occupation or qualification depends on several aspects; 
the best choice depends on the data available for that 
particular industry and model fit of the various 
specifications. Gaps in the data, and statistical noise, 
can result in unstable estimates. Judgement is also 
required for the final choice, which is often based on 
the evidence of qualitative analysis and other sources. 
This overall approach can be regarded as focusing on 
the demand side. 

The number of people in employment by occupation is 
determined by multiplying employment per industry by 
the shares of occupations in that industry’s total 
employment, and then summing over all industries. 

The number of people in employment by qualification 
is determined by multiplying employment by 
occupation by the shares of qualifications in that 
occupation’s total employment, and then summing 
over all occupations. This process delivers a consistent 
set of employment estimates across all three 
dimensions (sector, occupation, qualification). 

Expansion demands for both occupation and 
qualification are the net change in employment from 
these equations, calculated between any two years. 
For example, total expansion demands by occupation 
between 2010 and 2020 can be calculated by 
subtracting the calculated values for 2010 from those 
for 2020. 

105

Developing skills foresights, scenarios and forecastsBox 16.  Examples of econometric specifications in Cedefop’s expansion demand

The projections of occupation and qualification shares are conducted separately, and are based on a similar 
methodology, in turn similar to the supply share estimates. Various factors might explain changes in occupation 
and qualification structure, and estimation of complex, behavioural models is complicated in practice, mainly due 
to data restrictions. This report presents results based on simple models that have time as the only repressors. 
However, in future stages of this project a behavioural element will be included in the analysis to provide better 
insights into factors that affect changes in occupations and qualifications structure. 
The models estimated include a range from the general form shown in equation: 

Sijt = F(time)

where Sijt denotes the share S of occupation i in industry j in time t. Three main methods of analysis were 
adopted. These are based on analysis of occupations/qualifications shares in employment extracted from the 
EU-LFS data, and adopting specifications as in the equation above. 
These range from simple extrapolation between fixed points, to various methods based on ‘line fitting’. The latter 
includes fitting: 
•  a linear trend [S = a + b*Time]; 
•  a log linear trend [Ln(S) = a + b*Time]; or 
•  a logistic equation [Ln(S/(1-S)) = a + b*Time]. 

For the shares to add up to 100% an ad hoc external constraint is imposed in all the above models.

9.3.  Replacement needs

Replacement demand focuses on what might be called 
‘permanent or semi-permanent’ withdrawals from the 
employed workforce. These include retirement, family 
care or other reasons for temporarily leaving the 
workforce, and emigration. 

Replacement demand often provides a very large share 
of the job openings in an economy. It easily surpasses 
the share of job openings based on changes in the 
employment structure, such as growing occupations or 
growing demand. Estimating replacement demand to 
identify job openings by qualifications or occupations 
can be an important part of the skills forecast. 

Projections of replacement demand require three main 
inputs:

(a)  a forecast of demographic development within a 

country;

(b)  a forecast of (changes in) participation, preferably by 

gender and age groups;

(c)  an estimate of the outflow by occupation/education 

category, gender and age group.

The outflow estimate by occupation or education, 
gender and age can be developed using the cohort 
component method, which is based on comparing the 
number of persons in the same age cohort at two 
different points in time (55). 

(55) The described methodology is based on that developed for the Netherlands (Cörvers et al., 2010) and also used for the European skill fore-

cast (Kriechel and Cörvers, 2009; Kriechel and Sauermann, 2010). Similar models using variants of the methodology are used in several other 
countries, both within and outside the EU (BLS, 2008; Fox and Comerford, 2008; Shah and Burke, 2001).

106

Guide to anticipating and matching skills and jobsFrom the labour force survey, it is possible to analyse 
the demographic composition of each occupation or 
education category. This makes it possible to estimate 
specific rates of outflows for each occupational class or 
education type. Projections of occupational 
employment typically focus on the total numbers of 
people who are expected to be employed in such jobs 
in the future. While these estimates can provide a 
useful indication of areas of change, highlighting the 
likely net ‘gainers’ and ‘losers’, they give a misleading 
impression of job opportunities and skill requirements. 
Even where the projections indicate significant 
employment decline over the medium term, there may 
nevertheless be quite good career prospects with 
significant numbers of new job openings. This is 
because, as long as significant numbers are still likely 
to be employed in the future, employers will need to 
replace those employees who leave because of 
retirement, career moves, mortality, or other reasons. 
Replacement demand may often dwarf any structural 
or expansion demand, resulting from growth in 
employment in a particular category. It can easily 
outweigh any negative changes from projected 
employment decline.

The main problem in estimating replacement demand 
is that official statistics place much more emphasis on 
measuring stocks of people in particular states, rather 
than flows from one state to another. Yet it is the 
measurement of such flows which is essential to 
estimating replacement demand, ideally requiring a full 
set of demographic accounts that traces individuals’ 
movements from one socioeconomic position (such as 
employment in a particular occupation) to another 
(such as retirement). In practice, such a complete set 
of accounts is rare even at national level. 

The information on outflow rates is often estimated 
using stocks of age cohorts within occupations for 
several years. Using the year-to-year changes, the 
outflow rates by occupation-age cohort can be 
estimated. However, these estimates may not allow 
discrimination between the reasons for the outflow 
that leads to replacement demand. 

Data on age structure are required since many of the 
flows, especially retirement, mortality and occupation 

mobility, are age-specific. Age structures vary 
significantly by occupation and education type. 
Differences in age structure across occupations influence 
replacement demand due to occupation mobility and 
retirement, which are age-related. Even inter-occupation 
mobility is affected differently over occupations. 

Realised retirement rates can vary by gender and 
by age and may differ for different occupation groups 
and education. Outflows at an older age are often 
tagged as belonging to early retirement or retirement. 
Within replacement demand models it is also possible 
to include a mandatory or statutory retirement age. The 
assumption is then made that all employees beyond 
that age will leave the occupation because of 
retirement.

Occupation mobility is an additional source of 
replacement demand in some occupations, although 
not for all. The full occupation mobility flow matrix 
indicates that some occupations, such as managers, 
tend to gain employment as people are promoted from 
other positions. The cohort component approach does 
not differentiate replacement demand due to 
occupation mobility; it only identifies net mobility. 

Another potential outflow is mortality. While losses due 
to death are not great for individual age groups up to 
the age of 65, they can cumulate to produce significant 
losses over an extended period. The focus of the 
cohort component methodology is to identify overall 
outflows over cohorts, irrespective of the cause 
(sickness, death, family obligations). 

The overall scale of change is dependent on the length 
of the period considered, as well as the opening stocks 
and the age structure of the current workforce. 
Replacement demand is also dependent on the level of 
occupation aggregation. With lower levels of 
aggregation, observed occupation mobility is lower. 
Mostly, projections of rates of outflow are assumed to 
be constant over time. The scale of structural or 
expansion demand (which in some cases may be 
negative) is usually modest compared with 
replacement needs; in most cases the latter offsets 
any negative change. 

107

Developing skills foresights, scenarios and forecastsThere are three components to the model: 

(a)  a forecast of demographic development within a 

country;

(b)  a forecast of (changes in) participation on the labour 

market, by gender and age groups;

(c)  an estimate of the outflow by occupation 

(education) category, gender and age group.

Components (a) and (b) are considered external to the 
replacement demand model; they are set to be 
consistent with the other elements of the model. 
Component (c) is derived using the cohort-component 
method described below. 

The first step in modelling future replacement demand 
per occupation class is a description of the inflow and 
outflow patterns by occupation class in a historical 
period. The historical experience of outflow of specific 
(age x gender) groups within an occupation or 
qualification is used to predict future outflows, based 
on the implicit assumption that past behaviour has 
something to tell us about future outflow. Any type of 
outflow coefficient can be used; the closer this outflow 
coefficient is to the true behaviour of labour market 
participants, the better. 

Because there are no appropriate data for mobility 
flows on the labour market, stock data are used. With 
the cohort components method, cohort change rates 

based on the number of persons of the same birth 
cohort who were employed at two different times can 
be calculated (Shryock et al., 1980). In essence, 
repeated cross -sections are used, comparing the 
outflow of workers through these methods. 

The second step in modelling is to translate these 
coefficients of outflow-percentages into the 
replacement demand by occupation class. This 
methodology measures only the net flow to or from a 
class (56). 

The third step is to project historically measured net 
replacement demand rates per age-gender group, 
measured by the estimated coefficient for a 
particular occupation class, onto the age-gender 
structure of workers at the beginning of the forecasting 
period and for each subsequent year. The model can be 
improved by correcting for participation rate changes, 
in which an increase in participation implies lower 
replacement demand. Higher worker participation 
rates manifest themselves in this model by lower 
outflow rates (57). 

Crucial factors in the estimation of replacement 
demand are the age and gender distribution of workers 
in a specific occupation. Reliable measures of this 
distribution in the base year, and in the past, make the 
estimates more reliable.

Box 17.  Simplified replacement demand: concentrating on retirement outflows

If detailed data on the age/occupation or age/qualification structure are unavailable, it is possible to approximate 
replacement demand by its main drivers. The biggest share of replacement demand is generated through 
retirement or early retirement. If the share of older workers is available by occupation (all workers above 55 years 
of age), assuming the outflow of these workers over the remaining period up to the legal retirement age is a 
workable approximation of the main share of replacement demand. 

(56) This means that replacement demand satisfied by returning workers of the same age cohort is not measured, so replacement demand is 

actually measured for newcomers on the labour market.

(57) For a more detailed description, see: Kriechel and Sauermann, 2010; Kriechel and van Thor, 2011.

108

Guide to anticipating and matching skills and jobsChapter 10. 
Specific issues in skills modelling

10.1.  Technological change

Technological change, changing trade patterns, 
outsourcing and different forms of production, can lead 
to important shifts in a country’s economic structure. 
This can lead to important changes in the sector, 
occupation and qualification structure. There is 
evidence of a shift from unskilled to skilled workers 
(Machin and van Reenen, 1998; Autor et al., 2003; 
Machin, 2001; Murray and Steedman, 1998), but also 
towards a polarisation of occupations into high skill and 
very low skill (Goos et al., 2009). It has been suggested 
that the main causes of this chahange are the use of 
real incomes and related changes in patterns of 
demand for goods and services, international trade and 
skill-biased technological change. 

Briscoe and Wilson (2003) used annual data from 
successive UK labour force surveys to model 
occupation trends over the period 1981 to 1999. They 
developed time series models for some nine 
occupation groups across 17 industry sectors. The 
variables they used included output, wages, 
unemployment, and export and import shares, which 
were combined with different technology (trend) and 
interactive dummy terms to identify the most 
significant determinants of the changing demand for 
skills.

Cörvers and Dupuy (2006) developed a model to 
explain the occupation structure of industry sectors in 
the Netherlands. They estimated the structural 
parameters of a model for the period between 1988 
and 2003 using system dynamics OLS techniques to 
account for the employment dynamics dependence 
across occupations and sectors of industry. Cörvers 
and Dupuy (2006) distinguish between 13 sectors of 
industry and 43 occupation groups. They argue that 
employment series by occupation and sector have both 
a long-term relationship with levels of value added, 
capital and R&D, reflecting the production technology 

specific to each sector, and a short-term relationship 
with changes in value added, capital and R&D. The 
results indicate that high-skill occupations have a 
negative and significant elasticity with respect to value 
added, but a large and significant positive elasticity 
with respect to R&D. Output expansion in a sector led 
to a decrease in employment in high-skilled 
occupations within that sector. However, this effect can 
be partly or fully compensated for by the 
complementarity of high-skilled workers with new 
technology, as indicated by the positive elasticity of 
employment in high-skill occupations with respect to 
R&D. Intermediate-skill occupations have a positive and 
significant elasticity with respect to capital. The results 
have been used to complement the expansion demand 
model, and to include these shifts into the allocation of 
sector developments to occupations. 

10.2.  Skills mismatch

One of the problems skill forecasting wants to address 
is to avoid skill mismatch, by no means a new 
phenomenon (58). Overeducation – people working in a 
job below their education level – signals 
overinvestment in education which are costly to 
society, while undereducation – people who work in a 
job above their education level – signals 
underinvestment in human capital that could result in 
productivity loss. The literature discusses over- and 
underqualification at individual level (such as the wage 
and welfare effects of skill mismatches, see Hartog, 
2000; Sloane, 2003; Leuven and Oosterbeek, 2011). 
These studies show that overqualified workers suffer 
from wage penalties relative to workers whose 
education is better matched to their jobs, whereas they 
earn more than others at their respective job level if 
they are overqualified.

(58) Richard Freeman’s 1976 book The Overeducated American was the first to touch on the issue of individuals working in jobs below their level 

of education. Ever since, there has been a lively debate as to the causes and consequences of skills mismatch (and overeducation in  
particular), and the body of literature growing.

109

Developing skills foresights, scenarios and forecastsThere is currently a tendency for people to attain 
higher- level qualifications in all economies. This move 
towards higher levels of education has the potential to 
result in overqualification if the skills demanded do not 
increase in line with supply. In general, it is expected 
that the increase in the number of people with 
higher-level qualifications grows broadly in line with the 
expected trend in skill demand, at least in advanced 
economies. Increase in the high educated workforce is 
essential to the development of a knowledge-based 
and innovative economy.

A perceptive theoretical framework is provided by 
Sattinger (2012), who introduces two concepts of skill 
mismatch: short-term qualitative skill mismatch and 
long-term qualitative skill mismatch. Short-term 
qualitative mismatch is a temporary mismatch that is 
the result of matching under imperfect information. Not 
all workers find adequate jobs for their qualifications, 
nor do all firms find the perfect candidate. The implied 
policy would be that the matching process should be 
organised more efficiently, either by making 
information more transparent or by decreasing the cost 
of its acquisition. Long-term qualitative mismatch is 
more structural; it is the result of changes in 
qualifications or skills demanded in jobs which are not, 
or only poorly, reflected in the worker’s education. The 
policy implication would be the need to anticipate such 
long-term mismatches and adapt education policies to 
react to such shifts. 

The skills mismatch and use of labour market 
information for better matching is the topic of Volume 1. 

10.3. 

Imbalances

Comparing current demand and supply projections is 
problematic, both practically and theoretically. Unless 
the two sets of results are based on common data and 
are carried out simultaneously, they cannot be directly 
compared. Various other conceptual and methodological  

issues regarding imbalances need to be considered with 
some care if misleading inferences are not to be drawn.

Initial projections of the demand for and the supply of 
skills are developed independently. Many adjustment 
mechanisms operating on the labour market reconcile 
imbalance and mismatches as they evolve: short-term 
influences include adjustments in wages and different 
kinds of mobility, as well as changes in the ways 
employers utilise available skills. In the longer term, 
both supply and demand adjust to imbalances and the 
short-term adjustments they have created. Generally, 
employers will not cease operating if they cannot find 
the ideal mix of skills but will work with what is 
available.

There are several techniques to identify and analyse 
imbalances. Koucky et al. (2010) describe the Australian 
and Canadian forecasting models, where supply and 
demand are matched, taking the differences between 
them as imbalances. In the Canadian model the 
emphasis is on the occupation side, while in Australia 
the emphasis is on education level and (broad) field. 

The Dutch procedure is to derive imbalances after 
some of the discrepancies have been solved. This 
should mimic the market mechanism that routinely 
solves minor discrepancies automatically. However, the 
procedure does not solve all imbalances, nor is that the 
intention. The substitution process often carries over 
imbalances from an initial education type to other 
related education types, which aim to solve the initial 
imbalance (Cörvers et al., 2010).

The ultimate goal of skill forecasting is to make 
predictions about future imbalances in supply and 
demand. Understanding the main outcomes of the 
forecasting exercise comes from presenting key trends 
in a comparable way. Bringing together supply and 
demand, however, is also the point at which all 
accumulated inaccuracies might show. In an ideal 
world, indicators would be insensitive to the 

110

Guide to anticipating and matching skills and jobsaccumulated error while showing the trends clearly. 
The results of the imbalance indicators should be seen 
as indicative of the situation that would evolve if 
current trends in sector, occupation and education level 
continue to develop in the same way. Within the 
imbalances it is shown that discrepancies will arise 
that have to be solved in the labour market. It is 
difficult to assess ex ante in which way such a market 
process will solve the imbalances. 

10.4. 

Indicators 

Indicators supply a translation of some aspects of the 
forecast outcome. They provide and summarise 
information that comes from within the forecasting 
model, or that combines it with external information. 
A good indicator translates outcomes to ease 
interpretation. A typical indicator shows the demand 
pressure on specific occupations or qualifications that 
indicate imbalances which might lead to recruitment 
difficulties in the future. The opposite can be true, with 
the indicator showing, for a given qualification, an 
oversupply which indicates poor labour market 
opportunities for those achieving the qualification. 

10.5.  Regional forecasting

Skills forecasting in many cases takes place at regional 
rather than national level: regional forecasts can exist 
alone or alongside national forecasts. It is natural to 
operate at regional level, as most countries match 
supply and demand predominantly using regional 
supply and regional demand. Skill imbalances can then 
be addressed by regional actors who are often the 
relevant decision-makers. 

Regional forecasting follows the main methodologies 
presented in the second part of the guide as well as 
qualitative elements from the first part. Some specific 
regional problems are addressed in this part, while we 
do not repeat the methodological issues already 
addressed for national forecasting. 

Ideally, a regional forecast is based on a national model 
and forecast. In such top-down approaches, the results 
of individual regional forecasts reflect overall 
development in the economy. Wilson (2008) sets out 
the requirements for regional forecasts that are 
embedded in national forecasts: they take national 
effects into account and make full use of any regional 
information that might be available (59). 

Many more possible indicators can be derived directly 
or indirectly. An early discussion of indicators that can 
be used in a quantitative forecasting framework to 
support the work of guidance counsellors, companies 
and school leavers looking for adequate training can be 
found in De Grip and Heijke (1988).

Regional skills forecasting will not always have access 
to a national skills model, or may not always be 
embedded in one. Many forecasts can only make use 
of a regional setting, in which the national part of the 
development has to be approximated more or less 
precisely.

(59) See Kriechel et al. (2008) for a description of Dutch regional skills forecasting, and the contributions on regional forecasting in Knobel et al. 

(2008), especially Wilson (2008).

111

Developing skills foresights, scenarios and forecastsChapter 11. 
Examples of skills forecasting

Many countries have anticipatory measures in place 
and others are building and developing systems for 
skills anticipation. Approaches vary, but all have a 
common objective: to improve the match between 
labour demand and supply. There is a clear shift from 
top-down, manpower planning towards informing all 
labour market participants about the knowledge and 
skills required, and changes in job contents, in different 
occupations.

There are various approaches to achieve these aims, 
combining different methods and the efforts of many 
different institutions and projects. These range from 
analysis of quantitative/qualitative trends in the labour 
market, through development of policy proposals to 
bridge the quantitative/qualitative gap, to fostering 
cooperation between firms and TVET (60) providers. 
However, implementation of policy in practice, and 
creating programmes/actions to bridge the gaps 
identified in the results, remain the most difficult 
points.

Many countries carry out regular skills forecasts. Most 
long-term forecasts are done at national level, for 
example in Cyprus, France, Germany, Malta, Norway 
and the UK. Short-term forecasts are undertaken more 
generally at regional or local level, often through public 
employment services (PES), as in Latvia, Austria, 
Poland and Slovenia.

Other countries are currently developing their 
forecasting methodologies, such as Romania and 
Slovakia, including national strategies for skills 
anticipation, as in Bulgaria, the Czech Republic, Latvia 
and Poland.

Many countries, including Bulgaria, Germany, Estonia, 
Spain, France, Italy, Cyprus, Lithuania, Luxembourg, 
Hungary, Poland, Slovakia and Finland, conduct 
employer surveys or annual labour market forecasts to 
inform TVET provision planning. Cyprus, Finland, 
France, Malta, Norway and Portugal conduct sector 

skills needs studies. In many newer EU Member 
States, information on skill needs is collected in one-off 
studies or projects. Some countries, including 
Germany, Spain, France, Latvia, Portugal and Romania, 
have multilevel schemes for policy-making and 
research in anticipating skill needs, linking anticipatory 
activities at national, regional and sector levels.

Most countries acknowledge that methods to 
anticipate skill needs have to improve. Quantitative 
and qualitative anticipation methods and results are 
not always consistent, and regional anticipation 
systems also need improving. Most countries plan to 
develop models and improve methodologies. 
Norway and Sweden will broaden the tasks of 
existing bodies to include anticipation and identification 
of skills needs. Spain has already set up a national job 
market observatory network. Social partners also 
emphasise the importance of plans to establish 
national information systems, develop networks for 
skills assessment and participate in wider EU studies.

11.1.  Pan-European forecasts: Cedefop

Anticipation systems across EU Member States, 
although having many similarities in features and 
development trends, are quite different in many 
respects, such as in their detailed methodology and 
data sources used. In consequence, they do not 
produce comparable data at European level.

Developing pan-European anticipation systems is 
important for providing comparable data on future 
challenges across Europe. Cedefop (with the support 
of the European Commission) has since 2008 
produced regular forecasts of skills supply and demand 
for the EU and each Member State up to 2020, 
including details by broad sector, occupation groups 
and education levels. 

(60) TVET is an international term used to denote vocational education and training (VET). The two terms are used interchangeably throughout 

this publication

112

Guide to anticipating and matching skills and jobsTo complement these quantitative forecasts, the 
European Commission has also published a series of 
studies on emerging competencies and future needs. 
These cover 18 economic sectors and provide 

a transversal analysis of the evolution of skills needs in 
the selected sectors, taking into account their global, 
national and regional contexts. The aim is to anticipate 
possible changes in jobs and skills needs up to 2020. 

Box 18. 

Imbalances in the Cedefop forecast: RAS procedure

The following contrived example aims to describe the module to reconcile supply and demand. From the initial, 
individual forecast of supply and demand, a net supply by education level is calculated, being the supply minus 
unemployment. To match supply (S) and demand (D), we reconcile them using an RAS procedure, within which, 
the allocation of education levels within occupations is done such that the total of all workers with an education 
level across occupations matches the predicted supply totals.
Consider a projection with the base year 0 and a projection into the year 5. Within that period, supply and demand 
are extrapolated independently. Supply is based on the population trends and the development of the education 
shares within different age groups, while demand is extrapolated from the sectoral forecast and the projected 
within sector occupation and education shifts, based on past developments. 
This implies that the allocation of education levels within occupations (by sector) is a good prediction of 
adjustments that an occupation has experienced in the past. Based on historic evidence, we can assume that, if 
larger shifts in the education composition of an occupation have taken place, it will be possible to achieve such 
shifts in the future as well. 
While the reconciliation takes place, the supply of education (levels) has to match the implied demand for 
education. Let us take a simple example. The contrived example depicted in the table below exemplifies the 
working and the interpretation of the processes within the RAS framework. In the example we assume to have 
three occupation and three education levels. Three occupation levels are sufficient to show all relevant 
relationships, but can easily be extended to any number of occupations. 

Starting values within a contrived RAS example

Occ./Edu.

Demand forecast

Low  
education

Intermediate 
education

High  
education

Total 
row

Supply forecast

Occupation 1

Occupation 2

Occupation 3

Total column

175

200

75

450

Source: Kriechel and Wilson (2010)

100

75

100

25

200

200

50

80

25

155

150

50

20

25

95

450

175

200

75

113

Developing skills foresights, scenarios and forecastsThe table has several elements: it specifies the outcome of the supply forecast of education in the first row, 
i.e. the number of low-, intermediate-, and high-educated that will be employed. We assume that they will be 
employed, as the supply depicted here is net of unemployment. Then there is the outcome of the demand 
forecast in terms of the total number in occupations 1 to 3 in the first column. These supply and demand 
outcomes can be seen as fixed, providing a frame in which the underlying allocation of education within 
occupations has to be fitted. The last column and row sum the allocated workers within the occupation (column) 
and within education (row). In the example, we used the estimated demand allocation of education levels within 
occupations as starting values (numbers are in grey, inner matrix of the table). The interpretation is simple: these 
are the values that firms would demand if the current trend of development could go on undisturbed by any supply 
problems. 

Final values within a contrived RAS example

Occ./Edu.

Demand forecast

Low  
education

Intermediate 
education

High  
education

Total 
row

Supply forecast

Occupation 1

Occupation 2

Occupation 3

Total column

36

200

75

450

Source: Kriechel and Wilson (2010)

100

75

52

12

200

200

61

111

28

155

150

78

37

35

95

450

175

200

75

We can already interpret the discrepancies the table gives us: total demand by education is higher in lower 
education than the supply (200 demanded, 100 supplied), and lower in the intermediate (155 demanded, 
200 supplied) and higher education (95 demanded, 150 supplied). The economy in this contrived example has 
thus shifted to provide more intermediate- and higher-educated candidates than required initially, while the 
demand for low-skilled workers cannot be met.
Within our economy we have to resolve the supply and demand discrepancies. Firms employing the various 
occupations would now try to fill the occupations with workers of different levels of education from what was 
initially demanded. In our case, intermediate- and higher-educated workers will fill the gaps that the lower-
educated have left. 
The RAS procedure would, at this point, reweight the inner matrix using the ratio of total demanded in education 
level over total supplied in education level. This scales demand by education level to match supply, but the 
occupation totals will – after this first (half) iteration of the RAS procedure – not match the number required. To 
resolve this, the matrix is reweighted again, using as weight the total of occupation in row over the total 
demanded in occupation. This reweights the matrix to ensure that the total demand in occupation is met. Given 
that this first part of the procedure will knock the total of the demanded education level somewhat away from the 
supplied level, we have to repeat the two steps for several iterations until the matrix converges to values where 
demand and supply match. The factors of the RAS procedure used to adjust the cells can be related to underlying 
(relative) price changes, i.e. wages that adjust to balance supply and demand. 

114

Guide to anticipating and matching skills and jobsThe current policy agenda favours further 
developments in this area, and many EU Member 
States are positive about developing a common 
approach, or a common European tool, for anticipating 
skill needs. A key problem is the diversity of the 
current methods, tools, statistics and definitions used. 
But most of the countries also mention different 
stages of economic development as a key issue. 
Nevertheless, countries recognise the need for a 
common approach which should consider the data 
limitations and local knowledge. This should 
complement rather than substitute for activities at 
national level, as well as representing added value for 
EU Member States by pulling together existing 
analyses and research.

115

Developing skills foresights, scenarios and forecastsREFERENCES AND RESOURCES  
ON PART B

Cedefop, ETF, ILO. (2015). Guide to anticipating and matching skills and jobs:

Rihova, H. (2015). Volume 1: Using labour market information. Luxembourg: Publications Office. Jointly published 
by Cedefop, ETF and ILO.

Wilson, R.A.; Tarjani, H.; Rihova, H. (2015). Volume 3: Working at sector level. Luxembourg: Publications Office. 
Jointly published by Cedefop, ETF and ILO.

Andersen, T.; Feiler, L. (2015). Volume 4: What is the role of employment service providers. Luxembourg: 
Publications Office. Jointly published by Cedefop, ETF and ILO.

Corbella, T.; Mane, F. (2015). Volume 5: Developing and running an establishment skills survey. Luxembourg: 
Publications Office. Jointly published by Cedefop, ETF and ILO.

Schonburg, H. (2015). Volume 6: Carrying out tracer studies. Luxembourg: Publications Office. Jointly published by 
Cedefop, ETF and ILO.

Autor, D.H. et al. (2003). The skill content of recent technological change: an empirical explanation. Quarterly 
journal of economics, November, Vol. 118, No 4, pp. 1279--1333. http://dx.doi.org/10.1162/003355303322552810 

Barry, F. et al. (2003). The Czech economic transition: exploring options using a macrosectoral model. Economics 
of transition, Vol. 11, No 3, pp. 539-567. http://dx.doi.org/10.1111/1468-0351.00156 

BLS (2008). Estimating occupational replacement needs. In: BLS (ed.). Occupational outlook handbook: statistical 
supplement (2008--09th edition). Washington: BLS, Bureau of Labour Statistics, pp. 125--129.

Bosworth, D.; Wilson, R.A. (2011). Stock flow model and other modelling developments. Thessaloniki: Cedefop. 
Forecasting skill supply and demand in Europe to 2020: technical report.

Bradley, J. (2000). Policy design and evaluation: EU structural funds and cohesion in the European periphery.  
In: den Butter, F.; Morgan, M. (eds). Empirical models and policy-making: interaction and institutions. London: 
Routledge.

Bradley, J. et al. (1995). Modelling in the EU periphery: the Hermin project. Economic modelling, Vol. 12, No 3,  
pp. 219--220. http://dx.doi.org/10.1016/0264-9993(95)00006-5 

Bradley, J. et al. (2005). Hermin: a macro-model framework for the study of cohesion and transition. In: Bradley, J. 
et al. (eds). Integration, growth and cohesion in an enlarged European Union. Berlin: Springer, pp. 207--243.

Briscoe, G.; Wilson, R.A. (2003). Modelling UK occupational employment. International journal of manpower,  
Vol. 24, No 5, pp. 568--589. http://dx.doi.org/10.1108/01437720310491099

Cedefop (2010). The skill matching challenge: analysing skill mismatch and policy implications. Luxembourg: 
Publications Office of the European Union.

Cörvers, F.; Dupuy, A. (2006). Explaining the occupational structure of Dutch sectors of industry, 1988--2003. 
Maastricht: ROA, Research Centre for Education and the Labour Market, Maastricht University.

116

Guide to anticipating and matching skills and jobsCörvers et al. (2010). Methodiek arbeidsmarktprognoses en -indicatoren 2009--14 [Methodology labour market 
forecasts and indicators 2009--14]. Maastricht: ROA, Research Centre for Education and the Labour Market, 
Maastricht University. ROA technical report; ROA-T-2010/5. http://www.roa.nl/pdf_publications/2010/ROA_
TR_2010_5.pdf 

D’Alcantara, G.; Italianer, A. (1982) A European project for a multinational macrosectoral model. Document MS 11, 
DG XII, Brussels: European Commission.

De Grip, A.; Heijke, J.A.M. (1988). Labour market indicators: an inventory. Maastricht: ROA, Research Centre for 
Education and the Labour Market, Maastricht University. ROA-W-1988/1E.

El Achkar Hilal, S. et al. (2013). The Philippines employment projections model: employment targeting and 
scenarios. Geneva: International Labour Office. Employment working paper; No 140. http://www.ilo.org/wcmsp5/
groups/public/---ed_emp/---emp_elm/---trends/documents/publication/wcms_213378.pdf 

Fox, R.; Comerford, B. (2008). Estimating replacement demand: lessons from Ireland. International journal of 
manpower, Vol. 29, No 4, pp. 348--361. http://dx.doi.org/10.1108/01437720810884755 

Goos, M. et al. (2009). Job polarisation in Europe. American economic review papers and proceedings, Vol. 99,  
No 2, May, pp. 58--63.

Hartog, J. (2000). Overeducation and earnings: where are we, where should we go? Economics of education 
review, Vol. 19, No 2, pp. 131-147.

Ignaczak, L. (2011). Projecting labour market needs in Canada: the COPS national model after 30 years. Paper 
presented at the Warwick international symposium on employment and skills forecasting, 29 September 2011.

ILO (1997). Private employment agencies convention No 181. Convention concerning private employment 
agencies, adopted by the International Labour Organization at Geneva on 19.6.1997. http://www.ilo.org/dyn/
normlex/en/f?p=NORMLEXPUB:12100:0::NO::P12100_ILO_CODE:C181

ILO (2008). Conclusions on skills for improved productivity, employment growth and development: international 
labour conference 2008. Geneva: International Labour Office. http://www.ilo.org/wcmsp5/groups/public/@ed_
emp/@ifp_skills/documents/publication/wcms_103457.pdf 

ILO (2009). ILO support for the role of public employment services in the labour market. Paper submitted for 
debate and guidance at the Committee on employment and social policy, Geneva, on 09/10/2009. http://www.ilo.
org/wcmsp5/groups/public/@ed_norm/@relconf/documents/meetingdocument/wcms_115454.pdf 

ILO (2012). International standard classification of occupations: structure, group definitions. ISCO-08, Vol. 1. 
Geneva: International Labour Organization. http://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/---publ/
documents/publication/wcms_172572.pdf

ILO-PERI (2011). Employment opportunities and skills requirements for building the green economy: a survey of 
research methods. Amherst, MA: PERI, University of Massatchusetts-Amherst [unpublished].

Knobel, C. et al. (eds) (2008). Regional forecasting on labour markets. Mering: Rainer Hampp Verlag.

Kriechel, B.; Cörvers, F. (2009). The Skillsnet project on medium-term forecasts of occupational skill needs in 
Europe: replacement demand module. Thessaloniki: Cedefop.

117

Developing skills foresights, scenarios and forecastsKriechel, B.; Sauermann, J. (2010). Replacement demand: methods and results. Thessaloniki: Cedefop. Forecasting 
skill supply and demand in Europe to 2020: technical report; No 5. 

Kriechel, B.; van Thor, J. (2011). Development of a replacement demand module for Korea: final report. Maastricht: 
ROA, Research Centre for Education and the Labour Market, Maastricht University.

Kriechel, B.; Wilson, R.A. (2010). Reconciling the demand for and supply of skills. Thessaloniki: Cedefop. 
Forecasting skill supply and demand in Europe to 2020: technical report; No 8. 

Kriechel, B. et al. (2008). Regional labour market forecasts in the Netherlands. In: Knobel, C. et al. (eds). Regional 
forecasting on labour markets. Mering: Rainer Hampp Verlag.

Kvetan, V. et al. (2006). Model Hermin. Ekonomický casopis/Journal of economics, Vol. 54, No 10, pp. 994--1008.

Leuven, E.; Oosterbeek, H. (2011). Overeducation and mismatch in the labour market. Handbook of the economics 
of education, Vol. 4, pp. 283--326.

Lindley, R.M. (1994). A perspective on IER forecasting activities and future developments. Heijke, H. (ed.). Labour 
market forecasts by occupation and education: the forecasting activities of three European labour market research 
institutes. Massachusetts: Kluwer Academic, pp. 167--179.

Livanos, I.; Wilson, R.A. (2008). Trends in stocks of those acquiring qualifications: multilogit models based on LFS 
data. Paper presented at the SkillsNet workshop on medium-term forecast of skills supply in Europe, Cedefop, 
Thessaloniki, 2 and 3 June 2008.

Lutz, C. et al. (2003). Forecasting the interindustry development of the German economy: the model Inforge. 
Osnabrück: GWS, Institute of Economic Structures Research. Discussion paper; No 2. http://www.gws-os.de/
Downloads/gws-paper03-2.pdf 

Machin, S. (2001). The changing nature of labour demand in the new economy and skill-biased technology change. 
Oxford bulletin of economics and statistics, Vol. 63, Supplement 1, pp. 753-776.

Machin, S.; Van Reenen, J. (1998). Technology and changes in skill structure: evidence from seven OECD 
countries. Quarterly journal of economics, Vol. 113, No 4, pp. 1215--1244. http://dx.doi.org/10.1162/003355398555883

Meade, D.S. (2010). Lotus: an interindustry macro model for Vietnam. College Park: Inforum, University of 
Maryland.

Meagher, G.A. et al. (2012). Interfacing a CGE labour market model with the E3ME multisector macroeconomic 
model. Thessaloniki: Cedefop. Forecasting skill supply and demand in Europe 2020: draft technical paper.

Moniz, A.B. (2008). Assessing scenarios on the future of work. Enterprise and work innovation studies, Vol. 4, 
No 4, pp. 91--106.

Murray, A.; Steedman, H. (1998). Growing skills in Europe: the changing skill profiles of France, Germany, the 
Netherlands, Portugal, Sweden and the UK. London: Centre for Economic Performance, London School of 
Economics and Political Science.

OECD (1965). The Mediterranean regional project: an experiment in planning by six countries. Paris: OECD. 

OECD (2011). Towards an OECD skills strategy. http://www.oecd.org/edu/47769000.pdf

Parnes, H.S. (1962). Forecasting educational needs for economic and social development. Paris: OECD.

118

Guide to anticipating and matching skills and jobsRadvanský, M.; Frank, K. (2010). Modelling the impact of postponed implementation of EU structural funds: the 
case of Slovakia. In: EcoMod2010: international conference on economic modelling [online]. Istanbul: Istanbul Bilgi 
University. http://www.ecomod.org/files/papers/1371.pdf

Sattinger, M. (2012) Assignment models and quantitative mismatches. Albany: Department of Economics, 
University of Albany. http://www.albany.edu/economics/research/seminar/files/Michael%20Sattinger.pdf 

Shah, C.; Burke, G. (2001). Occupational replacement demand in Australia. International journal of manpower,  
Vol. 22, No 7, pp. 648--663.

Shryock, H.S. et al. (1980). The methods and materials of demography. Washington DC: US Bureau of Census. 

Sloane, P.J. (2003). Much ado about nothing? What does the overeducation literature really tell us.  
In: Büchel, F. et al. (eds). Overeducation in Europe. Cheltenham: Edward Elgar Publishing, pp. 11--49.

Sparreboom, T. (2013). Labour market information and analysis systems. In: Cazes, S.; Verick, S. (eds). Perspectives 
on labour economics for development. Geneva: International Labour Office, pp. 255-282.http://www.ilo.org/
wcmsp5/groups/public/---dgreports/---dcomm/---publ/documents/publication/wcms_190112.pdf 

Van Eijs, P. (1994). Manpower forecasting in the western world: the current state of the art. Maastricht: ROA, 
Research Centre for Education and the Labour Market, Maastricht University. ROA-RM-1994-1/E.

Van Notten, P. (2006). Scenario development: a typology of approaches. In: OECD. Schooling for tomorrow. Paris: 
OECD. http://www.oecd.org/site/schoolingfortomorrowknowledgebase/futuresthinking/scenarios/37246431.pdf 

Vietnam Ministry of Labour, Invalids and Social Affairs. 2011. Vietnam employment trends 2010. Hanoi: ILO 
Country Office for Vietnam and National Centre for Labour Market Forecast and Information, Bureau of 
Employment. http://www.ilo.org/wcmsp5/groups/public/---asia/---ro-bangkok/---ilo-hanoi/documents/publication/
wcms_151318.pdf 

Vogler-Ludwig, K. et al. (2013). Arbeitsmarkt 2030: eine strategische Vorausschau auf die Entwicklung von Angebot 
und Nachfrage in Deutschland [Labour market 2030: A strategic foresight on the development of supply and 
demand in Germany]. Bielefeld: W. Bertelsmann.

Wilson, R.A. (2008). Local and subregional medium-term skills forecasting in the UK. In: Knobel, C. et al. (eds). 
Regional forecasting on labour markets. Mering: Rainer Hampp Verlag, pp. 13--37.

Wilson, R.A. et al. (2004). Developing a national skills forecasting tool for South Africa. Pretoria: South African 
Department of Labour.

Zaleski, J. (2009). Regionalisation of Hermin macroeconomic modelling framework in Poland. Wroclaw: Regional 
Development Agency working paper.

Zaleski, J. et al. (2004). Revision of the Polish Hermin model using the new national accounts: 1995--02. Wroclaw: 
Regional Development Agency working paper. http://www.warr.pl/pliki/2005/Projekty/Marek/HERMIN%20
2-Report%201%20for%20the%20Ministry%20of%20Economy-eng.pdf

119

Developing skills foresights, scenarios and forecastsACRONYMS

Bureau of Labour Statistics

classificação Brasileira de ocupações [Brazilian classification of occupations]

computable general equilibrium

European Union

gross domestic product

International Labour Organization

Institute for the Future

Institute for Prospective Technological Studies

international standard classification of education

international standard classification of occupations

Joint Research Centre

labour force survey 

methods combination matrix

Nomenclature statistique des activités économiques dans la Communauté européenne 
[Statistical classification of economic activities in the European Community]

nomenclature of territorial units for statistics

ordinary least squares

research and development

science and technology

National Service for Industrial Training

small and medium-sized enterprises

strengths, weaknesses, opportunities and threats

technical and vocational education and training

United Nations Industrial Development Organisation

United States

United States dollar

BLS

CBO

CGE

EU 

GDP

ILO

IFTF

IPTS

ISCED

ISCO 

JRC

LFS

MCM

NACE

NUTS

OLS

R&D

S&T

SENAI

SME

SWOT

TVET

UNIDO

US

USD

120

Guide to anticipating and matching skills and jobsANNEXES: 
COUNTRY CASE STUDIES ON 
FORESIGHTS AND SCENARIOS

ANNEX 1.  Brazil 

ANNEX 2. Germany 

ANNEX 3. Finland 

ANNEX 4. Japan 

ANNEX 5. Korea 

ANNEX 6. The European Union 

ANNEX 7. Skills foresight in the EU enlargement region 

ANNEX 8. Russia 

ANNEX 9. Australia 

ANNEX 10. United Kingdom 

ANNEX 11. United States of America 

123

137

147

156

160

163

176

184

196

203

211

121

Developing skills foresights, scenarios and forecastsIntroduction

The annex builds on a large range of country case 
studies. The review is necessarily selective, based on 
the following criteria:

(a)  foresight processes on skills are in place in a 

country;

(b)  available country information gives a 
comprehensive enough picture;

(c)  the processes are able to create foresight products;

(d)  the products are used by their potential users (for 

example, are fed into policies).

Given the diversity of socioeconomic contexts across 
countries it is virtually impossible to present only a few 
definitive examples that represent the best practices. 
The cases presented here provide a mix, taking into 
account different stages of economic development, 
different geographic regions and different cultural 
backgrounds.

A review of the country cases reflects the fact that 
foresight of future skill needs is a young discipline. An 
ideal example of a functioning permanent system of 
foresight for anticipating skill needs is not available, 
and it is likely that no such system yet exists in any 
country. The most appropriate experience comes from 
Brazil. Other examples show some elements of skills 
foresights which are incorporated into an anticipation 
system for the future development of the society and 
economy, or they describe projects which could be a 
basis for subsequent activities in skills foresighting: 
Russia is an interesting example from this perspective. 
Nevertheless, all the cases presented bring knowledge 
which could be useful for those thinking about or 
already implementing a foresight exercise.

122

Guide to anticipating and matching skills and jobsANNEX 1.  
Brazil

A1.1. 

 Economic and social context

Brazil is Latin America’s largest economy and the world’s 
fifth-most populous country (200 million inhabitants). It 
is still classified as a developing country by the World 
Bank but is also seen as one of the most dynamically 
expanding economies and is included in the BRIC group 
with other important emerging economies (61). Although 
its economy extensively relies on exports of traditional 
agricultural and primary goods, the service sector has 
become the main driver of growth during the past 
couple of decades (62).

Brazil is geographically and socially heterogeneous. It is 
the world’s fifth largest country in terms of surface 
area, and administratively a federation of 26 self-
governed states (and one federal district), for statistical 
purposes gathered into five regions. Profound territorial 
disparities in terms of economic and social 
development can be found not only across these areas 
but also between municipal and rural areas. Despite 
the rapid development of high-tech sectors 
(nanotechnology, biotechnology, information and 
communications technology) the Brazilian economy still 
possesses many typical features of developing 
economies, such as economic dualism (almost a half of 
the 100 million workers, are part of the informal labour 
market), extensive poverty (one fifth of inhabitants 
below the poverty line), widespread functional illiteracy 
(one sixth of inhabitants) and pronounced economic 
and social inequalities often leading to violence and 
social unrest (highly unequal income distribution, 
access to public education and, healthcare) (CIA, 2013).

from social security, labour regulations and collective 
bargaining. The other, more traditional, segment is 
labour-intensive, comprising mostly unskilled workers; 
it lacks capital and has a large number of non-formal 
workers. Entrepreneurs and government authorities 
tend to organise production for profit and invest in the 
formal segment of the labour market. The non-formal 
segment, on the other hand, is subsistence-oriented, 
with workers or producers unable to save or invest.

In the 1990s, after years of inward-looking 
development strategies, Brazil decided to undertake 
structural reforms and open up its economy. The new 
development strategy has led to important structural 
changes in production. In the last decade the Brazilian 
labour market witnessed an important drop in 
unemployment, concentrated in the metropolitan 
regions and equally distributed across all social groups, 
although no substantial labour market reform was 
made during this period (de Holanda, 2011) (63). 
Similarly, employment informality has been reduced to 
a historical minimum (below 40%), while schooling has 
increased (64).

The recent global financial and economic crisis affected 
the country only mildly. In 2011 the federal government 
announced the Brasil maior plan which included tax 
incentives for labour-intensive sectors that are sensitive 
to international competition, innovation and investment 
measures. This plan also embraces some foresight 
skills-oriented initiatives (65).

A1.2. 

 Main challenges and policy objectives

The nature of the dual economy and dual labour market 
determines the role of labour market stakeholders 
(employers, federal and state institutions, trade unions, 
sector associations). One segment of the economy is 
considered modern, capital and skill-intensive, with a 
relatively small number of mainly formal workers. 
Workers in the formal segment are expected to have 
regular, full-time jobs; they are registered, and benefit 

There is a wide range of challenges in the skills area in 
Brazil. The country’s labour market legislation is 
believed by industry to have an adverse effect on 
employment and productivity growth. There is a need 
for institutional adjustments that allow companies to 
compete in international markets under equal 
conditions, while also ensuring that workers have 
adequate protection (Corseuil and Ramos, 2007) (66).

(61) Brazil, Russia, India and China (BRIC).
(62) Brazil has a relatively developed economic structure that contrasts with the transitional structure of employment.
(63) Department of Statistics and Socioeconomic Studies (DIEESE). http://www.dieese.org.br
(64) IPEA Boletim de mercado de trabalho: conjuntura e análise (2000-12) [IPEA Bulletin of the labour market: situation and analysis] website:  

http://www.ipea.gov.br/portal/index.php?option=com_alphacontent&view=alphacontent&Itemid=144

(65) Brasil Maior website: http://www.brasilmaior.mdic.gov.br/
(66) The inconsistency between labour legislation related to the 1988 Constitution on the one side, and the opening up of the economy, monetary 

stabilisation, the rise in the tax burden and external shocks, on the other, result in a rise in informality and higher unemployment.

123

Developing skills foresights, scenarios and forecastsThe informal sector has its specific challenges in 
increasing the protection of workers who lack the skills 
to be engaged in the formal sector. Reducing illiteracy 
and raising the skill levels of future generations, not 
only by increased years of schooling but also by the 
quality of basic education, is seen as the way out of 
poverty and the informal sector for families (67). Despite 
considerable efforts and remarkable social progress, 
increased basic illiteracy and reduced basic education 
quality was reported during the last decade. Education 
policy measures focus on addressing both quality and 
quantity challenges in technical and vocational 
education and training (TVET) in general.

Given Brazil’s size, spatial mismatch is an important 
issue. Economic development is taking place far from 
traditional production regions. Multinational companies 
opt for transferring workers from other areas to secure 
skilled workers. This may further aggravate territorial 
disparities. However, it has been identified that newly 
created jobs in many states are not suitable for the most 
qualified workers, which underlines the skills matching 
issue (overqualified workers are reported to earn less 
than people matched to jobs) (ABDI and IPEA, 2012).

There is a need to improve the level of skills in industry. 
Participation in TVET and higher education is generally 
low in Brazil (11% of people aged 18 to 24); promotion 
of TVET can be particularly challenging in rural areas 
and in the informal economy. There is also a general 
lack of teaching staff for technically oriented subjects 
(physics, chemistry, mathematics). Teachers’ capacities 
need to be complemented by pedagogical 
competency, with a focus on the transmission of a 
range of high-level skills.

A shortage of talent and skilled workforce, especially 
those with technical qualifications, has been an issue in 
many industrial sectors. Activity in sectors such as oil 
and gas, civil engineering and the automotive industry 
are in urgent need of qualified labour. Companies have 
identified skill shortages as a key issue, and many have 
developed apprenticeship programmes and increased 
spending on training and wages. The increasing 

demand for new sources of energy and environmental 
concerns will stimulate energy efficiency and the use 
of renewable energies by industries, with consequent 
demand for qualified professionals. The same 
development is identified in the healthcare sector. 

New technology trends exert pressure on the 
qualifications of the labour force, generating demand 
for new skills. Whether these trends can be identified 
and met by the Brazilian labour force without further 
segmentation remains the key research and policy 
challenge. The main approaches in the skills area in 
Brazil intend to address the impact of technology or 
production organisation on the demand for labour and 
its consequences for work content. Derived from this, 
the key questions are what professions will be 
needed, including emerging occupations in other 
countries, and what are the implications for education 
and training.

A1.3. 

 Key institutions, processes, approaches, 
methodologies and results

Foresight in Brazil has evolved gradually. Programmes 
and projects incorporating concepts and techniques 
from a wide range of international exercises, mainly 
from Europe, have been launched and supported by 
the United Nations Industrial Development 
Organisation (UNIDO) or the European Commission. 
However, the country has also managed to develop its 
own way of doing foresight, often due to the creative 
use of limited resources, thus leading to effective 
innovations in practices and tools (Popper and Medina, 
2008). In recent years there has been increasing use of 
prospective methods and techniques by Brazilian 
organisations (from industry, academy or government), 
especially to help define research priorities and 
strategies, as well as to foster development to support 
the national innovation system. These activities aim to 
integrate economic success, environmental quality and 
social equity to achieve sustainable growth, 
competitiveness and job creation (Santos and Fellows 
Filho, 2006). 

(67) The successful anti-poverty scheme Bolsa familia ( which covers one fourth of the population) offers cash conditional on school attendance 

and vaccination of children in targeted families.

124

Guide to anticipating and matching skills and jobsThe first national foresight programme was launched in 
the late 1990s as Brazil 2020, a large-scale reflection 
on alternative paths Brazil could take in the 21st 
century. Four scenarios were produced, including one 
desired outcome for which a list of obstacles, and 
action to overcome them, in 17 thematic areas was 
identified. In the last decade dozens of foresight 
exercise have been conducted. The main bodies that 
initiated, sponsored or carried out these exercise 
include the Ministry of Development, Industry and 
Foreign Trade (MIDIC), the Centre for Strategic Studies 
and Management in Science, Technology and 
Innovation (CGEE), the Brazilian Innovation Agency 
(FINEP) and the Centre for Strategic Affairs of the 
Presidency (NAE). The areas covered include civil 
engineering, wood and furniture, plastics 
transformation, textiles and garments, energy, 
biotechnology, bio-fuel, agro-business, 
telecommunications, the petrochemical industry, water 
resources and climate change. Many of these studies 
have a human resources component; some of them 
even lead to occupational profiles (for example, 
researcher profile in 2022) (European Commission, 
2009) (68). Recent prospective studies under the 
national innovation initiative covering the strategic 
areas of nanotechnology, biotechnology and ICT within 
the 2008 to 2025 timeframe include methodological 
design leading to a proposal for six area actions 
including human resources (Ludovico de Almeida and 
Caldas de Moraes, 2010). The latest large-scale 
foresight activity, the Brazil 3 moments project, aims to 
define national objectives based on dialogue between 
different stakeholders in society over three horizons 
(2007/2015/2022). Preliminary results of the project 
have indicated that improving the quality of basic and 
primary education would have the highest positive 
impact on society. A specific exercise was launched on 
this topic (Popper and Medina, 2008). 

In 2012 a brand new initiative was launched by the 
Brazilian Agency for Industrial Development (ABDI) and 
the Institute of Applied Economic Research (IPEA). The 
Research network: education and labour market, is 
aimed at drawing together a profile of the most 
in-demand professions in the labour market in the 
coming years and suggesting how to ensure TVET 
provision in the future. Research institutions, 

government and the private sector will work together 
to investigate the need for skilled human resources in 
Brazil in the short, medium and long term (ABDI and 
IPEA, 2012). 

Institutions specifically focused on skills forecasting 
include labour market observatories, TVET institutions, 
and technical advice forums. The key objective is to 
identify industry needs, including by specific 
companies or groups. The approach is competency-
based with a sectoral focus (Vargas Zúñiga, 2012). A 
key feature of the sectoral skills development system 
in Brazil is the network of non-profit institutions 
providing National Training Services for the industrial 
(SENAI), commercial (SENAC), transportation (SENAT) 
and rural (SENAR) sectors. While these institutions are 
mainly financed through a compulsory payroll tax, they 
are managed by industry bodies, creating a strong link 
between TVET and labour market needs. These 
institutions are based on partnerships between 
government organisations and stakeholders, such as 
employer associations, rural labour unions, 
cooperatives and other associations in their respective 
sectors. Their organisation structure is decentralised 
across regional administrations and states. 

The best example of an integrated approach to skills 
anticipation in Brazil is a foresight model developed by 
SENAI (69). This model develops sectoral prospects at 
the national (federal) level. It aims to capture industrial, 
technological and organisational change and the 
impacts on employment in order to anticipate future 
training and qualification needs (quantitative and 
qualitative). The process is managed by an executive 
group formed by SENAI technical representatives, 
academia and business representatives, who are both 
the producers and users of the information generated.

A continuous approach to skills anticipation in SENAI 
was initiated in 2001 by focusing systematically on 
the two main questions: how many workers by 
occupation and industrial sector will be demanded in 
the near future, and what changes to the professional 
profile (knowledge, skills and abilities) will be 
required? These two questions have been addressed 
by a series of interrelated activities coherently 
integrated in a model. Table A1 shows the original 
model design.

(68) Overview of foresight exercises in Brazil.
(69) SENAI is a technical and vocation education training institution focused on providing professional training and technological services for 

Brazilian industrial companies. It has a total of 809 TVET schools and mobile training units located in all 27 Brazilian states. In 2012 it provided 
professional training for 3.1 million students and workers at various qualification levels.

125

Developing skills foresights, scenarios and forecastsTable A1.  Original SENAI foresight model

Projects

Technology 
observatory

Action

Objectives

Products

Technology foresight

Organisational 
foresight

Conduct foresight on emerging 
technologies related to specific 
industrial sectors for a period 
of 5 to 10 years

Conduct foresight on new forms 
of work organisation for industrial 
sectors for the period of 10 years

Series of sector studies. 
List of specific emerging 
technologies

List of new forms of work 
organisation

Occupation 
observatory

Analysis of emerging 
occupations

Identify roles and occupations that 
are emerging in other countries

Emerging occupations series

Analysis of occupation 
trends

Occupation issues

Education 
observatory

Compared vocational 
education

Consolidation of 
impacts

Thematic antenna

Identify growth rates of selected 
occupations in specific industries 
in Brazil

Conduct studies on occupation 
themes that impact on 
professional education and 
occupation information to generate 
content

Identify changes in vocational 
education supply in selected 
countries for specific industries

Analyse occupation and education 
impacts in technical and 
technological services

Series analysis of occupation 
trends

Series of occupation studies. 
Series of occupation monographs

Series systems of vocational 
education in comparison

Series em tempo (in time)

Use of results

System of occupation 
information (SINO)

Generate occupation information 
for HR managers

Industrial professions website

Generate occupation information 
for the young

Generate occupation information 
for the general public

Generate occupation information 
for technicians and teachers of 
SENAI

Generate occupation information 
for employed and unemployed 
workers

Almanac website

Occupation perspectives website

Occupation directory website

Occupation sentinel website

Source: Cruz Caruso and Bastos Tigre (2004).

126

Guide to anticipating and matching skills and jobsThe SENAI model has combined different 
methodological approaches under each activity 
(Box A1). These are foresight and forecasting 
approaches used to feed into occupation analysis 
and subsequently into thematic antenna, 
a workshop-like event, where the executive group 
discusses and analyses the outcomes, and 
generates recommendations for SENAI in relation to 
vocational training and provision of technical and 
technological services for the sector in focus. The 

recommendations generally relate to actions such 
as changes in curriculum design, provision of new 
courses, new technical and technological services 
and upskilling trainers. The results of the foresight 
exercise are released in various publications (70). 
Besides providing information for industry, students 
and researchers, the results feed into a continuous 
monitoring process of occupations to support SENAI 
in the development of skills strategies .

Box A1.  Original SENAI model: activities and methods

The SENAI model combines several activities with their own methodology design. The main task of the 
technology foresight exercise is to identify technologies with greater prospects of diffusion in the Brazilian 
economy over five to 10 years. Sectoral studies conducted by SENAI and its partners (brainstorming, workshops 
and literature review are the main instruments used) are key elements of the technology foresight exercise. 
They characterise growth and supply/demand structure patterns, industry structure, employment evolution, 
investment landscape and the structure of the sector’s value chain. These studies aim to elaborate a preliminary 
list of emerging technologies in the sector that may impact future occupation demands. The preliminary list is the 
basis for the Delphi rounds of expert interviewing, which is the main instrument in the foresight exercise. 
A minimum of 25 experts participate in two rounds of consultation. In the first, they have to evaluate each 
preliminary ‘emerging specific technology’ in terms of: its commercial viability in the country over the coming 
10 years; its likelihood of diffusion in Brazilian industry and the expected timing of adoption considering all 
industries that are potential adopters; the impacts the technology may have on workers’ skills needs (radical, 
incremental or low/null) and the respondents’ degree of familiarity/knowledge in respect to the technology. In the 
second round, respondents receive the same questionnaire already processed with results from the first round 
(mean, median and standard deviation) so that they can reformulate their first opinion if they wish. As a result, a 
definitive list of technologies is produced.
Skills are influenced by adoption of new technologies but they are also a result of new trends in organisational 
practices. The rationale is that changes in the architecture of firms and in the way work is organised impact 
occupation and skills needs. This foresight exercise tries to identify management and human resources, 
management models, decision making structures, hierarchies of firms and contractual or outsourcing practices 
adopted that will impact the nature of occupations over a period of five to 10 years. The methodology is structured 
in the same way as the technology foresight and comprises the following steps: 
(a)  elaboration, by external researches, of sectoral studies revealing organisational structures present in the 

industry in focus; 

(b)  selection, by the executive group, of actors to be consulted for the prospective exercise considering the 

sector’s value chain; 

(c)  preparation of the Delphi survey investigating the main organisational trends (questions explore changes in the 
content of work, such as the increase – or none – in the demand for more versatility from workers in a specific 
occupation, fragmentation or accumulation of tasks by various occupations; changes in the composition of 
firms’ workforces in relation to contractual arrangements, for instance an increasing – or decreasing – number 
of temporary workers and decision-making process and hierarchy); 

(70) SENAI ProspectaSE.

127

Developing skills foresights, scenarios and forecasts(d)  application of the Delphi questionnaire in two rounds to a group of external experts;
(e)  tabulation of the information; analysis and validation by the executive group; and 
(f)  elaboration of a list of organisational trends most likely to occur in the sector in focus over the coming 

10 years.

Analysis of emerging occupations aims at identifying and characterising new occupations and changes in the 
traditional occupation content. Research conducted in the US, Canada, Australia, New Zealand and in the United 
Kingdom on sector occupations is revised and occupation content is compared to the Brazilian classification of 
occupations system (CBO) (literature review and comparative analysis methods are used). These countries are 
selected because they systematically monitor occupation changes and because their occupational classification 
systems are compatible with CBO. Occupations are then classified as ‘emerging’ if they comprise a whole new 
range of tasks that do not find correspondence in the existing classification system; ‘in evolution or changing’ 
when they comprise an evolving set of activities and skills; and ‘stable’ if they do not reveal any substantial 
change. This classification follows the United States Bureau of Labour Statistics definitions. Emerging and 
changing occupations are further characterised in terms of work and skills content.
Analysis of occupation trends applies two models to estimate future quantitative changes in manpower demand 
by industry and by occupation in the country. The first is an input-output model which estimates the sectoral 
impact on employment changes in the final demand for goods and services produced by a particular industrial 
sector and its value chains. The second is analysis of the database of the Ministry of Labour and Employment, the 
relatório annual de informações sociais (RAIS) [annual report of social information]. This enables researchers to 
identify the most dynamic occupations in a particular sector in the recent past (the ones that generated more net 
employment). The RAIS database offers information by occupation breakdown in five digits.
A comparative study of TVET responses aims at identifying changes in the provision of sector-specific vocational 
training and education services in selected countries that might guide the development of national skills response 
strategies. This is done through literature review. In addition to characterising the type and reach of the training 
institutions, financial mechanisms, and planning and organisation structures, the analysis seeks to identify which 
are the teaching methodologies and resources in use, how the training of trainers is being conducted and which 
occupations in each sector are being more or less targeted for qualification or retraining activities. Analysis also 
focuses on understanding the context in which the new strategies and solutions arise, so they can be better 
adapted to the national circumstances. 
Results from the exercises are presented in the thematic antenna, a workshop-like event gathering together the 
executive group. During approximately half a day (four hours), outcomes are debated and analysed to generate 
recommendations for SENAI in relation to provision of TVET and technical and technological services for the 
sector in focus. The recommendations are broadly structured around the following topics: new courses provision; 
changes in curriculum design; provision of new technical and technological services; needs for updates and 
training for trainers; and needs for new studies.

Source: Cruz Caruso and Bastos Tigre (2004).

128

Guide to anticipating and matching skills and jobsThe first foresight study was prepared in 2004, based 
on SENAI’s own model to capture industry 
technological and organisational change, and its 
impacts on the future training and qualification needs 
(in quantitative and qualitative terms). A scenario 
methodology was incorporated to increase accuracy 
rates, making it possible to perform analyses in 
contexts with variables others than those studied by 
the model. Scenario planning was conducted by SENAI 

in 2010, to provide a sound analytical basis for strategic 
planning to 2027 (CNI and SENAI, 2010). Four scenarios 
have been developed through considering possible 
future outcomes in different contexts: macroeconomic 
and political-institutional; technological; TVET; and 
technical and technological services. Qualitative 
variables were identified through expert consultation 
by individual structured interviews and expert panel 
discussions.

Table A2.  2013-27 scenario

Brazilian context

Active entry to the world 
market through product 
diversification

Product specialisation 
with passive entry into 
the international market

World context

Shared leadership

Lack of a new world order

Scenario A

Scenario C

Scenario B

Scenario D

Source: Cruz Caruso (2013).

To determine the impacts of the technology change, 
the technology diffusion paths and occupational 
impacts were identified through expert interviews. 
Employment projections under each scenario were 
prepared using quantitative modelling. These 
projections were disaggregated to occupation level 
employment projections, which were mapped to find 
the demand for corresponding qualifications. The final 
results showed the demand for low, medium and high 
qualified workers under each scenario.

The integration of scenarios is an example of the 
evolution of the model. The original version of the 
SENAI model has been updated and some key 
activities have been reorganised (technology, 
organisation, occupation and education exercises) to 
fit user needs better, but it retains its basic feature of 
combining coherently various quantitative and 
qualitative approaches to the skills anticipation 
(Figure A1).

129

Developing skills foresights, scenarios and forecastsFigure A1.  Updated general outline of the SENAI model

Source: ILO/Cinterfor (2013).

130

Guide to anticipating and matching skills and jobsLabour market anticipation is also performed at state 
level by local observatories. In 2012, collaboration 
among observatories in the state of Paraná 
(observatory for prospecting and disseminating social 
initiatives; observatory for prospecting and 
disseminating technologies; observatory for industrial 
development) resulted in a study that analysed future 
skills demand through identifying promising future 
industry sectors and strategic routes for them, and 
developing professional profiles for this future (231 
professional profiles in 12 economic sectors). 
Identification of occupation profiles for the future was 
based on preparatory studies on profiles, the economy 
and industry of the state and also on international 
technology, social and industrial trends. The study 
included university-industry interaction and methods 
combined expert panels with quantitative data and 
analysis of current professional profiles (Parise, 2012). 

The occupation industrial observatory in Rio de Janeiro 
state is an activity of the FIRJAN system that includes 
the federation of industries of the state of Rio de 
Janeiro (FIRJAN), the industrial centre of Rio de Janeiro 
(CIRJ), the industry social services (SESI), SENAI and 
the national industrial training service and the Euvaldo 
Lodi institute (IEL). Its aim is to provide information on 
supply and demand of skilled labour in industrial 
sectors of the state by presenting the labour market 
situation, including its characteristics (distribution of 
formal employment across industries), trends and 
perspectives, and employer needs to public. The 
information is based on qualitative data from the 
systematisation of information and research from 
FIRJAN (such as its salary survey) and quantitative data 
from RAIS and CAGED (the general register of 
employed and unemployed). The main product is a 
website built according to the development map of the 
state: this was drafted in 2006 with the aim of 
promoting TVET and higher education and aligning it 
with the needs of the state’s economic centres. The 
website provides regional (within the state) and 
sectoral views on the labour market and its 
perspectives (71). 

(71) Observatorio Ocupacional – Mapa de Desenvolvimento do Estado do Rio de Janeiro http://www.firjan.org.br/site/observatorioocupacional/

index.html

131

Developing skills foresights, scenarios and forecastsFigure A2.  FIRJAN occupational observatory

Source: FIRJAN, 2012.

132

Guide to anticipating and matching skills and jobsinfluencing other context factors (for example, political 
and institutional stability). An unfavourable economic 
situation may disconnect new technologies from the 
main social problems and polarise the workforce. 
Recent social progress and increased social inclusion 
linked with good economic conditions (reduction of 
poverty, informality, inequality) directly affect the 
number of workers included in government-monitored 
activities, making foresight exercises more valuable. 

ILO/Cinterfor, (72) jointly with SENAI, already promotes 
the transfer of the SENAI foresight model to national 
training institutions in Central America and the 
Caribbean. Five countries are involved, represented by 
the National Institute for Occupational Training of Costa 
Rica [instituto nacional de aprendizaje] (INA), the 
Salvadorean Vocational Training Institute [instituto 
Salvadoreño de formación profesional] (INSAFORP), 
the Institute of Technical Training and Productivity of 
Guatemala [instituto técnico de capacitación y 
productividad] (INTECAP), the National Institute of 
Vocational Education and Training for Human 
Development of Panama [instituto nacional de 
formación profesional y capacitación para el desarrollo 
humano] (INADEH) and the National Institute of 
Vocational Technical Education of the Dominican 
Republic [instituto nacional de formación técnico 
profesional] (INFOTEP) and in May 2012 they began the 
implementation of the programme Skills anticipation: 
the transfer of the SENAI prospective model (ILO/
Cinterfor, 2013).

A1.4. 

 Lessons learned

The Brazilian experience shows an expanding foresight 
culture reflected in the rapidly growing number of 
foresight products. These products result from wide 
adaptation of international experience to the Brazilian 
reality. A variety of foresight approaches, ranging from 
qualitative methods, such as scenario development, 
Delphi style methods, focus groups and expert panels, 
to more data-demanding formal quantitative modelling, 
sectoral studies combining qualitative and quantitative 
methods and future-oriented employer surveys, have 
been developed or adapted and successfully used in 
practice. Foresight is becoming part of strategic 
thinking in Brazil.

The Brazilian case demonstrates good practice in 
coping with the resource and capacity deficiencies 
found in many developing countries in areas such as 
institutional framework and coordination, technology 
and innovation promotion capabilities, inflexible 
scientific institutions or financing. The fact that such a 
large and heterogeneous country was able to develop 
integrated skills anticipation models, not only at the 
regional (state) but also at the national (federal) level, is 
worth noting. 

The integration of sector foresight for technology, 
production organisation, and occupational and 
educational aspects under one institutional setting are 
further elements of good practice, helping to overcome 
some institutional framework deficiencies.

Networking in labour market research will help to 
accumulate knowledge of heterogeneous social and 
economic reality and develop appropriate 
methodologies for anticipation of future labour market 
trends. Networks support ambitious government 
projects such as building a national database of training 
courses to match supply and demand for TVET.

A general key factor in ensuring the success of 
foresight activities is the state of the Brazilian 
economy. Economic growth is a necessary but not 
sufficient condition for the success of foresight 
activities, directly through the financing of 
corresponding programmes and indirectly through 

(72) Cinterfor – Inter-American Centre for Knowledge Development in Vocational Training. More information is available in ‘Banco de Conocimien-

tos de Estudios Prospectivos’.

133

Developing skills foresights, scenarios and forecastsReferences

ABDI; IPEA (2012). Boletim eletrônico: formação e 
mercado de trabalho [Electronic newsletter: training 
and labour market]. First edition, December 2012. 
http://www.brasilmaior.mdic.gov.br/images/
data/201212/e4196555d9f62d9286cccfaf5fd5e133.pdf 

CNI; SENAI (2010). Technical and vocational educational 
training and technical and technological scenarios in 
Brazil 2010--24.

Corseuil, C.H.; Ramos, L. (2007). Brazilian labour 
market performance: 1995--2005. Paper presented at 
the Seminar on labour markets in Brazil, China and 
India, OECD, 28 March 2007. http://www.oecd.org/els/
employmentpoliciesanddata/38355614.pdf 

Cruz Caruso, L.A. (2013). SENAI’s forecast and 
foresight model. Paper presented at the Skolkovo-ILO 
International expert workshop ‘Using technology 
foresights for identifying future skills needs’, Skolkovo 
Moscow School of Management, 11 and 12 July 2013.

Cruz Caruso, L.A.; Bastos Tigre, P. (2004). Modelo 
SENAI de Prospecçã. Documento Metodológico 
[SENAI prospective model. Methodological document]. 
Montevideo: ILO/Cinterfor.

de Holanda, F. (2011). Labour market changes in Brazil. 
Presentation. http://www.oecd.org/eco/
labour/48185653.pdf 

Ernst, C. (2008). Recent dynamics in Brazil’s labour 
market. Geneva: ILO. Economic and labour market 
paper. 

European Commission (2009). Mapping foresight 
revealing how Europe and other world regions navigate 
into the future. Luxembourg: Publications Office. ftp://
ftp.cordis.europa.eu/pub/fp7/ssh/docs/efmn-mapping-
foresight.pdf 

FIRJAN (2012). Perspectivas Estruturais do Mercado de 
Trabalho na Industria Brasileira – 2020 [Industry’s 
structural perspectives in the Brazilian labour market]. 

Survey results published on Firjan’s website. 
Presentation. http://www.firjan.org.br/data/
pages/402880811F3D2512011F7FE00DA433D9.htm 

ILO/Cinterfor (2013). Skills anticipation: the transfer of 
the SENAI prospective model. Montevideo: ILO/
Cinterfor. http://www.oitcinterfor.org/sites/default/files/
file_publicacion/Prospectiva_ingles.pdf 

Instituto Nacional de Tecnologia (2003). Prospecção 
tecnológica: metodologias e experiências nacionais e 
internacionais [Technology foresight: national and 
international methodologies and experiences]. Rio de 
Janeiro: Instituto Nacional de Tecnologia. Technical 
note; No 14. http://www.davi.ws/prospeccao_
tecnologica.pdf 

Ludovico de Almeida, M.F.; Caldas de Moraes, C.A. 
(2010). Iniciativa Nacional de Inovação: modelo 
conceitual de prospecção tecnológica para áreas 
estratégicas no Brasil [National innovation initiative: 
conceptual model of technological forecasting to 
strategic areas in Brazil]. Parcerias Estratégicas, Vol. 15, 
No 30, pp. 297--310. 

Parise, D.J. (2012). Case: promising future sectors in 
the State of Paraná: Brazil. Presentation at the Skills 
academy, elective course on skills anticipation, Turin, 15 
to 26 October 2012.

Popper, R.; Medina, J. (2008). Foresight in Latin 
America. In: Georghiou, L. et al. (2008). The handbook 
of technology foresight: concepts and practice. 
Cheltenham: Edward Elgar Publishing.

Santos, D.M.; Fellows Filho, L. (2006). The role of 
foresight experience in the promotion of Brazil’s 
national innovation system. http://www.ires.ma/sites/
default/files/exercices_prospective/pdf_brazil_s_t.
pdf?access=1 

Vargas Zúñiga, F. (2012). Anticipating skills needs. Latin 
America and the Caribbean: overview. Paper presented 
at the Skills academy, elective course on skills 
anticipation, Turin, 15 to 26 October 2012.

134

Guide to anticipating and matching skills and jobsWebsites

[URLs accessed 7.5.2014]

Brasil Maior. 
http://www.brasilmaior.mdic.gov.br 

Centre for strategic studies and management in 
science, technology and innovation (CGEE). 
http://www.cgee.org.br/ 

CIA. The world factbook: Brazil. Washington D.C.: 
Central Intelligence Agency. 
https://www.cia.gov/library/publications/the-world-
factbook/geos/br.html 

Cinterfor. Modelo SENAI de Prospección. 
http://www.oitcinterfor.org/experiencia/modelo-senai-
prospecci%C3%B3n-brasil 

Department of Statistics and Socioeconomic Studies 
(DIEESE). 
http://www.dieese.org.br 

FGV/IBRE (Instituto Brasileiro de Economia da 
Fundação Getulio Vargas). 
http://portalibre.fgv.br/ 

IBGE (Instituto Brasileiro de Geografia e Estatística). 
http://www.ibge.gov.br/home/ 

IETS (Institute for Work and Society). 
http://www.iets.org.br 

IPEA. Boletim de mercado de trabalho –  
conjuntura e análise. 
http://www.ipea.gov.br/portal/index.php?option=com_
alphacontent&view=alphacontent&Itemid=144 

Observatorio do Mercado de Trabalho. 
http://portal.mte.gov.br/observatorio/notas-e-estudos.
htm 

Observatorio ocupacional. Mapa de desenvolvimento 
do estado do Rio de Janeiro.  
http://www.firjan.org.br/site/observatorioocupacional/
index.html 

SENAI ProspectaSE.  
http://www.senai.br/prospectase

135

Developing skills foresights, scenarios and forecastsACRONYMS

Brazilian Agency for Industrial Development

Brazil, Russia, India and China

classification of occupations system

Centre for Strategic Studies and Management in Science, Technology and Innovation

Central Intelligence Agency

Inter-American Centre for Knowledge Development in Vocational Training  
[Centro Interamericano para el Desarrollo del Conocimiento en la Formación 
Profesional]

Department of Statistics and Socioeconomic Studies

Federation of Industries of the State of Rio de Janeiro

information and communications technology

International Labour Organization

Institute of Applied Economic Research

annual social information report

National Service for Industrial Training 
[Serviço Nacional de Aprendizagem Industrial]

technical and vocational education and training

United Nations Industrial Development Organisation

ABDI

BRIC

CBO

CGEE

CIA

Cinterfor

DIEESE

FIRJAN

ICT

ILO

IPEA

RAIS

SENAI

TVET

UNIDO

136

Guide to anticipating and matching skills and jobsANNEX 2.  
Germany

A2.1. 

 Introduction

A major political and economic power of Europe, 
Germany’s economy in 2011, as measured by gross 
domestic product (GDP), gemerated USD 3 085 trillion. 
This makes it the sixth largest economy in the world 
(by GDP in purchasing power parity (PPP)). The service 
sector contributes around 70% of the total GDP, 
industry 29.1%, and agriculture 0.9%. Most of the 
country’s products are in engineering, especially in 
automobiles, machinery, metals and chemical goods. 
Of the world’s 500 largest stock market listed 
companies measured by revenue, the Fortune Global 
500, 37 are headquartered in Germany. In 2010 the 10 
largest companies were Volkswagen, Allianz, E.ON, 
Daimler, Siemens, Metro, Deutsche Telekom, Munich 
Re, BASF and BMW. The German economy has 
withstood the global economic uncertainty and the 
European debt crisis. The government has held firm to 
policies emphasising sound public finance, keeping 
public spending under control through deficit-cutting 
efforts. Earlier labour market reforms that raised 
working-hour flexibility and reduced structural 
unemployment have contributed significantly to 
sustaining the relatively robust job market during the 
economic slowdown, and that appears to be important 
for recovery. The official average national 
unemployment rate in May 2012 was 6.7%. Germany 
has 81.8 million inhabitants and a highly qualified labour 
force.

A2.2. 

 Main challenges and policy objectives

Germany is experienced in technology foresight (as 
defined), which has been developed for more than  
20 years (73). Initiatives using technology foresight are 
targeted to the adoption of concrete political actions.  
This approach is mainly oriented towards technology 
foresight without direct focus on skills needs 
identification, but it provides important contributions to 
the further development of the high-tech strategy and 

innovation policy of the Federal Ministry of Education 
and Research (BMBF). This ministry also employs 
innovation and technology analysis (ITA), which studies 
the functioning of, and changes in, research and 
innovation systems, supporting courses of action but 
also using results for education and ministry strategy. 
Most of these projects are based on objectives set out 
in the high-tech strategy. The high-tech strategy is the 
first national concept to involve key stakeholders in 
innovation around a common idea. It was launched in 
2006, and in 2010 the federal cabinet decided to 
continue its use. The new high-tech strategy 2020 will 
ensure the continuity of the overall approach and 
identify new priorities. Its aim is to make Germany a 
leader in providing scientific and technical solutions to 
challenges in the fields of climate/energy, health/
nutrition, mobility, security and communication. 
Innovative technologies and services create new jobs, 
and so every generation will have the chance to 
develop its potential. Within the high-tech strategy 
2020, and complementary to the activities carried out 
in ITA, the units of the BMBF are free to carry out their 
own strategic planning processes; a good example is 
the continuing (2013) process Biotechnology 2020+ (74). 

There are several different paths, which may be used 
as good practice and examples of experience for other 
countries:

(a)  BMBF foresight process;

(b)  technology foresight: ITA;

(c)  Frequenz project;

(d)  skill needs forecasting approaches (without 

emphasis on the development of technologies);

(e)  ad hoc studies (ad hoc study work in the future: 

structures and trends of work in industry and trade 
unions).

(73) A good overview – especially on the Delphi and FUTUR approaches carried out in the 1990s and from 2001 to 2005 respectively – is provided 

by a case study on Germany in the UNIDO technology foresight manual (UNIDO, 2005).

(74) See also http://www.biotechnologie2020plus.de/

137

Developing skills foresights, scenarios and forecastsThe foresight activities of German trade unions include 
some technology foresight activities. A good example 
is the exercise on future internet technologies and their 
consequences. A joint approach supported by VDI/VDE 
Innovation + Technik GmbH (VDI/VDE-IT), 
Industriegewerkschaft Metall (IGM), Vereinigte 
Dienstleistungsgewerkschaft (ver.di) and Deutscher 
Gewerkschaftsbund (DGB) elaborated integrated 
roadmaps for sectors and working contexts affected by 
future internet technologies to derive impacts on 
working conditions and skills needed (Botthof and 
Bovenschulte, 2009).

The BMWi carries out several prognostic activities/
studies including future economic impacts of new 
technologies. A good example is provided by the 
AUTONOMIK-guideline on human-machine-interaction, 
addressing future challenges and needs in the 
implementation of advanced cyber-physical systems 
and Industry 4.0 technologies into production 
processes (BMWi, 2013) (75).

Other ministries (such as environment or traffic) are 
involved in fields like (electro-) mobility or energy. 

Institutions involved in the BMBF ITA process

The argets of the BMBF foresight process are:

(a)  identifying new research and technology focuses;

(b)  identifying fields of societal changes and future 

demands;

(c)  identifying (and defining) areas of activity covering a 

range of research and innovation fields;

(d)  analysing potential fields of technology and 

innovation in which strategic partnerships might be 
possible;

(e)  identifying priority action areas for research and 

development.

A2.3. 

 Key institutions, processes, 
approaches, methodologies and 
results

The ministries mainly involved in technology research 
are:

The institutions, according to research field, include: 
work and technology; information and communication 
technologies; innovation research; life sciences; 
mobility and urban development; nanotechnology; 
technology and society; environment and energy; and 
science ethics. By legal status they are non-university 
research institutes, universities, consulting companies, 
public research institutions, companies, networks/
organisations/associations (76).

Main technology research conductors

(a)  VDI/VDE Innovation + Technik GmbH, programme 

managing agency for foresight and ITA on behalf of 
the BMBF;

(b)  VDI Technologiezentrum GmbH, future technologies 

consulting department (VDI TZ);

(c)  Karlsruhe institute of technology (KIT);

(d)  Institut für Technologie und Arbeit;

(e)  Fraunhofer institute for systems and innovation 

(a)  the Federal Ministry of Education and Research 

research (FhG ISI);

(BMBF);

(f)  Fraunhofer institute for industrial engineering (FhG 

(b)  the Federal Ministry of Economics and Technology 

IAO);

(BMWi).

(g)  The office of technology assessment at the German 

Bundestag (TAB).

(75) This guideline was developed in the context of the accompanying measures and investigations of the BMWi-programme AUTONOMIK. 
(76) Map of innovations – Technical analysis: http://www.bmbf.de/de/5331.php

138

Guide to anticipating and matching skills and jobsMain institutions involved in labour market 
research

(a)  Federal institute of vocational education and training 

(BiBB);

(b)  Institute for employment research (IAB) of the 
German federal employment agency (BA).

A2.3.1. 

 BMBF foresight process

The BMBF foresight is a strategic instrument of the 
BMBF that provides technology foresight with a time 
horizon of approximately 10 to 15 years. The BMBF 
foresight process provides information- and 
knowledge-based decision-making support for 
long-term and farsighted action in research and 
innovation policy. The process is carried out cyclically 
and in several phases: search and analysis, transfer, 
and preparation of the next cycle. BMBF foresight is 
characterised by combining the two approaches in a 
cyclic process model: a cycle strongly influenced by 
the technology-push approach follows a cycle that is 
mainly demand-pull oriented, and vice versa. In this 
way, the results of each preceding cycle can be 
evaluated and further developed in the subsequent 
cycle from an alternative perspective. After 
successful completion of cycle 1 (search phase: 
2007--09), the BMBF started cycle 2 with a new 
two-year search phase (since May 2012). The first 
phase was conducted by the FhG ISI and the FhG 
IAO on behalf of the BMBF. 

For the search phase of the first cycle (2007--09), the 
BMBF chose a technology-orientated approach 
(technology push) using a set of foresight methods: 
literature review; scenarios; expert workshops and 
interviews; and international two-step panel. Young 

researchers were specifically consulted as inventor 
scouts, online surveys and other techniques. Inventor 
scouting aims to find people with unconventional ideas 
and it studies bibliometric clusters of isolated topics. The 
process sets out to identify people with ‘unconventional’ 
ideas in the respective area, as well as young 
researchers and their topics, by means of the German 
research association’s Hochschullehrerverzeichnis (index 
of academic staff) (PhD students) and the young 
researchers’ competition.

Environmental scanning (literature research and 
analyses of conferences and relevant results), 
bibliometric analyses (for dynamic developments in 
scientific fields), and online surveys were conducted. 
Central cross-cutting research fields were identified at 
the interfaces between individual disciplines, thus 
leading beyond the previous functional and programme 
logic (but based on the topics of the high-tech strategy 
and other departmental foresight activities). 

The starting point for this foresight process was the 17 
thematic fields (five societal demands and 12 key 
technologies) of the German high-tech strategy and 
continuous foresight activities in the departments of 
the BMBF. By mid-2009, a set of advanced methods of 
future research had been developed to identify new 
research and technology focus in 14 established future 
fields. This resulted in the so-called future topics in the 
areas of health research, mobility, energy, environment 
and sustainable development, industrial production 
systems, information and communications technology, 
life sciences and biotechnology, nanotechnology, 
materials, substances and their manufacturing 
processes, neurosciences and learning research, 
optical technologies, services science, systems and 
complexity research, and water infrastructures. 

139

Developing skills foresights, scenarios and forecastsFigure A3.  Schedule of topic searches

Source: Cuhls et al. (2009).

140

Guide to anticipating and matching skills and jobsA future field is a cross-sectoral thematic field and is 
only defined as sustainable if it completely meets the 
BMBF’s criteria. In this context, there are established 
future fields, and so-called new future fields. Two 
criteria were decisive in creating a new future field:

(a)  research dynamics: the new field bundles a range 
of dynamic research aspects with a similar outlook 
in a way that promises to enable better exploitation 
of their innovation potential;

(b)  needs dynamic: the new field promises to address 
central future challenges in an innovative way.

In Cycle 1, the combination of methods applied 
included monitoring, structured research, data mining, 
bibliometric analysis, inventor scouting, expert 
discourse and an international monitoring panel.

Figure A4.  Combination of methods in the BMBF’s process

Source: Cuhls (2008).

141

Developing skills foresights, scenarios and forecastsCycle 2 (since 2011) is divided into three steps (which 
are conducted partly sequentially, partly in parallel):

(a)  inventory: until spring 2013 broad trends in demand 
(demand pull) will be determined and evaluated. 
Among other areas, results from research in the 
social sciences and humanities will be included 
with developments in society and the economy, 
along with results from interviews with lead users 
and persons who exhibit a particular openness to 
sociocultural change. The goal, above all, is to 
include fringe issues – hidden trend developments 
and neglected phenomena – in the analysis;

(b)  updating: until autumn 2013, the technology-

oriented results (technology push) of foresight 
Cycle 1 will be updated, and previously neglected 
topics will be added. The perspectives of further 
disciplines such as the cultural, social and political 
sciences will be recorded and evaluated with 
respect to their relevance to the research policy;

(c)  linking: until spring 2014 the demand- and 

technology-driven perspectives will be combined 
and linked to scenarios to point out the future areas 
within research and science which have the 
greatest solution potential.

A2.3.2. 

 Technology foresight process: ITA

The ITA is a concept of the BMBF combining 
innovation research, technology assessment 
approaches and a middle-term foresight analysis 
focused on possible policy options and courses of 
action. ITA studies refer to the time horizon of five to 
10 years (in comparison with the long-term technology 
foresight process). Analysis seeks to identify fields of 
socially accepted technological progress, illustrate 
potential, identify political scope, and develop options 
for research and innovation. 

The ITA is a strategic tool to identify social innovation 
needs and technological innovation potential with 
stakeholders from industry, the academic sphere, 
politics and society. There is a map of stakeholders 

available on the BMBF website (77). This approach is 
aimed at helping different actors in the innovation 
process with decisions regarding research, technology 
and innovation. Innovation and technology analyses can 
contribute to identification of possible chances and 
risks, the acknowledgment of potential and options, 
and the early recognition of possible restraints and 
supporting factors for innovations.

The project ITA monitoring was financed within this 
activity to identify the most important strategic themes 
for ITA. It is based on problem-oriented methodology, 
building on TAMI (78) and aimed at transferring research 
results into practice. The project contributes to the 
development of strategic issues for the innovation and 
technology analysis, and inter alia for a ‘topic field’ call 
for papers (for example, of the BMBF) as well as 
offering opportunities, conclusions and orientations for 
the funding initiatives of individual departments. Short 
studies represent preliminary work for concrete ITA 
projects but they are not ITA studies themselves. 

Short studies are prepared for the selected topics which 
comprise not only the transdisciplinary problem 
description but also recommendations for a problem-
adequate, methodical approach. In terms of methods, 
short studies are only preparatory works; they contain 
detailed problem analysis of potential chances and 
risks, potential and options. Concrete recommendations 
for methodological proposals for the identified need of 
an ITA are developed from this analysis. So this project 
contributes to the development of strategic topics for 
the innovation and technology analysis (including for the 
ITA topic cluster tenders of the BMBF division 113) and 
offers possibilities for providing conclusions and 
orientation for the research initiatives of the various 
divisions of the BMBF. Systematic and continuous 
monitoring will be established to identify relevant ITA 
topics, both from technology and demand perspectives. 
This examines the many different institutions, 
instruments and strategies in industry, science and the 
state sector which produce knowledge and 
technological innovations or play a role in this process. 

(77) http://www.bmbf.de/de/5331.php
(78) TAMI, Transparent accountable data mining initiative.

142

Guide to anticipating and matching skills and jobsThe BMBF itself is the user of the ITA project results. 
The entire process is coordinated in such a manner that 
possible topics are continuously prepared for innovation 
and technology analyses in three cycles. The selection 
of topics by relevance and priority was made in with the 
BMBF, taking expert opinions into account. 

Following new themes, four ITAs have been identified: 
electric vehicles, smart grids, ‘sticky information’ (data 
handling through IT) and non-medical applications of 
neuroscience.

The ITA studies are larger studies based on more 
methods, including in-depth technological analysis. 
Prepared as part of the ITA was an interdisciplinary 
study about nanotechnology, its medical implications 
and economic potential. Other studies deal with issues 
of biotechnology and information technology.

The pilot study on nanotechnology is based on, and 
operates with, six thematic dimensions and four time 
dimensions. This preliminary study is based on 
secondary literature, a primary survey on ITA-related 
aspects in nanotechnology, and guideline-based expert 
talks. The experts were selected from universities as 
well as department heads and laboratory heads of 
companies. The discussions with them lasted between 
1.5 and 3 hours.

(a)  Thematic dimensions:

  (i) 
technical dimension;
 (ii)  economic dimension;
 (iii)  ecological dimension;
 (iv)  health dimension;
 (v) 
 (vi)  political dimension.

individual and social dimensions;

(b)  Time dimensions:

  (i)  short-term (up to five years);
 (ii) 
long-term (up to 10 years);
 (iii)  visionary (up to 30 years);
 (iv)  science fiction (more than 30 years) or 

unforeseeable feasibility.

The study includes a survey of experts. The experts 
were also asked about qualifications and the creation 
of additional study programmes such as nanostructure 
technology (in universities) which most of them saw as 
meaningful. Aside from the pilot study, two studies on 
nanotechnology focused in depth on its medical 
implications and economic potential.

A2.3.3. 

 Frequenz project

The name is an abbreviation of the phrase meaning 
early identification of skill needs in the network 
(Früherkennung von Qualifikationserfordernissen im 
Netz). The project is directly aimed at linking the topic 
of professional qualifications and skills needs with 
anticipating technological development. The main 
phase of the project lasted from 2004 to 2011 and the 
network involved 12 institutions. Frequenz has 
continuing activities such as workshops.

A2.4. 

 Lessons learned

Germany is very advanced in innovation and 
technology. National initiatives aim at developing 
technologies in new economic and socially 
interesting areas; a positive aspect is the ability to 
engage a network of experts and institutions in 
implementation of the high-tech strategy, ITA 
process and BMBF foresight process. This also 
includes the search for new scientific talents (inventor 
scouting) and interviewing so-called ‘antennas’ 
(persons who exhibit a particular openness to 
sociocultural change, see 2.3.1. BMBF foresight 
process, cycle 2 inventory).

Methodologies developed in Germany offer positive 
models in their discussion on new foresight fields; the 
topic searches process during the BMBF foresight 
process has value in this respect and the topic-
specifying phase of ITA (ITA monitoring) illustrates a 
three-step process. Engagement of different interest 
groups into clusters is a good example for other 
countries in building up a foresight environment.

143

Developing skills foresights, scenarios and forecastsReferences

Astor, M.; Bovenschulte, M. (2001). Innovations- und 
Technikanalyse: Zukunftschancen erkennen und 
realisieren [Innovation and technology analysis: 
identifying and implementing opportunities for the 
future]. Bonn: BMBF.

Frequenz (2011). Newsletter 2011. Stuttgart: Fraunhofer 
IAO.

Kinkel, S. et al. (2007). Working in the future: structures 
and trends in industrial work. TAB report No 113. Berlin: 
Office of Technology Assessment at the German 
Bundestag.

BMBF (2004). Technologie und Qualifikation für neue 
Märkte. Ergänzender Bericht zur technologischen 
Leistungsfähigkeit Deutschlands 2003--04 [Technology 
and skills for new markets. Supplementary report on 
Germany’s technological capabilities]. Berlin: BMBF.

Luther, W.; Malanowski, N. (2004). Nanotechnologie als 
wirtschaftlicher Wachstumsmarkt: Innovations- und 
Technikanalyse [Nanotechnology as a growth market 
opportunity: innovation and technology analysis]. 
Düsseldorf: VDI-Technologiezentrum.

BMWi (ed.) (2013). Mensch-Technik-Interaktion: 
Leitfaden für Hersteller und Anwender [Human-
technology interaction: guidelines for manufacturers 
and users]. AUTONOMIK-Leitfaden Vol. 3. Berlin: 
BMWi.

Malanowski, N. (2001). Vorstudie für eine Innovations- 
und Technikanalyse (ITA) zur Nanotechnologie 
[Introductory analysis on innovation and technology for 
nanotechnology]. Düsseldorf: VDI-Technologiezentrum. 
On behalf of and with the support of the BMBF. 

Nusser, M.; Hüssing, B.; Wydra, S. (2007). 
Potenzialanalyse der industriellen, weißen 
Biotechnologie: Endbericht. Studie im Auftrag des 
BMBF im Rahmen der Innovations- und Technikanalyse 
[Analysis of potential for industrial white biotechnology: 
Final report. Study commissioned by the BMBF under 
the Innovation and Technology Analysis]. Karlsruhe: 
Fraunhofer ISI.

UNIDO (2005). Technology foresight in action. 
Technology foresight manual, Vol. 2. Vienna: United 
Nations Industrial Development Organisation.

Botthof, A.; Bovenschulte, M. (eds) (2009). Das Internet 
der Dinge: die Informatisierung des Alltags [The 
Internet of things: the computerisation of everyday 
life]. Arbeitspapier 176. Düsseldorf: Hans-Böckler-
Stiftung.

Cuhls, K. (2008). The BMBF foresight process. Paper 
presented at the 3rd international Seville seminar on 
future-oriented technology analysis: impacts and 
implications for policy and decision-making, Seville, 16 
and 17 October 2008.

Cuhls, K.; Ganz, W.; Warnke, P. (eds) (2009). Foresight 
process: new future fields. Karlsruhe: Fraunhofer ISI; 
Stuttgart: Fraunhofer IAO. On behalf of the BMBF.

Decker, M. et al. (2012). Zukünftige Themen der 
Innovations- und Technikanalyse: Methodik und 
ausgewählte Ergebnisse [Future issues of innovation 
and technology analysis: methodology and selected 
results]. Karlsruhe: Karlsruhe Institute of Technologie.

144

Guide to anticipating and matching skills and jobsWebsites

[URLs accessed 8.5.2014]

Biotechnology 2020+.  
http://www.biotechnologie2020plus.de/ 

BMBF map of innovations: a technical analysis 
(Germany).   
http://www.bmbf.de/de/5331.php 

BMBF strategy processes.  
http://www.bmbf.de/en/6490.php 

Hans-Böckler-Stiftung.  
http://www.boeckler.de 

Innovations and Technology Analysis.  
http://www.innovationsanalysen.de/ 

145

Developing skills foresights, scenarios and forecastsACRONYMS

BMBF

BMWi

FhG IAO

FhG ISI

GDP

ITA

Federal Ministry of Education and Research

Federal Ministry of Economics and Technology

Fraunhofer Institute for Industrial Engineering

Fraunhofer Institute for Systems and Innovation Research

gross domestic product

innovations and technology analysis

146

Guide to anticipating and matching skills and jobsANNEX 3.  
Finland

A3.1. 

 Introduction and institutional context

The Republic of Finland is a Nordic country located in 
northern Europe. In terms of area, it is the eighth 
largest country in Europe and the most sparsely 
populated country in the European Union. Around  
5.4 million people reside in Finland, with the majority 
concentrated in the southern part of country. The 
labour force is about 2.7 million people.

Finland was a relative latecomer to industrialisation, 
remaining a largely agrarian country until the 1950s. 
Thereafter, economic development was rapid, such that 
today, Finland has a highly industrialised mixed 
economy with a per capita output equal to that of other 
European economies such as Belgium, France, 
Germany and the UK. The largest sector of the 
economy by employment is services at about 73% of 
the labour force, followed by industry and construction 
at approximately 23%. Agriculture and forestry employ 
about 4% of the working population. The key economic 
sector with respect to foreign trade is manufacturing. 
The largest industries are electronics, machinery, 
vehicles and other engineered metal products, forestry 
and chemicals.

In the 21st century, the key features of Finland’s 
modern welfare state are a high standard of education, 
equality promotion, and a national social security 
system, the last of these is currently challenged by an 
aging population and the fluctuations of an export-
driven economy.

Finnish society strongly favours education and the 
population is highly educated by international 
standards. Education is appreciated and there is a 
broad political consensus on education policy. 
Education is compulsory between the ages of 7 and 
16. Basic education encompasses nine years. Schools 

do not select their students but every student can go 
to the school of his or her own school district. Public 
authorities must secure equal opportunities for every 
resident in Finland to access education after 
compulsory schooling and to develop their potential, 
irrespective of their financial standing. Legislation 
provides for compulsory education and the right to free 
pre-primary and basic education. Most other qualifying 
education is also free of charge for students, including 
postgraduate education at universities.

General upper secondary education continues the 
teaching and education tasks of basic education to 
students aged about 16 to 19. The general upper 
secondary school ends in the matriculation 
examination which determines eligibility for all higher 
education studies. Students in vocational upper 
secondary education and training are mainly aged 16 to 
25 years. The school-based education system means 
full-time studies for three years at a vocational 
institution. A total of 73% of 25 to 64 year-olds have, at 
minimum, gained a certificate at upper secondary level 
and 33% (the highest in the European Union) have had 
a university or corresponding education. The aim is that 
92.5% of the age group 25 to 34 will, by 2015, pass an 
examination at upper secondary or tertiary level.

The system of vocational education and training 
consists of the national core curricula, each education 
provider’s locally approved curricula and the students’ 
personal study plans. The national core curricula are 
drawn up by the Finnish National Board of Education in 
cooperation with employers’ organisations, trade 
unions, the trade education union, and student unions. 
They are overseen by training committees, which are 
tripartite bodies established for each occupation field 
by the Ministry of Education and Culture.

147

Developing skills foresights, scenarios and forecastsInteraction and partnerships are built at all levels of 
activity. There is cooperation on the development of 
education between various levels of administration, 
between schools, and between other social actors and 
schools. Education authorities cooperate with teachers’ 
organisations, pedagogical subject associations and 
school leadership organisations. This provides strong 
support for development.

A3.2. 

 Main challenges and policy objectives 

The Finnish economy has been acknowledged for its 
exceptionally high investment in research and 
development, high-quality education, and social 
consensus. Recent decades have, however, brought 
several challenges that Finland has to tackle: recovering 
from the 2009 recession and unfavourable 
demographic development connected with expected 
major labour supply reduction are considered as the 
most important. The need to deal with the expected 
consequences of the reduced workforce has 
highlighted efforts to make the education system work 
more efficiently to ensure that the competencies of the 
workforce and the needs of employers will match. As a 
consequence, Finnish foresight activities aim to 
anticipate the demand for labour, changes in 
qualifications and competencies of the labour force, 
and education needs. 

The main policy goals are to improve the match 
between the competencies of the labour force entering 
the market and the skills needed by employers, 
improve the availability of a competent workforce, 
make the education system more efficient, and target 
education characteristics such as flexibility, transition 
between different levels of education and acceleration 
of completion times.

A3.3. 

 Key institutions, processes, 
approaches, methodologies and 
results

Foresight activities at both national and regional level in 
Finland are fragmented among many actors in both 
private and public sectors. There are many forms of 
foresight research: government level foresight; 
parliament level foresight; economy, industrial and skill 
needs forecasting carried out by public research 
institutes and funds; technology foresights; foresights 
at regional level; and foresight activities by private 
companies and corporations.

The focus of the anticipating activities at national level 
is set by the government foresight report produced 
once per government term (79). The report is created in 
cooperation with government sector authorities and 
research community experts. The futures committee in 
the Finnish parliament deliberates the government 
report, proposes amendments and prepares reports 
concerning future policy-making challenges for the 
Finnish parliament. Along with foresighting projects 
carried out by single ministries, there is the national 
foresight network of ministries acting as a central 
government body that serves as project coordinator 
and as a platform for sharing knowledge among 
ministries. 

Among the individual ministries’ foresight projects are 
the activity of the Ministry of Education and Culture 
(MEC), mainly the department for education and 
science policy and its subordinate agency, the Finnish 
National Board of education (FNBE), and the Ministry 
of Labour. However, the cooperation of many actors is 
common in foresighting processes. Universities, social 
partners, think tanks and public sector agencies are 

(79) A new government foresight report is under preparation. The final futures analysis report, forming part of the preparations for the 

government foresight report, was presented on 14 February 2013. The report highlights ways in which Finland can succeed in 2030. During 
spring 2013, the government will prepare a foresight report for parliament. In April 2012, the ministerial working group for the government 
foresight report discussed the selection of foresight themes and approved the following: the opportunities in the midst of scarcity; a new 
geography for the north; business regeneration; public administration as an enabler; working life in the future; and citizens’ well-being and 
inclusion. The group also discussed four cross-cutting themes: flexibility and crisis resilience; competencies and capabilities; ICT as an 
enabler; and the global perspective. The futures analysis phase was implemented in collaboration between the Prime Minister’s Office, the 
Finnish funding agency for technology and innovation and the academy of Finland and Tekes, with many independent specialists and experts 
from research institutions, enterprises and non-governmental organisations taking part in the work. Discussions were held at www.2030.fi 
and at seven locations in Finland in the autumn of 2012.

148

Guide to anticipating and matching skills and jobsBox A2.  Adequate institutional framework in place

• 

• 

• 

The Finnish foresight system has the following six parts (http://www.2030.fi/en/what-is-a-foresight-report):
• 

the government foresight report. This is a major report that discusses the development aspects of the country 
in one large thematic area, usually 20 to 30 years ahead, and defines the government’s vision and guidelines. 
The Prime Minister’s office’s policy analysis unit is responsible for its preparation. Appropriate background 
reports are commissioned, and they are elaborated by the state’s sector research institutes, such as the 
government institute for economic research (VATT), the national institute for health and welfare (THL), the 
technical research centre of Finland (VTT) and Statistics Finland. The next government foresight report is 
always on a new theme. This long-term visionary document is sent to the Parliament for comments. Next the 
Parliament futures committee with the Prime Minister’s office begin discussions with citizens around the 
country; they introduce the report results and gather citizens’ opinions. Implementation of government 
foresight normally takes a much longer time than one government term, which means that it partly binds the 
work of the next government;
the government foresight network of people who do, order, or coordinate anticipation work in different 
ministries. The network has two members from each of Finland’s 12 ministries. It shares produced foresight 
knowledge and discusses substance, issues and foresight methods;
the Finnish parliament’s committee for the future. This was established as a temporary committee in 1993 and 
became permanent in 2000. It collects the parliament’s answer to the government foresight report, performs 
future-oriented duties, conducts research associated with futures studies, and makes assessments of 
technological developments and the effects of technology on society;
the foresight consortium for labour force, competency, and educational needs. This was established in 2008 
based on a decision of the minister’s group of Finnish work, entrepreneurship and labour markets. The idea 
was to establish a mutual foresight system for coordinating all of the Finnish government actors’ decision-
making regarding vocational education and labour markets competency needs issues. The steering of the 
system’s work is divided between the Ministry of Employment and the Economy (MEE) and the Ministry of 
Education and Culture (ME). The domain of the MEE contains both the short-term foresight of competency 
and education needs, and the labour force needs foresight, in general. The ME sets out the foresight of 
competency and education needs in the medium and long term. The foresight system orders its basic 
forecasts and input data of the labour markets and economy from the VATT. These data are further broken 
down for use at regional level in different administrative fields, and are also used in the work of the 
Government foresight network and in the government’s future report writing. VATT forecasts are then 
complemented with other organisations’ development forecasts, with corporate organisations participating in 
the foresight especially in fields facing remarkable structural changes. The biggest challenge here is to merge 
the quantitative forecasts and qualitative foresight into one storyline;

149

Developing skills foresights, scenarios and forecasts•  SITRA’s foresight network (Finnish innovation fund). This was set up in conjunction with the Bank of Finland in 
1967. Today it is an independent public fund that, under the supervision of the Finnish parliament, promotes 
the welfare of Finnish society. It concentrates its activities in programmes oriented to Finland’s future. It 
identifies and closely examines change drivers that are relevant to Finland. The national foresight network is a 
part of the foresight work in SITRA (http://www.foresight.fi);
the Finnish futures community. This covers the various futurists’ or future-oriented people’s networks in 
Finland. The biggest is the Finnish society for futures studies, established in 1980.

• 

There are three main strands in the Finnish strategic foresight system: functions related to the parliament, to the 
government and to the futures or futurists community outside the government.

(c)  technology and innovations.

The main processes, instruments, methods and results 
are developed below.

A3.3.1. 
A.3.3.1.1. 

 Education and labour market

 The Mitenna model

The Mitenna is a quantitative model developed for the 
anticipation of education needs; its outputs are used as 
a basis for formulating Finnish education policy. It is a 
model for forecasting the requirements for student 
places in comprehensive and vocational schools and 
universities. A broad spectrum of data sources and 
approaches is taken into consideration in formulating 
education needs forecasts. The model is geared 
towards anticipating long-term education needs (about 
15 years) based on the requirements of the labour 
market, which have been used specifically in preparing 
the Ministry of Education and Culture’s development 
plans for education and research, drawn up for every 
government term. The outputs of the model are used 
both directly in planning intakes into education and 
training, and indirectly in providing advice and guidance 

Source: Kuosa (2011).

important players in this area. The VATT prepared 
scenarios in 2010--11 for employment trends in 
different industries to 2025. The FNBE produces data 
on demand for labour by occupation groups and 
education needs. Responsibility for qualitative 
anticipation of education content rests with the FNBE, 
higher education institutions and education providers. 
The system of national education and training 
committees functions as the expert organisation in 
qualitative foresight of education, consisting of a 
steering group, the national education and training 
committees, and fixed-term expert groups. The system 
of national education and training committees is an 
expert organisation in qualitative foresight of education. 
The government foresight network is an inter-
ministerial forum for cooperation and exchange of 
information on issues relating to the anticipation of the 
future.

In general, the recent foresight studies carried out in 
Finland concerned:

(a)  education and labour market;

(b)  challenges for the economy;

150

Guide to anticipating and matching skills and jobsto individuals (especially young people) who are 
choosing their education paths.

and infrastructure, and service and knowledge-
intensive products (SKIP).

A.3.3.1.2. 

 Oivallus

Oivallus is coordinated by the Confederation of Finnish 
Industries (EK) and funded by the FNBE, the European 
Social Fund (ESF) (2008--11) (80) and the confederation 
of Finnish industries. Its purpose is to identify 
potentially successful fields and obtain information 
from a multitude of perspectives. Oivallus attempts to 
characterise working life in the 2020s and consider 
what kind of experts and skills will then be needed.

A.3.3.1.3. 

 The national qualitative anticipation 
project

The aim of the project is to develop and establish a 
permanent operating model to anticipate skills needs in 
different sectors of the world of work. It was launched 
in 2008. The anticipation results will be used in the 
development of curricula, qualifications and 
qualifications frameworks. The model of anticipation 
developed within the project will serve different 
education structures and meet the anticipation needs 
of both national language groups (Finnish and 
Swedish).

A.3.3.1.4. 

 The education intelligence foresight 
system

The education intelligence foresight system, developed 
by the confederation of Finnish industries, is a series of 
studies that seeks to anticipate changes in the 
industrial environment. Its outputs consist of 
cluster-related predictions. A cluster is understood as a 
network of suppliers, producers, customers and 
competitors related by cooperation and competition. 
The clusters analysed were ICT, well-being, chemistry 
and biotechnology, forestry, construction, real estate 

A3.3.2. 
A.3.3.2.1. 

 Economic foresights

 Economic five-year regional forecasting 
system by research institute of the 
Finnish economy (ETLA)

Forecasting by ETLA consists of five-year predictions of 
the economic development of Finnish regions and 
industries. Forecasting outputs are summed up twice a 
year in the publication Suhdanne and include 
predictions of production, employment and other 
crucial economic indicators. Forecasts of development 
in Finland are linked with trends in the global economy 
and the situation in the European Union. 

A.3.3.2.2. 

 VATTAGE

VATTAGE is a model of the VATT based on an applied 
general equilibrium method. It is a quantitative 
approach to long-term forecasting of the economy and 
labour market.

A3.3.3. 
A.3.3.3.1. 

 Technology and innovations

 Finnsight 2015

Finnsight 2015 is a joint project of the academy of 
Finland and the Finnish funding agency for technology 
and innovation (Tekes) carried out in 2005--06 that 
explored drivers of change as they affected Finnish 
business and industry and society at large, and which 
identified emerging challenges for innovation and 
research. It also analysed focus areas of competency in 
science, technology, business and industry as well as 
society at large, that needed to be strengthened with a 
view to enhancing well-being in society and boosting 
the competitiveness of Finnish business and industry. 
Findings from Finnsight 2015 are being incorporated 
into national science and technology policies.

(80) Oivallus final report. http://ek.multiedition.fi/oivallus/fi/liitetiedostot/arkisto/Oivallus-Final-Report.pdf 

151

Developing skills foresights, scenarios and forecastsA.3.3.3.2. 

 Tekes foresight systems

Tekes is the main public funding agency for applied and 
industrial research and development (R&D) in Finland. 
Tekes’s mission is to fund private and public 
research and development projects, promote 
development in specific sectors of technology and 
industry and coordinate key players in Finnish R&D. 
Tekes also carries out technology foresight studies to 
initiate new programmes and develop a common 
vision and industrial strategy. Technology foresight 
studies typically include interviews, workshops and 
broad discussions, as well as having internet-aided 
sections.

A.3.3.3.3. 

 Finnish innovation fund (SITRA)  
foresight systems

SITRA is an independent public fund promoting the 
welfare of Finnish society. It produces a multistrand 
foresight report: welfare and everyday living; working 
life; public sector; multiculturalism; and environmental 
technology. The foresighting programmes are done 
using a wide range of methods including studies, 
strategy processes and innovative experiments.

There are also projects carried out regularly at 
governmental level (the government foresight report, 
the ministries’ future reviews) and reports from the 
Finnish Parliament’s committee for the future that are 
not focused on a single topic but deal with various 
future-related issues.

A3.4. 

 Lessons learned

Finnish forecasting activities seem complex and 
sophisticated. There are many public and private 
institutions involved in programmes that anticipate 
development in various spheres of society. Not to be 
surprised by future development is a subject of great 

importance in Finnish policy-making. Foresight research 
is fully embedded in the operation of the Finnish 
government and legislative bodies, making the level of 
institutionalisation unique. 

The most common foresight methods are expert 
panels, literature review, scenarios, futures workshops, 
SWOT and Delphi.

What can be considered as remarkable is the way the 
outputs of foresights are directly transferred into 
policy-making. The connection between the outcomes of 
the anticipation of demand for labour and education 
needs made by the Finnish National Board of Education 
and the major educational policy document, the 
development plan for education and research, is a clear 
example.

Article No 103 of the development plan for education 
and research 2011--16 states that:

‘The supply of higher education institutions in 2016 will 
be quantified based on national foresights: 

(a)  the overall volume of university education will be 

kept unchanged. In terms of field of education, the 
need for increases exists especially in teacher 
education. The field-specific increases will be made 
through the retargeting of existing provision;

(b)  the intakes in polytechnics will be reduced by 2 200 
entrants in 2013. The cuts will be primarily made in 
the fields of culture, tourism, catering and domestic 
services, and technology, communication and 
transport. A small increase is needed in health care 
and social services.’

Part of the development plan for education and 
research sets specific targets for intakes of students 
and expected output of graduates in individual 
disciplines and degrees. 

152

Guide to anticipating and matching skills and jobsLong-term anticipation data are used in preparing target 
and performance agreements between the Ministry of 
Education and the universities and polytechnics. Regular 
negotiations between the Ministry of Education and 
higher education providers are the main element of the 
steering process of higher education. The result of these 
negotiations is an agreement on the objectives, profiles 

and priorities, as well as quantitative targets, for single 
universities and polytechnics. 

The intention of all stakeholders is to increase the 
impact of anticipation on education and training. 
Cooperation between ministries and different levels of 
administration is being promoted. 

Figure A5.  Target scenarios

Notes:
1. Education and Research 2007–2012. Development Plan. Ministry of Education, 5th Dec. 2007.
2. Anticipation results on provision targets for 2012. Target scenario. Finnish National Board of Education.
3. New students in 2006 (Statistics Finland, AMKOTA database).

Source: Hanhijoki et al. (2009).

153

Developing skills foresights, scenarios and forecastsReferences

Websites

[URLs accessed 13.5.2014]

[URLs accessed 13.5.2014]

Aggregates of foresight insights and relevant 
information from Finland and abroad.  
http://www.foresight.fi 

CIA. The world factbook: Finland. Washington D.C: 
Central Intelligence Agency.  
https://www.cia.gov/library/publications/the-world-
factbook/geos/fi.html 

Finnish government: government foresight network.  
http://valtioneuvosto.fi/tietoarkisto/ennakointiverkosto/
en.jsp 

Finnish National Board of Education.  
http://www.oph.fi/english 

Ministry of Education and Culture, Finland.  
http://www.minedu.fi/OPM/?lang=en

What is a foresight report.  
http://www.2030.fi/en/what-is-a-foresight-report 

Ahola, E. Technology foresight within the Finnish 
innovation system. Helsinki: Tekes, National  
Technology Agency. http://www.nistep.go.jp/IC/
ic030227/pdf/p3-4.pdf 

Arnkil, R. 2010. Anticipating and managing 
restructuring: 27 national seminars, ARENAS. The case 
of Finland. National background paper presented at the 
Finnish seminar, The National Institute of Health and 
Welfare, Helsinki, 20 to 21 May 2010.

Confederation of Finnish industries EK (2011). 
OIVALLUS Final report. Helsinki: Confederation of 
Finnish industries.

Hanhijoki, I. et al. (2009). Education, training and 
demand for labour in Finland by 2020. Helsinki: Finnish 
National Board of Education. 

Hanhijoki, I. et al. (2012). Education, training and 
demand for labour in Finland by 2025. Helsinki:  
Finnish National Board of Education. http://www.oph.fi/
download/144754_Education_training_and_demand_
for_labour_in_Finland_by_2025_2.pdf 

Kaivooja, J.; Marttinen, J. (2008). Foresight systems 
and core activities at national and regional levels in 
Finland 1990--2008: developing foresight systems for a 
better life in Finland and Europe. Turku: Finland Futures 
Research Centre, Turku School of Economics.

Kuosa, T. (2011). Practising strategic foresight in 
government: the cases of Finland, Singapore and the 
European Union. RSIS Monograph No 19. Singapore: 
Rajaratnam School of International Studies.  
http://www.rsis.edu.sg/publications/monographs/
Monograph19.pdf 

MEC (2012). Education and research 2011--16: the 
development plan. Helsinki: Ministry of Education and 
Culture.

154

Guide to anticipating and matching skills and jobsACRONYMS

ETLA

FNBE

MEC

MEE

R&D

SITRA

Tekes

VATT

Research Institute of the Finnish Economy

Finnish National Board of Education

Ministry of Education and Culture

Ministry of Employment and the Economy

research and development

Finnish Innovation Fund

Finnish Funding Agency for Innovation

Government Institute for Economic Research

155

Developing skills foresights, scenarios and forecastsANNEX 4. 
Japan

A4.1. 

 Economic and social context

Japan has built its economic success predominantly on 
a skilled population, government-industry cooperation 
and intensive science and technology development, 
mainly within technology-based industries. It has a 
culture of workers staying for a long time, if not for life, 
with one employer, which can result in a relatively less 
dynamic labour market and education system(81). 
Though this can mean fewer opportunities to influence 
the labour force directly from a central level, individual 
companies are motivated to invest in the development 
of their workforces and draw benefits from various 
foresight exercises.

A4.2. 

 Main challenges and policy objectives

The main objectives of the nationwide science and 
technology foresight exercises include:

(a)  outlining the future prospect for evolution in major 

areas of science and technology;

(b)  determining what should be done from now to 

attain future goals and overcome major global and 
national challenges;

(c)  contributing to the development of science and 

technology policies (for example, the basic plan for 
science and technology) and to setting the 
corresponding priorities for investment and 
resource allocation;

(d)  providing data to a broad variety of stakeholders 

and decision-makers.

A4.3. 

 Key institutions, processes, 
approaches, methodologies  
and results

The major technology and science foresight surveys 
are carried out regularly every five years and cover the 
following 20 to 30 years. Initially they consisted solely 
of extensive Delphi surveys, determining science and 
technology supply, but in the last two surveys (eighth 
science and technology (S&T) foresight in 2003--04 and 
ninth S&T foresight in 2008--10) the approach was 
broadened to cover demand for new technologies and 
multiple methodologies (for example, scenario 
development or regional workshops) were used.  
The results have been used by stakeholders from both 

public and private sectors and, since the eighth S&T 
foresight, also provided direct input into science and 
technology policy formulation (the science and 
technology basic plan). 

As well as extensive long-term surveys, a variety of 
other foresight activities are undertaken. These range 
from sectoral foresights and mezzo-level exercises for 
specific industrial associations, to micro-level foresight 
within individual companies, using a wide range of 
methods.

The following description concerns the major 
nation-wide exercise of recent years, the ninth S&T 
foresight carried out in 2009. The main institution 
involved in carrying out the surveys was the National 
Institute of Science and Technology Policy (NISTEP). 
Associated partners included the Ministry of Education, 
Culture, Sports, Science and Technology (MEXT) and 
the Council for Science and Technology Policy (CSTP).

The following processes and methodologies were 
employed.

A4.3.1. 

 Setting of ‘grand challenges’

A preliminary discussion considered current global and 
national trends and identified the main scientific and 
technological challenges/goals for future development. 
The following four directions (grand challenges) were 
determined:

(a)  central players in the scientific and technological 

arena;

(b)  sustainable growth through green innovation;

(c)  a successful model for a society ageing healthily;

(d)  secure life.

A4.3.2. 

 Delphi survey

Four preliminary panels were organised, defined by 
broad themes: security, safety, international 
collaboration and international competitiveness. 
Experts (from humanities and social sciences as well 
as natural sciences) discussed future targets that can 
be attained with the contribution of science and 
technology, and the main global and national 
challenges. Through these extensive discussions,  
24 ‘critical issues’ were identified.

(81) http://www.meti.go.jp/english/report/downloadfiles/2-3e.pdf 

156

Guide to anticipating and matching skills and jobsA total of 12 interdisciplinary panels were established 
(in contrast to the previous surveys where panels were 
field-based) to determine relevant future issues 
irrespective of the existing scientific disciplines. The 
panels consisted of 135 experts (in total) in the 
humanities, social sciences and natural sciences (from 
universities, industrial sectors and research 
organisations). The main topics and areas (the area 
represented a group of interrelated topics) for the 
survey emerged from their discussions.

Two rounds of an extensive questionnaire survey were 
conducted among experts. They gave responses on the 
perspectives of the topics (832 in total) over 30 years 
until 2040. In the end, 2 900 responses were gathered. 
Analysis of the final results of the questionnaire survey 
was then carried out.

A4.3.3. 

 Scenario writing

In the framework of extensive interdisciplinary 
discussions by expert groups scenarios, were created 
to outline future changes that may be brought about by 
the development of science and technology, including a 
path for reaching goals. A description of related 
elements was also requested, including human 
resource development.

Scenarios were also developed from a Delphi survey, 
reflecting the general consensus of the experts. Future 
scenes of daily life in 2025 were described, integrating 
forecast Delphi topics.

Group discussions were held among representatives of 
the younger generation to compensate for the possible 
age bias, as the experienced experts who took part in 
scenario groups or Delphi questionnaires came 
disproportionately from the middle-aged and older 
generations.

A4.3.4. 

 Regional workshops

Workshops were held in eight Japanese regions. 
Participants discussed ideal future regional lives and 
what kind of science and technology can be expected 
to contribute to them. 

A vision of attainable an future society was outlined. 
Those of the Delphi topics likely to be widely available 
and/or widely known in society by 2025 were selected 
to outline the shapes of future society. The science and 
technology areas of key importance for attaining the 
goals and resolving the challenges were identified. 
Management of technology-related issues, such as 
knowledge base, business, risk, globalisation and 
human resources, was seen as inevitable.

A4.4. 

 Lessons learned

Often it is stressed that in Japanese foresight culture 
the main value of the foresight is seen not only in the 
direct outputs like subsequent policies, but in the 
benefits arising from the process itself, expressed as 
the ‘five Cs’: communication, concentration on the 
longer term, coordination, consensus and commitment 
(UNIDO, 2005a). From this it can be suggested that 
education policies have been indirectly formed by such 
exercises, even if no direct regular linkage has been 
established so far, especially since the eighth S&T 
foresight when the MEXT and the government CSTP 
were largely involved. Ministerial proposals regarding 
education or training are generally made by reference 
to surveys and estimations based on predicted change 
in industry structure; some may also result in policy 
formulation. For example, the eighth Delphi study 
contributed to the strategy Innovation 25 which 
outlines major Japanese prospects and challenges 
regarding innovations until 2025. It also contains a 
chapter on human resources where basic directions 
(for example, fostering the creativity of young people 
by gaining experience overseas) are set.

The mission-oriented and interdisciplinary approach of 
the ninth S&T foresight proved effective in determining 
the direction of innovation and its promotion strategy. 
Systems thinking that includes not just technical 
systems but also social systems and provision of 
services is essential for embedding technologies in real 
society. The survey results included a strong call to 
support future human resources related to the 
identified areas. As the responsible national bodies 
participated from the beginning in the survey (see ‘key 

157

Developing skills foresights, scenarios and forecastsNISTEP (2010d). The 9th science and technology 
foresight: capability of local regions for green 
innovation. Report No 142. Tokyo: National Institute of 
Science and Technology Policy.

Shirakawa, N. (2011). Foresight-related activities at 
NISTEP. Presentation at UNIDO mutual learning 
workshop, Bucharest, 29 to 31 August 2011.

UNIDO (2005a). Organisation and methods. Technology 
foresight manual, Vol. 1. Vienna: United Nations 
Industrial Development Organisation.

UNIDO (2005b). Technology foresight in action. 
Technology foresight manual, Vol. 2. Vienna: United 
Nations Industrial Development Organisation. 

Yokoo, Y.; Okuwada, K. (2011). Identifying the direction 
and promoting factors of innovation toward grand 
challenges. Fourth international Seville conference on 
future-oriented technology analysis, Seville, 12 and 13 
May 2011.

Websites 

[URLs accessed 13.5.2014]

Ministry of Education, Culture, Sports, Science and 
Technology 
http://www.mext.go.jp 

National Institute of Science and Technology Policy. 
http://www.nistep.go.jp 

institutions’), the identified needs make their way into 
the national policies indirectly through the major policy 
papers which draw on them. The S&T forecasts have 
been used to create a draft of the government’s 
science and technology basic plan. The realisation time 
forecast in the surveys led to the setting of R&D 
targets in government and industry research institutes 
and universities. The survey results are also being 
considered in the process of developing the R&D plans 
in educational and other organisations.

Employers – especially those active in research and 
development – make use of the results of the 
foresights, and it can be assumed that many consult 
the results when devising their human resources 
strategy, though an exact overview of the methods 
they use is not available.

References

[URLs accessed 13.5.2014]

European Commission (2008). The European foresight 
monitoring network: collection of EFMN briefs, Part 1. 
Brussels: European Commission. http://ec.europa.eu/
research/social-sciences/pdf/efmn-report_en.pdf 

European Commission (2013). Japanese S+T foresight 
2035. Foresight brief No 35 of the European foresight 
monitoring network. http://www.foresight-platform.eu/
wp-content/uploads/2011/04/EFMN-Brief-No.-35-
Japanese-S+T-Foresight-2035.pdf

NISTEP (2010a). Contribution of science and 
technology to future society: summary of the 9th 
science and technology foresight. Report No 145. 
Tokyo: National Institute of Science and Technology 
Policy.

NISTEP (2010b). The 9th science and technology 
foresight: the 9th Delphi survey. Report No 140. Tokyo: 
National Institute of Science and Technology Policy.

NISTEP (2010c). The 9th science and technology 
foresight: future scenarios opened up by science and 
technology. Report No 141. Tokyo: National Institute of 
Science and Technology Policy.

158

Guide to anticipating and matching skills and jobsACRONYMS

CSTP

MEXT

NISTEP

R&D

Council for Science and Technology Policy

Ministry of Education, Culture, Sports, Science and Technology 

National Institute of Science and Technology Policy

research and development

159

Developing skills foresights, scenarios and forecastsANNEX 5. 
Korea

A5.1. 

 Economic and social context

The Republic of Korea has a distinctive education 
system that produces outstandingly well educated 
people in comparison with international standards, and 
this is believed to be one of the important factors 
behind Korea’s rapid economic rise in the last decades. 
Education is egalitarian and highly appreciated. 
Nevertheless, the recent focus on rapidly-changing 
knowledge-based societies has brought new demands, 
and the drawbacks of the Korean education system, 
such as the lack of flexibility and autonomy in education 
institutions and insufficient diversity and creativity, 
have become more apparent. There are significant 
disproportions between labour market needs and 
workforce supply. The systems of the labour market 
(employers) and education are not sufficiently 
interlinked. The government seeks to address these 
problems by various initiatives, including merging the 
Ministry of Education with the Ministry of Science and 
Technology, the establishment of sector skill councils 
that identify and monitor skill needs in high-growth and 
technology-intensive sectors, and surveys of the 
demand for training in the labour market. The practical 
effect of these measures so far seems limited.

A5.2. 

 Main challenges and policy objectives

The main objectives of the recent nationwide science 
and technology foresight exercise include:

(a)  providing a vision and direction for emerging 

science and technology areas;

(b)  identifying technologies that have a high potential 
of contributing to growth (in terms of national 
wealth as well as quality of life);

(c)  implementation of the results in science and 
technology (S&T) policy (concretely, as in the 
national S&T basic plan).

A5.3. 

 Key institutions, processes, approaches, 
methodologies and results

Since the 1990s, Korea has had a national foresight 
programme which has assisted the government in 
exploring the future of science and technology. It is 
embedded in legislation (S&T basic law) and is 
undertaken every five years by the Korean Institute for 
S&T Evaluation and Planning (KISTEP). Initially, only the 
Delphi method was used, but since the third national 
technology foresight (2003--04) a multi-methodology 
approach has been taken. The revision of the third 
national technology foresight (2007) and the fourth 
national technology foresight (2010--11) were directly 
used to feed into the national S&T basic plans that 
outline the S&T policy. 

Following the Japanese example, Korea has developed 
its own foresight culture, with each of the ministries 
having started its own foresight projects. This 
background, together with the participation of the 
ministry responsible for education in the national 
foresight, sets the basic conditions for interlinking the 
areas of technology foresight and education policy, 
though concrete mechanisms are yet to be formalised.

The following description is of the third national 
technology foresight of 2003--04. The two main 
institutions involved in the exercise are the Ministry of 
Science and Technology (MOST) and the KISTEP.

The following processes and methodologies were 
employed:

(a)  a panel of experts from various disciplines was 

established with athe task of identifying the future 
prospects and needs of Korean society. The 
prospects and needs were distinguished according 
to four so-called ‘actors’: world, nation, society and 
individual: 15 main themes and 43 subthemes were 
explored;

160

Guide to anticipating and matching skills and jobs(b)  a separate survey of 1 000 experts and 1 000 

References

members of the general public was conducted, 
followed by an additional Delphi study via an 
internet-based questionnaire, with more than 
32 000 experts (PhD holders) addressed in the first 
round. Almost 17% (ca 5 400) of them replied in the 
first, and 61% (3 300) in the second round. The 
questionnaire contained 40 to 50 questions about 
one or two of the eight defined fields of technology;

(c)  scenario panels of experts with various 

backgrounds were set up. They visualised likely 
scenarios for Korea in education, labour, health 
services and safety systems. Alongside the 
experts, a group of undergraduate students 
contributed to the education panel, choosing one or 
two future technologies that might have the biggest 
impact on systemic change in education. They also 
developed related scenarios. 

Databases of technology subjects were created for 
policy and strategy use. Out of a total of 761 
technologies, 189 were selected as priorities. The 
databases were also complemented by the 
technologies with high investment potential in other 
countries.

A5.4. 

 Lessons learned

The results of the foresight were used to formulate 
strategic initiatives and contributed to determining the 
priorities and allocation of resources regarding future 
science and technology developments. Future 
development of the education system was outlined 
within the scenarios phase in terms of how the 
technologies might change it.

The third national technology foresight enjoyed 
unprecedented public promotion. Cartoons, science 
books, posters, a short movie and similar promotional 
materials were produced with the aim of spreading a 
foresight culture in Korea. The revision of the third 
national technology foresight that took place in 2007 
contributed to the policy paper on the national S&T 
basic plan.

[URLs accessed 21.5.2014]

European Commission (2013). Korea 2030. Foresight 
brief No 36 of the European foresight monitoring 
network. http://www.foresight-platform.eu/wp-content/
uploads/2011/04/EFMN-Brief-No.-36-Korea-2030.pdf 

Kim, G-J. (2002). Education policies and reform in 
South Korea. In: Secondary education in Africa: 
strategies for renewal. Africa region human 
development working paper series. The World Bank.

Kis, V.; Park, E. (2012). A skills beyond school review of 
Korea. OECD reviews of vocational education and 
training. Paris: OECD Publishing.

Ra, Y-S.; Shim, K.W. (2009). The Korean case study: 
past experience and new trends in training policies.  
SP Discussion paper No 0931. Washington, D.C.:  
The World Bank.

Shin, T.; Hong, S-K.; Grupp, H. (1999). Technology 
foresight activities in Korea and countries closing the 
technology gap. Technological forecasting and social 
change, Vol. 60, No 1, pp. 71--84.

Yim, H. (2011). Korean views for technology foresight in 
society. Presentation at KISTEP, 10 October 2011.  
http://www.vtt.fi/files/events/foresight_seminar/
Ennakointiseminaari1010_Yim.pdf 

Websites 

[URLs accessed 21.5.2014]

Korea Educational Development Institute.  
http://eng.kedi.re.kr/khome/eng/webhome/Home.do 

Korea Institute of S&T Evaluation and Planning.  
http://www.kistep.re.kr 

Ministry of Education, Science and Technology.  
http://english.mest.go.kr/enMain.do 

161

Developing skills foresights, scenarios and forecastsACRONYMS

S&T

KISTEP

science and technology

Korean Institute for Sciences and Technology Evaluation and Planning

162

Guide to anticipating and matching skills and jobsANNEX 6. 
The European Union

A6.1. 

 Context

The European Union has an overall strategic goal to 
promote more and better jobs and to offer equal 
opportunities for all in its social agenda. There are 
numerous options for supporting this goal, one of 
which is a foresight exercise developed with the 
financial support of the European Commission in the 
second half of last decade. 

A6.2. 

 Main challenges

The restructuring processes underway in Europe 
should be placed in the broader context of securing 
and improving the EU’s competitiveness, plus 
redeploying the European economy towards new 
activities with more added value and providing new 
and better jobs. To be successful, this approach should 
be underpinned by more strategic management of 
human resources, encouraging a more dynamic and 
future-oriented interaction between labour supply and 
demand. Without this there is the risk that greater 
shortages, gaps and mismatches of skills will result in 
structural unemployment.

Europe aims to renew its competitiveness and 
increase its growth potential and productivity, while 
strengthening social cohesion; this places emphasis on 
knowledge, innovation and optimisation of human 
capital. Fostering growth and jobs by stepping up the 
transition to a knowledge-intensive society remains a 
core ambition. 

A6.3. 

 Methodology

A methodology developed by Professor Maria João 
Rodrigues over the last decade supports strategic 
management of human resources. Professor 
Rodrigues, with the financial support of the European 
Commission, identified and analysed foresight on 
emergent job profiles and their training needs in the 

different EU Member States and recommended a 
European foresight cross-sector methodology. The 
purpose was to prepare possible future actions to 
investigate new jobs for Europe, encouraging more 
effective interaction between innovation, skills 
development and jobs creation. 

The methodology takes several factors into account: 
competitiveness, industrial evolution, innovation and 
globalisation. It also integrates elements such as the 
sectoral dimension, mechanisms to update the results, 
and levels of precision in job profiles. This methodology 
has been designed with the following possible uses in 
mind:

(a)  to identify new sector drivers for jobs renewal  
(main trends in demand and supply, process, 
products and service innovations, organisation);

(b)  to support sector innovation and employment by 
developing foresight instruments to analyse 
emerging activities, shaping factors, skill needs  
and job profiles;

(c)  to spread new job profiles in each sector in 

vocational guidance, employment services and 
competency validation;

(d)  to improve strategic sector planning in education 

and training;

(e)  to encourage the development of partnerships for 
innovation, skills and jobs at sector level, involving 
all stakeholders.

The methodology includes the following steps: 

(a)  present and analyse the main economic and 

employment trends and the occupational structures 
of the sector over the past 15 years;

(b)  identify the main drivers of change with regard to 
the sector: economic, technological, organisation;

163

Developing skills foresights, scenarios and forecasts(c)  identify the main emerging or changing sector job 

(f)  Usable within short time frames and with limited 

profiles, skills and competencies;

resources.

(d)  identify three different scenarios (roughly over a 
period of seven years) for future development;

(g)  Focusing on different possible uses:

(i)  early identification of emergent jobs and skills 

(e)  draw the main implications of these scenarios for 

needs;

employment trends: new jobs in expansion, jobs in 
transformation and jobs in decline;

(f)  identify the strategic choices to meet sector skill 

needs;

(g)  identify specific implications for education and 

training of sector skill needs;

(h)  formulate the main recommendations.

The European foresight methodology to identify 
emergent jobs and their skills needs is described 
below (Rodrigues, 2007).

A6.3.1. 

  Framing a European foresight 
methodology on skills needs

A6.3.1.1. 

 Main features of the methodology

(a)  A forward-looking approach:

 (i)  focusing on activities with relevant potential for 

jobs creation;

 (ii)  identifying the main drivers and their 

implications for skills needs.

  (ii)  building qualification standards according to 

the levels defined by the European 
qualifications framework (EQF);

  (iii)  supporting a credit system for transfer, 

accumulation and recognition of learning 
outcomes, as proposed by the European credit 
system in education and training (ECVET);

  (iv)  curriculum development;
  (v)  vocational guidance: possible use by the portal 
on learning opportunities throughout the 
European space (PLOTEUS) and the European 
jobs network (EURES);
  (vi)  organisational development;
 (vii)  human resources management at company, 
sectoral, regional, national and EU level;
 (viii)  sectoral learning strategies and competency-

building projects;

  (ix)  partnerships for innovation, skills and jobs, in 
connection with high-level industrial groups, 
clusters, lead markets and technology 
platforms promoted at European and national 
level.

(b)  Enabling a strategic conversation by identifying 

(h)  Easy to use and adaptable by the main 

consistent, plausible but contrasting scenarios to 
support strategic choices by the main stakeholders.

stakeholders.

(c)  Keeping a flexible approach on occupations 
evolution, open to different combinations of 
competency units.

(d)  Adaptable to different national contexts.

A6.3.1.2. 

 Main research techniques 

The main techniques to be used are:

(a)  statistical and forecast analysis;

(b)  desk analysis;

(e)  Enabling two-way interfaces between foresight and 
forecast analysis, ensuring compatibility with the 
statistical classification of economic activities of the 
European Community (NACE), the international 
standard classification of occupations (ISCO) and 
the international standard classification on 
education (ISCED).

(c)  foresight techniques, notably systems analysis, 

scenario building and actor games; at this stage of 
the pilot project, their main purpose is neither 
simulation nor optimisation, but exploration of 
possible scenarios;

(d)  web search;

164

Guide to anticipating and matching skills and jobs 
(e)  workshops involving top experts in the focused on 

economic activities, dealing with markets, 
technologies, human resources, research or education 
and training and coming from big, medium and small 
companies, business consultants and research, 
education and training institutions.

The selection of experts is crucial for the outcome of 
these workshops, as are the roles of chair and 
rapporteur. The preparatory phase is also particularly 
important in providing a good background report to all 
the participants. Previous interviews and meetings may 
be required for good preparation. Moreover, these 
workshops should be conducted with a precise 
methodology, as follows.

A6.3.2. 

 Presentation of the methodology

Taking into account the framework already defined, we 
will now present a draft methodology for the expert 
workshop, assuming that it should be prepared for in 
detail and that its outcomes should be further 
developed by its rapporteur.

The sequence of steps to be performed, with clear 
direction by the chairperson and the rapporteur, should 
be the following.

Step 1:  What are the main economic and employment 
trends and structures in these activities?

The aim is to present a brief stastical portrait of these 
activities, focusing on the industries under 
consideration in terms which are as disaggregated as 
possible in order to obtain statistical data:

(a)  comparing EU and its main international partners;

(b)  comparing Member States with the EU total in 
three different dates over the past 15 available 
years, if possible, when the purpose is to analyse 
the main trends.

It would also be very useful to take into account  
the projections to be provided by the project on 
pan-European skills forecasts.

To compare employment trends and the relative 
specialisation of the EU versus other international 
partners, the following would be particularly relevant: 

(a)  employment trends by industry in the EU, US, 

Japan and others (notably in BRICS);

(b)  the same in vertical shares;

(c)  the same in horizontal shares.

To compare the employment trends and the relative 
specialisation of the Member States:

(a)  employment trends by Member State in each 

considered industry;

(b)  the same in vertical shares, of each Member State/

total EU.

To measure the key economic trends:

(a)  output trends by industry in the EU, US, Japan and 

others (notably in BRICS);

(b)  trade balance by industry in the Member States;

(c)  knowledge intensity by industry in the EU, US, 

Japan and others (notably in BRICS).

To measure the qualification content of each 
considered industry:

(a)  employment trends by education level in each 

industry in the EU.

To measure the occupation structures of each industry:

(a)  employment by occupation in the Member States 

in each industry in the EU;

(b)  employment by form of work organisation in the 

Member States. 

(Rodrigues, 2007).

Step 2:  What are the main drivers of change in these 

activities?

Table A3 provides the grid which aims at identifying the 
main drivers of change with possible implications for 
employment and competency trends.

165

Developing skills foresights, scenarios and forecastsTable A3.  Main drivers of change in each activity

Main drivers 
activities

Technical 
product and 
services

Technical 
process

Economic 
demand

Economic 
supply

Organisational 
conceptual

Organis. 
executive

Others

Content industries

Specialised 
software

Interactive 
software 
customised 
software

Automatisation Very rapid 
increase, 
differentiation, 
price also 
matters

Network 
companies, 
role of 
start-ups 
and SMEs, 
outsourcing, 
offshoring

Specialisation, 
creative activity

Routinisation

Generic software

Computing 
hardware

Telecommunication 
services

Telecommunications 
hardware

Source: Rodrigues (2007).

These drivers, defining challenges and opportunities 
are:

(a)  in the economic dimension, the main trends in 

demand and supply;

(b)  in the technological dimension, the main trends in 
process innovations and in product and service 
innovations;

(c)  in the organisational dimension, the main trends 
regarding the more conceptual functions and the 
more executive functions.

The time horizon to be proposed for this exercise is 
seven years: 10 years would be too long and too risky, 
taking into account the present speed of change, while 
five years would be too short to organise large-scale 
initiatives at European level. Seven years is a relevant 
policy-making cycle in the EU, based on the financial 
perspectives and EU community programmes for 
research and technological development (RTD), 
innovation or lifelong learning.

166

Guide to anticipating and matching skills and jobsStep 3.  What are the main emergent competencies by 

function in each of these activities?

Considering these assumptions, the current exercise to 
identify skills needs:

This exercise, assumes that:

(a)  competencies can basically be of three types: 

theoretical (‘knowledge’ in ECVET, technical (‘skill’ 
in ECVET); and social (‘competencies’ in ECVET);

(b)  competencies are combined in occupation profiles, 

where they can be core competencies, 
specialisation competencies or complementary 
competencies. Most occupation profiles are in 
permanent transformation through adding new 
competencies up to a point where emergent new 
occupation profiles can be signalled and 
recognised;

(c)  these occupation profiles are quite often translated 
into qualifications standards, which are diverse 
across Member States, reflecting their different 
structures of education, work organisation, 
collective agreements and social identities;

(d)  transnational cooperation to define common 

qualification standards in high-level education is 
taking place with the Bologna process; under the 
Copenhagen process, the same progressive change 
can take place for technical and vocational 
education and training (TVET), using the EQF as a 
common frame as well as the method proposed by 
ECVET. This system is based on units of learning 
outcomes combining the three types of 
competency.

(a)  should be general enough to accommodate the 

national specificities afterwards;

(b)  should be focused on competencies and on 
occupation functions and not on occupation 
profiles, which are diverse across Member States. 
Nevertheless, if the need for a new occupation 
profile is clearly identified, it should be registered;

(c)  should locate these competencies in a general grid 
defined by the main occupation functions: general 
management, marketing, financial and 
administrative management, research and 
development (R&D), logistics, production 
management, production, quality and maintenance. 
This grid should be connectable with the eight 
levels of the EQF;

(d)  should not aim at an exhaustive listing of 

competencies in each occupation function, but at 
identifying some of the emergent critical 
competencies in each function, to meet the challenges 
defined by the main drivers of change. An exhaustive 
list of competencies can be found in several national 
occupation directories prepared by Member States;

(e)  should take into account the need to define 

consistent competency units (or unit of ‘learning 
outcomes’ as named by ECVET), composed by 
theoretical, technical and social dimensions.

Table A4 helps identify new critical competencies by 
occupation function in the specialised software industry.

167

Developing skills foresights, scenarios and forecastsTable A4.  New critical competencies by occupation function in the specialised software industry

Organisation 
conceptual

Organisation 
executive

Routinisation

Specialisation, 
creative 
activity

Promoting 
creative 
environments

Improving 
control 
systems

Economic 
supply

Outsourcing, 
off-shoring 

Tech. process Tech. products/

Automation

services

Interactive 
software

Global 
networking 
Intercultural 
management

Developing 
corporate image

International 
finance 
management

International 
cooperation and 
competition

Applying new 
languages

Improving 
mechanisms for 
interactivity

International 
supply chain

International 
supply chain

Main drivers Economic 

demand

Very rapid 
increase, 
diversification, 
customisation

Focus on 
new customs 
needs

Exploring 
new market 
segments

Functions

General 
management

Marketing

Financial and 
administrative

R&D

Logistics

Production 
management

Quality

Maintenance

Production

Source: Rodrigues (2007).

168

Guide to anticipating and matching skills and jobsStep 4.  What are the main scenarios and their 

implications for employment trends?

This exercise aims to identify different possible 
scenarios for the employment trends, to be defined in 
terms of specialisation patterns, taking into account 
the international division of labour:

(a)  where some of the considered economic activities 

can develop, and others decline in Europe;

(b)  where the more conceptual functions or the more 

executive functions can either develop or decline in 
Europe.

The possible employment trends are given by 
comparing the present situation with the possible 
future situation over seven years. This comparison is 
signalled below by:

(a)  I, increase the employment level;

(b)  M, maintain the employment level;

(c)  D, decrease the employment level;

(d)  ?, uncertain trend.

In answering these questions, the experts’ opinions 
should be confronted with the statistical analysis on 
employment trends presented in step 3.

Table A5.  Scenarios and implication for employment trends

Active/functions 
scenarios

Content 
industries
Conceptual 
executive

Specialised 
software
Conceptual 
executive

Generic 
software
Conceptual 
executive

Tele-communication 
services
Conceptual 
executive

Tele-communication 
hardware
Conceptual 
executive

I

I 

I

I 

I

I 

I

I 

D I 

D I 

D I 

D I 

I

D

I 

I 

A.  Expanding all 
activities

B.  Expanding all 

activities except 
hardware

C.  Expanding 

conception, 
reducing execution

D.  Focusing on 
specialised 
software, content 
industries and 
telecommunications

Source: Rodrigues (2007).

These scenarios should be built on plausible trends and 
their consistent combination. It is then be possible to 
identify the most probable ones among them in 
analytical terms and, afterwards, the most desirable 
ones. It can also be useful to launch a number of ‘wild 
cards’ by suggesting some possible unattended 
scenarios.

All these contrasted scenarios provide the basis for a 
‘strategic conversation’ in the expert workshop, which 
should then be deepened by looking at least two 
scenarios, one best and one worst, among the 
probable ones. This is the purpose of the next step.

Step 5.  What are the implications of these scenarios 

for competencies and occupation profiles?

In this step, it should be possible to reach a central 
outcome of this methodology, by building on the 
previous steps.

First, it is necessary to deepen the analysis of at least 
two of the previous scenarios by specifying the 
implications for each occupation function in terms of 
jobs expanding, transforming or declining. Table A6 
should be filled in by indicating the occupation function 
at stake.

169

Developing skills foresights, scenarios and forecastsTable A6. 

 Jobs in expansion, transformation or decline in Scenario E ‘Expanding in specialised 
software, content industries and telecommunications and declining in executive jobs in 
the other industries’

Employment trends  
activities

New jobs in 
expansion

Existing jobs in 
expansion

Jobs in 
transformation

Jobs in decline

Content industries

Specialised software

Generic software

Computing hardware

Telecommunication 
services

Telecommunication 
hardware

Source: Rodrigues (2007).

All functions

All functions

R&D, logistic

All functions

R&D, marketing, 
logistic

General managers, 
marketing, financial, 
production, quality

Production, quality

Production, quality

General managers

Production, quality

Building on the previous steps, particularly step 3, it 
should then be possible to describe the scenarios, as 
telling possible stories about the future, in the 
following terms.

In scenario E, ‘Focusing on specialised software, 
content industries and telecommunications’:

(a)  the jobs in specialised software will expand, 

requiring new competencies, such as in general 
management: focus on customer needs, building 
global networks, intercultural management, 
promoting creative environments, improving control 
systems; in marketing, etc.

Step 6.  What can be the main strategic choices to 

meet these skills needs?

In discussing this issue, it is important to bear in mind 
that skills needs can be met in quite different ways:

(a)  changing work organisation;

(b)  retraining employed workers;

(c)  recruiting unemployed workers with or without 

retraining;

(d)  recruiting young people coming from education, 

with or without retraining;

(b)  the jobs in generic software, in computing hardware 
and in telecommunications software will decline.

(e)  recruiting workers from other Member States;

(f)  recruiting workers from non-Member States;

This concrete story about the future can only be 
exemplified during the workshop. Its detailed drafting 
can only be carried out afterwards by the rapporteur, 
who will circulate it among the experts for their 
comments.

If there is a need to draw implications from these 
scenarios for some Member States, it is possible for 
them to adapt them using a correspondence grid such 
as Table A7. However, it is never possible to make a 
mechanical transposition of European scenarios to 
national scenarios, as several adaptations are required.

(g)  outsourcing and offshoring;

(h)  other ways.

For an identified critical skills gap, it might be useful to 
draft a flow chart to identify possible solutions.

Step 7.  What are some of the more specific 

implications for education and training?

Among the solutions to meet skills needs, many 
depend on new responses from education and training 
institutions. This methodology can support future 
choices by providing:

170

Guide to anticipating and matching skills and jobs(a)  Table A4 on the critical competencies by occupation 

(g)  recommendations to meet the skills needs;

function in each industry;

(b)  Table A6 on jobs in expansion, transformation or 

decline;

(c)  Table A9 on occupation profiles by function and 

industry in a specific Member State.

Step 8. Main recommendations

In conclusion, some recommendations can be 
formulated by building on the main outcomes of this 
methodology:

(a)  a map of emerging economic activities and their 

main connections;

(h)  support to sectoral learning strategies;

(i)  support to partnerships for innovation and jobs 

creation.

A6.3.3. 

 Further developments to adapt European 
references to national or local conditions

The outcomes of this expert workshop will lead to 
references at European level which can be relevant but 
are necessarily quite generic and abstract. In order to 
adapt them to the national or local conditions, it is 
necessary to mobilise more information, involving key 
stakeholders at these different levels:

(b)  the employment trends of these activities in Europe 

(a)  strategic choices to be made by the different actors;

within a globalised economy;

(b)  framework conditions to develop the cluster and 

(c)  drivers of change and implications for critical 

competency building;

competencies;

(c)  concrete occupation profiles in each occupation 

(d)  new critical competencies by occupation functions;

function;

(e)  scenarios and implications for employment trends 

(d)  various choices to meet the identified skills needs.

and competencies;

(f)  a list of jobs expanding, changing or declining;

The following tables provide a grid to extract this 
supplementary information.

Table A7.  Main corporate strategies by activity

Corporate strategies  
and activities

Main competitive factor  
Cost efficiency/product 
differentiation

Scope broadening/
specialisation in  
the range of products

Corporate structure vertical 
integration/networking/
outsourcing/offshoring

Content industries

Specialised software

Generic software

Computing hardware

Telecommunications 
services

Telecommunications 
hardware

Source: Rodrigues (2007).

171

Developing skills foresights, scenarios and forecastsTable A8.  Key framework conditions by activity

Research Intellectual 

property 
rights

Human 
resources

Venture 
capital

Tax 
incentives

Infra-
structures

Standards Others

Key framework 
conditions 
activities

Content industries

Specialised software

Generic software

Computing hardware

Telecommunications 
services

Telecommunications 
hardware

Source: Rodrigues (2007).

Table A9.  Occupation profiles by function and activity in the EU Member States X

Activities

Content 
industries

Specialised 
software

Generic 
software

Computing 
hardware

Telecommunications Telecommunications

Function/ 
EQF level

General 
management

Marketing

Financial and 
administrative

R&D

Logistics

Production 
management

Production

Quality

Maintenance

Function/ 
EQF level

Source: Rodrigues (2007).

172

Guide to anticipating and matching skills and jobsTable A10.  Strategic choices to meet skills needs

Choices to meet 
skills needs and 
activities

Changing 
work 
organisations

Re-training 
employed 
workers

Recruiting 
unemployed 
workers

Recruiting 
young 
people

Recruiting 
workers 
from other 
Member 
States

Recruiting 
workers 
from 
abroad

Offshoring 
and 
outsourcing

Content industries

Specialised software

Generic software

Computing hardware

Telecommunications 
services 

Telecommunications 
hardware

Source: Rodrigues (2007).

A6.4. 

 Lessons learned

•  computer, electronic and optical products;

A series of forward-looking sector studies was 
elaborated under the European Community 
programme for employment and social solidarity 
(PROGRESS) (2007--13) using the European foresight 
methodology. They were completed in 2009. 

•  building of ships and boats;

• 

furniture;

•  electricity, gas, water & waste;

•  distribution, trade;

The sectors covered were:

•  non-energy extractive industries;

• 

tourism including hotels, catering and related 
services;

• 

textiles, garments and leather products;

• 

transport;

•  printing and publishing;

•  post and telecommunications;

•  chemicals, pharmaceuticals, rubber and plastic 

•  financial services (banking, insurance and others);

products;

•  non-metallic materials (glass, cement, ceramic, 

etc.);

•  electromechanical engineering;

•  health and social work;

•  other services, maintenance and cleaning.

173

Developing skills foresights, scenarios and forecastsA6.4.1. 

 Use of the studies

Websites 

[URLs accessed 21.5.2014]

European employment strategy 
http://ec.europa.eu/social/main.
jsp?langId=en&catId=101 

EU skills panorama. 
http://euskillspanorama.cedefop.europa.eu/

PROGRESS programme 2007--13. http://ec.europa.eu/
social/main.jsp?catId=327

Following input from the European Commission (82) 
these sectoral studies were disseminated to European 
sectoral social partners with the aim of encouraging 
debates between them about possible trends in their 
sectors. The results were also disseminated among the 
different services in charge of these sectors inside the 
Commission.

The main message for the Commission is the 
importance of creating sectoral capacity to update 
these first studies (for example, to take on board the 
economic crisis). Given the lack of a space for sharing 
information about sectoral trends at European level 
(national sectoral observatories may exist but any 
exchange between them is at European level), the 
Commission supports the concept of European 
sectoral councils.

This concept will be an element of the EU skills 
panorama (83), the aim of which is to develop links with 
the services in charge of education (84).

References

European Commission (2007). Comprehensive sectoral 
analysis of emerging competences and economic 
activities in the European Union. Invitation to tender No 
VT/2007/090. Tender specifications. http://ec.europa.
eu/social/main.jsp?catId=624&langId=en&callId=86& 
furtherCalls=yes 

Rodrigues, M.J. (2007). Innovation, skills and jobs. 
Pilot-project to develop a European foresight 
methodology to identify emergent jobs and their skills 
needs. Working document 8.3.2007.

(82) Reply of Jean François Lebrun, Advisor EMPL/C, European Commission, on the email request No 19965 from 29.5.2013.
(83) http://euskillspanorama.cedefop.europa.eu/
(84) See the EU employment strategy:  http://ec.europa.eu/social/main.jsp?langId=en&catId=101

174

Guide to anticipating and matching skills and jobsACRONYMS

ECVET

EQF 

R&D

TVET

European credit system in education and training

European qualifications framework

research and development

Technical and vocational education and training

175

Developing skills foresights, scenarios and forecastsANNEX 7.  
Skills foresight in the EU enlargement region

A7.1. 

Introduction

A7.2. 

 Economic and social context

The European Commission has entrusted the European 
Training Foundation (ETF) with supporting enlargement 
countries (85) in their human resources development 
(HRD) efforts. The overall objective is to assist the 
countries in developing the skills of their people for 
sustainable economic development and social 
cohesion in a medium to long-term perspective. The 
ETF initiative is part of the drive to support the 
implementation of the second cycle of the European 
Union (EU) instrument for pre-accession assistance 
2014--20 (IPA), which requires a future-oriented 
strategy. 

Foresight is one of four components of the ETF project 
and will be complemented with a review of institutional 
arrangements and monitoring at national level, as well 
as regional cooperation among the enlargement 
countries. At national level, the aim of foresight is to 
elaborate a commonly agreed vision and a roadmap for 
skills 2020 in a wider human resources development 
context.

Skills will be at the centre of the foresight initiative in 
the enlargement countries, addressing the question 
‘Which skills should the country develop towards 2020, 
and how can these skills be generated by the 
education and training system?’

This case description draws on the experience to date 
in implementing foresight in two enlargement 
countries.

Enlargement countries need to ensure a more 
coherent and evidence-based policy approach to 
human resources development and improved 
institutional and interinstitutional cooperation. 
Strengthening evidence and capacity for better 
institutional performance will lead to a better match 
between education and training and the needs of the 
economy and the labour market. A more strategic 
approach for developing a vision for human resource 
development needs to be adopted. 

The rationale for skills anticipation in the enlargement 
region is not only to minimise labour market failure and 
to ensure better careers and lives for individuals; there is 
also the additional purpose of informing programming 
for the next cycle of EU structural funds, for example, 
IPA for the period 2014--20. 

A qualitative approach is applied to developing a vision 
for skills 2020, taking broader issues and trends into 
account and building on existing evidence. 

A7.3. 

 Main challenges and policy objectives

Key stakeholders in the countries have recognised the 
need for policy coherence and have outlined strategic 
development goals in education, employment and 
social inclusion in national and regional development 
plans, in sector policies and programmes and in related 
strategies. What is needed is to derive implications for 
skills from these strategies.

(85) Albania, Bosnia and Herzegovina, the former Yugoslav Republic of Macedonia, Kosovo (this designation is without prejudice to a position on 

status, and is in line with UNSCR 1244 and the ICJ Opinion on the Kosovo Declaration of Independence), Montenegro, Serbia, Turkey.

176

Guide to anticipating and matching skills and jobsFigure A6.  Vision for skills 2020

Major efforts are being made in the region to match 
the needs of the labour market with training and skills 
development. These efforts aim at reducing the 
mismatch of demand and supply of skills and 
supporting the economic and social development of 
the countries. All countries have been working on 
national HRD strategies, with more or less success, in 
focusing on skills and in transforming strategies into 
concrete and forward-looking measures. A key aspect 

of the problems faced by the countries relates to the 
capacity of institutions for implementation and 
interinstitutional cooperation and coordination. 
While countries recognise the need to strengthen 
their policies to make HRD a cornerstone of their 
economic and social development, a shared vision 
among all stakeholders and institutions, as well as the 
means to implement an effective policy cycle, remain 
top priorities. 

177

Developing skills foresights, scenarios and forecastsThe key objectives of skills foresight in the 
enlargement region are:

(a)  to break down silos between ministries and to 

develop more joined-up policy approaches for skills, 
bringing together the key players in the sector and 
drawing together the HRD component in these 
strategies and identifying the interfaces between 
them;

(b)  to promote a more future-oriented approach to 

skills policies, paying attention to key stakeholders 
(private sector, civil society) which may not have 
been sufficiently engaged in the development of 
the strategies;

(c)  to flag key concerns in relation to skills, in particular 
the mismatch between demand for and supply of 
skills;

(d)  to promote capacity-building for the HRD policy 
cycle with regard to the implementation of EU 
support provided under IPA.

Interinstitutional cooperation for better coordination, 
monitoring and implementation of the many strategies 
has increased but still needs substantial improvement. 
There are platforms and councils in most of the 
countries to discuss issues related to skills policies, job 
creation, employment, or social inclusion, but their role 
and capacity are still limited. Social partners have been 
involved in developments at system, regional, sectoral 
and local level, but not sufficiently. 

The qualitative foresight approach adopted should be 
supported by quantitative data on skills anticipation and 
robust evidence. In most countries of the enlargement 
region, quantitative forecasts and a coherent labour 
market information system remain to be improved. 

A7.4. 

A7.4.1. 

 Key institutions, approach, process 
and results
 Institutions involved and active

Participation in the foresight process ensures 
representation of the main government institutions 
with a stake in the HRD sector, economic actors and 
civil society. The institutions involved are the ministries 
of finance and European integration, line ministries 
(labour, education, social affairs, research, the 
economy), government agencies (public employment 
services, agencies for vocational education and training 
and for higher education), social partners and civil 
society organisations. In line with the management 
system for EU structural funds in place, a coordinating 
government institution is nominated which takes the 
lead to drafting and disseminating the results and 
forwarding them for adoption to the government. 

A7.4.2. 

 Approach

Foresight for skills 2020 in the enlargement countries 
also aims at informing the programming process for 
IPA, contributing to the best use of funds. There are 
particular features of foresight which distinguish it from 
other strategic planning processes:

(a)  systematic: in the sense that it involves a 

well-designed approach based on a number of 
phases and using appropriate tools;

(b)  participatory: since it brings together a wide range 
of stakeholders and encourages interactions, 
networking and learning;

(c)  future intelligence gathering: studying trends and 

drivers, their interactions and possible disruptions, 
thereby allowing more evidence-based policy 
approaches and a level of anticipation;

178

Guide to anticipating and matching skills and jobs(d)  vision-building: exploration of alternative scenarios 
supports the eventual focus on a common vision 
and consensus-building;

Following this general design, specific actions were 
implemented in the first two countries where foresight 
had been carried out.

(e)  shaping decision-making: foresight empowers the 
participants to move beyond exploration to shape 
the future through more proactive thinking;

(f)  mobilising action: by engaging stakeholders it 
supports the pathway to effective policy 
implementation through joined-up approaches.

A7.4.3. 

 Process

The foresight process basically consists of four main 
phases: preparation, system building, foresight proper, 
and follow-up to ensure the best use of the results.

During the system-building phase, national government 
institutions and agencies, social partners and donor 
organisations were consulted. A government institution 
responsible for overall coordination of the foresight 
process was assigned, and further stakeholders 
consulted and invited to participate. The added value of 
foresight and expectations were discussed with the 
main stakeholders. 

Milestones of the ‘foresight proper’ process were three 
workshops, but it also included work done between 
these events, such as drafting inputs and summarising 
results, as well as consultation meetings. All work was 
done in the local language and translated into English. 

Table A11.  Overview of phases and tasks

Phases

Pre-foresight

System building

Foresight proper

Follow-up

Tasks/steps

Basic preparation of the exercise;
preliminary analysis of reference documents in HRD;
information gathering among a wider group of potential stakeholders;
mapping of existing government strategies.

Identification and commitment of participants;
securing political and technical support and resources;
identification of participants (involving relevant stakeholders in the exercise).

Issue analysis, strategic panorama and its skills relevance;
trends and drivers at global and national level;
developing success scenarios;
developing a shared vision, setting priorities and elaborating roadmap.

Ensuring commitment after the exercise;
documentation of the foresight process and results;
formal debriefing of results with key policy-makers;
communicating results to a wider audience.

Source: Casingena Harper et al. (2014).

179

Developing skills foresights, scenarios and forecastsWorkshop 1

This workshop brought together more than 30 
stakeholders. To place developing the skills vision in the 
wider context of the country’s macroeconomic 
strategy, government representatives presented the 
key national strategies. To complete the wider context, 
the main goals and comparative benchmarks of the 
Europe 2020 strategy for growth and jobs and the 
south-east Europe regional growth strategy 2020 were 
also brought in. A strategic panorama, mapping the 
core national strategies, was prepared and the 
consequent key issues and gaps discussed. This 
underlined the need to develop a coherent and 
forward-looking approach over the mid-term. Current 
issues and problems were discussed in breakout 
groups, covering the supply side, the demand side and 
cross-cutting issues related to human resource 
development. 

Workshop 2 

This workshop started with further exploration of 
demand side issues, debating what is currently done to 
anticipate future skills demands. A presentation of 
economic, social and technological global trends and 
drivers inspired a debate about their likely relevance 
and impact on the national skills system. Breakout 
groups worked on success scenarios, developing 
future visions linked to the main issues and defining 
the main actions to be taken to achieve these future 
visions. The workshop concluded with first drafts for a 
skills vision 2020. 

Workshop 3 

This workshop was a key element in the vision-building 
process. To ensure the policy relevance of the vision, 
key government institutions highlighted their core 
strategic priorities, followed by identifying and defining 
shared priorities which require a coherent approach and 
cooperation among stakeholders.

During the follow-up phase actions were taken to 
complete and finalise the roadmap, specifying how the 
vision will be realised. Clustered in a few (four) policy 

priorities, activities and measures were defined, each 
with the specification of the main responsible and 
coordinating bodies and the objectives to be achieved. 
These objectives were specified in terms of qualitative 
and quantitative targets, with result-oriented indicators. 
Baseline targets and targets to be achieved by 2020, as 
well as interim targets, remain to be fine-tuned with 
the support of monitoring experts. Among the priorities 
were systemic issues that will be further elaborated in 
the course an institutional capacity review which is 
foreseen within FRAME (86).

Results

The result to be achieved for each country is a concise 
vision for skills 2020, containing a joint vision 
statement, policy priorities, and a roadmap for how to 
make the vision real. The results are documented in the 
form of a short and concise paper, proposed for 
endorsement by the government and used as input for 
IPA 2014--20 programming.

Foresight is process-oriented work, undertaken 
together with key national stakeholders. Cooperation 
between different stakeholders – involving different 
government organisations, social partners, civil society 
organisations and researchers – is not a new practice. 
However, foresight works best with a shared 
understanding that this is a change management 
process which requires regular and long-term 
engagement. 

The added value of foresight as tool for strategic and 
future-oriented policy development lies not only in a 
well-documented result but also in the process. The 
experience enhances the adoption of a future-oriented 
mindset among stakeholders. The process does not 
end with the elaboration of a vision for skills; regular 
monitoring will ensure sustainable results and a 
tangible impact. As a management process, the 
foresight experience helps to develop future 
intelligence that can be applied for further, in-depth 
foresight studies.

(86) ETF FRAME project: http://www.etf.europa.eu/web.nsf/pages/Frame_project

180

Guide to anticipating and matching skills and jobsFigure A7.  Process of foresight

The question for foresight:
What skills should we (the country) develop by 2020 and how can the education and training system 
develop these skills?

FORESIGHT 
MILESTONES

Pre-foresight

MAIN ACTIVITIES

SCHEDULE

Basic preparation
Preliminary analysis of reference documents, ensuring political and 
technical support and resources

Commitment of decision makers
Information and coordination with key decision makers and stakeholders

November–
December 2012

System 
building, fact 
finding

Dialogue with stakeholders (line ministries, National Employment 
Agency, social partners, state statistical office, EUD, donors, etc.), 
organisation of workshops, agreement on agenda, joint preparatory 
meeting of key stakeholders

January–
February 2013

FORESIGHT 
MILESTONES

MAIN ACTIVITIES

SCHEDULE

Work-
shop 1

Launch of foresight: introduction to the process, team building, strategic 
panorama (mapping existing strategies), presentation of key macro-
economic and development strategies; group work on issues

February 2013
(1 day)

Work-
shop 2

Global trends and drivers and their relevance for the country, developing 
success scenarios and a first draft of vision statement (breakout groups)

March 2013
(1.5 day)

Work-
shop 3

Vision workshop, setting priorities and elaborating a roadmap
Result: draft Vision for skills with priorities and roadmap

April 2013
(2 days)

Follow-up

Result: Vision for skills with priorities and roadmap validated, edited 
and finalised
Paper submitted for adoption by the government

May, June 2013

181

Developing skills foresights, scenarios and forecastsTo provide an example of the overall process, timing 
and steps are depicted above in Figure A7.

sector, a specific industry or [subnational] region) might 
lead to more concrete and practice-relevant results. 

Reference

Casingena Harper, J. et al. (2014). The future FRAME 
foresight guide for skills 2020. Turin: European Training 
Foundation. 

Website

[URL accessed 22.5.2014]

ETF FRAME project, skills for the future.  
http://www.etf.europa.eu/web.nsf/pages/Frame_project 

A7.5. 

 Lessons learned

The process design for foresight needs to be tailored 
to the specific framework conditions in each country 
and each case. There are many variations and 
differences in the institutional set up and the timeliness 
of exercises compared to other processes. Previous 
experiences and – most important – parallel activities 
with similar future-oriented processes need to be 
considered and synergies explored. 

Alternative process designs may also be considered. 
The vision-building process may involve much wider 
groups, including larger parts of the business 
community and civil society. But when it comes to 
setting priorities and elaborating the details of a 
roadmap, small groups of decision-makers and experts 
may work most efficiently in achieving good results. 

It is important to build on past experiences and on 
national expertise, with the involvement of researchers. 
When working on skills, the opinions of employers, 
human resources (HR) managers of companies in key 
industrial sectors, and owners of small companies may 
certainly bring in additional insights. It is not enough 
just to involve employer representatives. 

Also important is scoping the foresight question, with a 
clear thematic focus for the process. Addressing ‘skills’ 
as such is like using a wide-angle lens: while this has 
advantages in terms of coherence, a view with a 
telephoto lens (for example, addressing an economic 

182

Guide to anticipating and matching skills and jobsACRONYMS

ETF

HRD

IPA

European Training Foundation

human resources development

instrument for pre-accession assistance

183

Developing skills foresights, scenarios and forecastsANNEX 8. 
Russia

A8.1.  Economic and social context

Similar to most industrialised countries, Russia is 
experiencing growing mismatch between the demand 
for labour with specific skills and competencies, and the 
training and education provided by the vocational and 
tertiary education system. One of the causes is 
accelerated technological progress, with the 
introduction of new technologies and methods into 
various industries and economic sectors, plus 
intensified global competition. The cycle of technological 
renovation is getting shorter even in more traditional 
industries (such as agriculture, natural resource 
extraction or construction), from decades to years; in 
new and emerging industries (such as information and 
communications technology (ICT) or biotech) substantial 
revision of technologies can take just a few years, and 
in some cases as little as 12 to 18 months.

The ‘normal’ process of coordination between 
education and training and labour markets, involving 
recognition of new tasks, adoption of new education 
and training programmes, and subsequent preparation 
of new specialists, today risks that demand for these 
specialists will disappear even by the time they 
complete their education. Additional investment in 
training will then be required to close the skills gaps. In 
Russian vocational education, the complete cycle from 
communication of a new skill demand to new 
specialists being ready can take between two and 
three years; in tertiary education, this cycle can take 
between four and six years. As the education system is 
often inflexible and unable to recognise new labour 
market needs, the cycle becomes even longer.

Figure A8. 

 Growing mismatch between training and new skills demand due to increased rate of 
technological change

Source: Authors.

184

Guide to anticipating and matching skills and jobsA8.2.  Main challenges and policy objectives

The inability of tertiary education to adapt to labour 
market needs has caused severe mismatch: according 
to December 2012 statistics from the Ministry of 
Labour of the Russian Federation, only 43% of new 
specialists in the formal sector of the Russian economy 
find jobs in accordance with their qualifications. In the 
informal sector, the gap is even higher: only 24% work 
in accordance with their qualifications.

One of the ways to cope with this challenge is to 
reorganise the cycle through replacement of demand 
recognition with demand anticipation (Figure A8). This 
allows an industry’s technological development to be 
aligned with human capital development, and prepares 
specialists for industry challenges that may unfold in the 
future. Also, because the foresight process establishes 
communication between employers and education 
institutions, this allows better synchronising even for 
existing labour demand.

The current context of the skills 2030 foresight project 
is set by growing pressure to revise industrial policies 
in Russia. Sectors that have been primary drivers of 
economic growth through the 2000s (oil and gas, steel 
and mining, and defence) are now stagnating. 
Technologies in infrastructure sectors (transportation, 
energy) are obsolete, and substantial investments into 
modernisation will be required through the 2010s. The 
government is also considering the possibility of using 
some of the Russia’s competitive advantages, such as 
the relatively high quality of its technology and 
engineering education, to create new drivers of 
economic growth such as in the information and 
telecommunication sector or aerospace.

Four key factors in establishing the need for the skills 
foresight in Russia are:

(a)  changing technologies, processes and managerial 

approaches in the economy resulting from 
technological progress and social innovations;

(b)  increasing global competition for consumer markets 

and skilled labour;

(c)  government efforts to modernise industries and 

launch the new ‘knowledge economy’;

(d)  declining quality of the Russian system of tertiary 
and vocational education and training, and its 
inability to cope with international standards.

A8.3.  Key institutions

The skills 2030 foresight in Russia was launched in 
2011 by two government areas:

(a)  the Ministry of Education and Science of the 

Russian Federation launched a study of demand for 
new skills and competencies in high-technology 
industries as a part of its third science and 
technology foresight;

(b)  the Strategic Initiatives Agency for the President of 
Russian Federation launched the competency 2030 
foresight as part of its initiative to create a national 
system of qualifications and competencies. This is a 
comprehensive project coordinated through the 
roadmap that involves activities in several 
ministries, including economic development, labour, 
education and science, and telecommunications 
and mass media.

The team at Moscow school of management Skolkovo 
was responsible for the execution of both projects. The 
project for the Ministry of Education and Science 
involved key high-tech sectors, including biotechnology 
(including agriculture and food industry applications), 
healthcare, ground transportation systems, aerospace, 
energy generation and transmission, information and 
telecommunications, extraction and processing of 
mineral resources, environmental protection and 
waste management. Additional sectors for the 
Strategic Initiatives Agency project involved 
construction, finance, education, government and 
public services.

A8.4. 

 Skills 2030 foresight process, methods 
and results

The skills 2030 foresight was conducted for sectors 
where technology is the primary driver of change in 
skills demand (high-tech sectors and technology-
dependent industries). The main focus of the foresight 
was to assess how key trends and new technologies 

185

Developing skills foresights, scenarios and forecastschange the nature of work tasks, shifting demand for 
existing and new skills.

The study of the demand for competencies consisted 
of (Figure A9):

(a)  general economic analysis: definition of global and 
country challenges for the future. This phase was 
conducted by systematising key international studies 
on megatrends and challenges of the future, as well 
as through a cycle of interviews with leading 
economists, analysts, and industry experts in Russia;

(b)  industry analysis: systematisation of Russian and 
international industry forecasts of scientific, 
technological and economic development. This 
stage allowed the determination of the 
technological and social factors driving industrial 
change;

(c)  formation of expert groups including 

representatives of large, small and medium 

enterprises (SMEs) in the sector (i.e. employers), 
representatives of research institutions and 
universities, and representatives of professional 
associations, regulators and advisors working with 
the sector. Employer organisations were expected 
to be listed in industry rankings (including SMEs 
that were listed as ‘most innovative’ or were 
supported by leading Russian development 
agencies) and were expected to be involved in 
collaboration with universities in the development 
of new education programmes. It has also been 
found productive that between one-third and 
one-quarter of participants are industry ‘outsiders’: 
suppliers, users of industry products, and students 
in industry-related programmes;

(d)  foresight session (see below for details);

(e)  post-session verification of reports through 

roundtables and questionnaires.

Figure A9. 

 Key steps of the skills foresight study

Source: Authors.

186

Guide to anticipating and matching skills and jobsThe core of this study was foresight sessions. These 
focused on the collaborative design of the industry 
‘map of the future’ through structured discussion 
(Figure A10):

due to changing technologies and industry context, 
and emerge due to changing industry context. 
Working tasks were identified for intellectual and 
manual labour;

(a)  analysis of key trends that drive change in the 

sector, and discussion of new ‘hard’ technologies 
(new equipment, production processes) and ‘soft’ 
technologies (new managerial methods, new 
organisational formats) resulting from these trends. 
These ‘factors of the future’ were analysed across 
three time horizons: short-term (next three years: 
2012--15), mid-term (following five years: 2015--20), 
and long-term (following decade: 2020--30). Around 
12 to 15 key trends and soft technologies, and 
around 15 to 20 new ‘hard’ technologies were 
identified during this discussion;

(b)  analysis of new market opportunities (products and 
services) and new market threats formed by trends 
and new technologies. Identification of changes in 
working tasks due to changing industry 
technologies, new opportunities and new threats 
that the industry has to deal with;

(c)  identification of working tasks that are similar to 
existing working tasks (and can be resolved with 
existing skills and knowledge), become obsolete 

(d)  for new tasks that cannot be resolved with existing 
skills and knowledge, new core competencies were 
identified; some were described through samples 
of best practices that may exist in other countries 
or in other industries, and some were described 
through requirements for new knowledge and 
skills. Between eight and 12 new competencies 
related to technological change were identified in 
every industry. Also, between three and eight 
competencies in every industry were either 
cross-sectoral or meta-competencies, which 
allowed for some of the study generalisations (see 
below);

(e)  accomplished during verification sessions: 

participants also evaluated whether competencies 
will be widespread (most industry workers are 
supposed to master them) or narrow (only a few 
specialists would be required). Participants also 
discussed how these competencies can be formed 
(for example, change in education programmes, use 
of simulators, on-the-job training).

187

Developing skills foresights, scenarios and forecastsFigure A10. 

 General methodology of foresight sessions

Source: Authors.

188

Guide to anticipating and matching skills and jobsResults of the skills 2030 foresight

Apart from new competency maps for key high-tech 
and technology-driven sectors (which cannot be 
presented here for reasons of confidentiality), the 
study has also produced two generalisations: key 
competencies will be required in all technology-driven 
sectors; and key managerial competencies required in 
high-tech sectors will become new drivers of economic 
growth.

Three factors that influence all technology-driven 
sectors are (Figure A11):

(a)  rapidly increasing competition (sometimes dubbed 
‘hypercompetition’). This trend leads to increasing 
innovativeness (seen as the key competitive 
advantage) and thus a higher rate of technological 
renewal (calling for flexible production systems), 
and also to increased control over consumer and 
supplier markets (direct economic power as well as 
indirect influence through sophisticated marketing 
and supplier network management);

(b)  increasing (international) cooperation in research 
and development (R&D) and production. Despite 
increased competition, industry leaders also often 
collaborate to share investment resources, human 
capabilities and knowledge pools (thus the 
phenomenon of ‘collaborative competition’, dubbed 
‘coopetition’). Industrial innovation and rapid growth 
often occurs on the borders of disciplines or 
industries, dissolving ‘traditional’ knowledge and 
production domains;

(c)  digitalisation/automation: ICT penetrates all 

industries and domains of human activities, changing 
the nature of manual and intellectual labour. All 
routine operations, whether manual or intellectual, 
are gradually automated, and workers shift from 
actually doing their work to programming devices to 
do their work. The proportion of non-routine activities 
(creative work, especially in teams, and related 
communication and management processes) 
increases and relevant competencies become 
mandatory for workers of the future.

189

Developing skills foresights, scenarios and forecastsFigure A11. 

 Key factors changing working tasks in all technology-driven sectors of the Russian 
economy

Source: Authors.

190

Guide to anticipating and matching skills and jobsKey competencies of the ‘worker of the future’ 
resulting from these trends are presented in Table A12, 
divided into technical competencies, competencies for 

working in a global context, cross-professional 
competencies and meta-competencies.

Table A12. 

 Key competencies of the Russian worker of the future

Key competency areas

Key competencies

Technical skills and knowledge

Skills and knowledge for global 
contexts

Multidisciplinary background: from T-shaped to M-shaped workers 
Technical + economic background

Foreign languages
Cross-cultural competencies
Knowledge of global practice domain (for example, standards, design 
environments, etc.)

Cross-professional competencies

Creative collaboration (co-creation) in project teams 
Communication skills
Working with large volumes of information

Meta-competencies

Source: Authors.

System thinking
‘Programmer thinking’ (working with programmable environments)
Rapid learning and re-learning (adaptation to various working contexts)
Self-development skills
Self-regulation skills

Another generalisation concerns the key skills and 
knowledge required for the creation and development 
of new high-tech sectors in Russia, such as ICT, 
biotech, smart grids, and smart transportation. In every 
industry considered, four competency types have been 
identified, in addition to industry-specific technology 
specialists. These competency types are combinations 

of key competencies required to launch and maintain a 
knowledge-based economy, including design, 
production, transfer of technologies, integration of new 
activities, and adaptation and standardisation of 
products (see Table A13). All of these competencies 
require a combination of technological and managerial 
education, training and experience.

191

Developing skills foresights, scenarios and forecastsTable A13. 

 Four competency types for knowledge-based sectors of the Russian economy

New skill cluster

Work tasks

Key competencies

‘Integrators’ (including technological 
entrepreneurs)

End-to-end organisation of innovative 
production from an idea to a product 
on the market

Business management and 
technology (understanding innovative 
activities across the product life cycle)

‘Translators’ (cross-discipline/inter-
industry)

Cross-discipline/cross-industry 
transfer of technologies (markets for 
new products in mature industries)

Understanding technologies and 
processes in at least two industries
Marketing competencies

‘Adaptors’

‘Standardisers’

Source: Authors.

Product adaptation to customer 
demands; design (including interface 
design and usability)

Requirements management
Client behaviour patterns
Design competencies

Development of industry standards 
(with regulators or through self-
regulation)

Management (negotiations, lobbying, 
promotion) and basic technology

These competency types are required at different 
stages of the industry life cycle, as the industry 
environment moves from early growth into more 
mature stages. During the early stages, integrative 
competencies (to help launch new enterprises, 
stimulate business and the regulatory environment) are 
required, then industry is able to work with 

evolutionary innovations (that involve minor and gradual 
product improvements); during the later stages, 
industry leaders, through their innovative ecosystems, 
are able to launch and maintain disruptive innovations 
(that require the collective efforts of supplier and 
distributor networks, often of hundreds and thousands 
of independent enterprises).

192

Guide to anticipating and matching skills and jobsFigure A12. 

 Emergence of new competency type in knowledge-based sectors of the Russian 
economy

Source: Authors.

A8.5. 

 Lessons learned and next steps

Summarising these findings, our team concluded that:

(a)  a substantial share of new competencies in 

technology-driven sectors are cross-professional or 
meta-competencies that go beyond the demand for 
competencies in specific sectors. Multidiscipline 
ability is among the key competitive advantages of 
the ‘worker of the future’;

(b)  for knowledge-based sectors, an ‘ecosystem’ 

approach is most productive: creation/preparation of 

teams with inter-dependent competencies that 
allow inventing, designing, marketing, producing 
and supporting new products;

(c)  future-oriented approach to skills and knowledge 

demand is necessary for the development of new 
industries. However, few employers are ready to 
discuss their future needs. The key problem here is 
that most industries are lacking ‘translators’ that 
could help connect strategic planning, technological 
development, and human resource preparation in 
the industry domain.

193

Developing skills foresights, scenarios and forecastsProject development

The skills 2030 foresight results have been 
communicated to the Ministry of Education and 
Science, the Agency of Strategic Initiatives, and a 
number of innovative forums, where its results were 
evaluated. However, the priority of our team is to have 
tangible results with observable social and industry 
impact. Therefore, the foresight process is planned to 
continue to the end of 2013 and beyond. Among key 
scheduled activities are:

(a)  ‘grounding’ of key foresight findings in the context 

of specific industries to revise education 
programmes for industry-related universities and 
vocational training institutions. Since late 2012, this 
work has begun for the aerospace sector in 
collaboration with United Aircraft Company, the 

largest aircraft producer in Russia. Other sectors 
are anticipated to follow through spring and 
summer 2013, including the transportation industry, 
and the nanotech/new materials industry;

(b)  creation of the career guidance tools (including 
Navigator through 100 jobs of the future and 30 
jobs that will disappear in the next 15 years) for the 
Russian public (youth and their parents), to be 
published summer 2013;

(c)  creation of a short-term/mid-term and long-term 

demand anticipation tool in collaboration with the 
Ministry of Labour (as part of the roadmap for a 
national system of qualifications and 
competencies), to be implemented in pilot regions 
of Russia through 2014.

194

Guide to anticipating and matching skills and jobsACRONYMS

ICT

SMEs

information and communications technology

small and medium enterprises

195

Developing skills foresights, scenarios and forecastsANNEX 9.  
Australia

A9.1.  Policy context

Australia is one of the few developed countries which 
have weathered the effects of the global financial crisis 
well, and its economy has remained in a strong 
position. This is primarily due to an uninterrupted 
resources boom, accompanied by significant capital 
investment in the mining-related sectors of resource-
rich states. At the same time, trade-exposed sectors, 
such as manufacturing, tourism and education have 
been negatively affected by the increasing terms of 
trade. This has created a ‘patchwork’ economy, as 
those states where the proportion of these sectors is 
higher in the economy are growing at a slower speed. 

A9.2.  Main challenges and policy objectives

Skill and labour shortages have been prevalent features 
of the Australian labour market for a number of years. 
While shortages in trade-exposed sectors have eased 
due to recent changes, they prevail and are seen as 
constraining growth in other sectors. The labour market 
also faces a number of challenges in the long term, 
such as the ageing population and slowing labour 
productivity growth. Addressing these issues through 
the creation of a high-skilled, world-class labour force 
has been the primary focus of government workforce 
development policies. Training policy responses are 
being formulated to aid structural adjustment of the 
labour market.

The top issues for the future of work and jobs in 
Australia were expressed in key questions at the 

October 2012 conference ‘The future of work – 
Developing Australia’s workforce in a global economy’ 
organised by leading institutions in the field, the 
Australian workforce and productivity agency and 
industry skills councils. Some of the questions 
explored at the conference were: 

(a)  what does the state of the nation in 2025 look like? 

(b)  what are the major changes looming that will affect 

work and productivity? 

(c)  what will be the impacts of the ‘Asian century’, for 

the economy, for jobs, for learning/skills 
development? 

(d)  what are the implications for the tertiary sector of 

meeting the skill needs of employers and 
individuals? 

(e)  are improvements in labour productivity the key to 

continued prosperity? 

(f)  what are the implications for various industry 

sectors of all this? 

(g)  how will the fast pace of change in information and 
communications technology (ICT) and social media/
social networking change the way we work? 

(h)  what is the impact of technology? 

The event promoted dialogue, raising the profile of the 
challenges and solutions around developing the future 
of Australia’s workforce in a global economy.

196

Guide to anticipating and matching skills and jobsA9.3. 

 Key institutions, processes,  
methods and results

The sectoral approach has been a key feature of 
vocational education and training in Australia. The key 
institutions in the sectoral skills development 
framework are the national and state/territory level 
industry skills bodies. 

A9.3.1. 

 Australian workforce and productivity 
agency (AWPA)

The Australian workforce and productivity agency was 
established in 2012 to support the formulation of 
workforce development policy and advice, and direct 
skills funding to industry needs. The AWPA’s key 
objectives are to sustain economic growth, avoid future 
skills shortages and raise productivity by increasing and 
deepening the skills of Australia’s workforce. To achieve 
these objectives the AWPA engages directly with 
industry on workforce development issues and to 
address sectoral and regional industry needs. 

The AWPA’s membership provides balanced 
representation of industry, employees and employers, 
and includes experience from academia, training 
provision or education, economics, industry and 
employee representatives.

The agency has replaced Skills Australia, with 
expanded roles and functions. As an independent 
statutory body, Skills Australia’s main role was to 
advise the Minister for Education, Employment and 
Workplace Relations on Australia’s current, emerging 
and future workforce skills and development needs. 
Building on this remit, the AWPA is responsible for: 

(a)  skills and workforce research, including into the 
quality of jobs and future working life in Australia;

(b)  providing independent advice on sectoral and 

regional skills needs to support workforce planning;

(c)  driving engagement between industry, training 

providers and government on workforce 
development, apprenticeships and technical and 
vocational education and training (TVET) reform;

(d)  administering the new national workforce 

development fund, and prioritising industry sectors, 
regions, and groups for funding;

(e)  developing sectoral skills and workforce 

development plans in conjunction with industry 
skills councils and industry;

(f)  promoting workforce productivity.

A key addition to the functions of the AWPA is the 
administration of the newly created national workforce 
development fund. The fund is an Australian 
government programme that supports the training of 
existing and new workers in areas of identified 
business and workforce development need. Industry 
skills councils (ISCs) participate in the administration of 
the fund, as they assist businesses with identifying 
their training needs, selecting a registered training 
organisation, and monitoring the implementation of 
projects. 

The AWPA provides research in a number of key 
workforce development areas. They prepare the 
specialised and skilled occupations lists, which provide 
the basis of the skilled migration programme. They also 
undertake work on skill needs and workforce 
development in specific sectors, including in the 
resources sector, the defence industry and green skills. 

Most importantly, the AWPA has established a rigorous 
process to formulate a national workforce development 
strategy in 2010, with the second, updated national 
workforce development strategy released in 2013. The 
process involves three key stages of research and 
analysis. 

197

Developing skills foresights, scenarios and forecastsIn the first phase, a number of scenarios are developed 
to identify what factors are likely to drive demand and 
supply for skills in the Australian labour market to 2025. 
The scenario approach acknowledges the limitations of 
economic modelling methods, particularly in accurately 
forecasting over the long term. The scenarios provide 
plausible alternative futures for the Australian economy 
through which common outcomes and key 
uncertainties and risks can be identified, so strategies 
that take account of uncertainty can be developed. 

The scenarios take into account: social, demographic 
and cultural trends; economic and financial trends and 
globalisation; labour force, industrial and workplace 
trends; science, technology and innovation trends; 
governance and public policy; and sustainability. They 
have been developed through extensive consultation 
using forums, expert interviews, stakeholder 
workshops, discussion papers and broadcasting 
information through a webinar. For the first strategy in 
2010, over 400 people attended roundtables and 
industry briefings, and Skills Australia received 45 
written submissions.

The second phase involves economic modelling to 
draw out the skill implications of the various scenarios. 
The AWPA has commissioned a private consultancy to 
translate the scenarios into assumptions about key 
macroeconomic variables. A dynamic macro model of 
the Australian economy is used to forecast the balance 
between the demand and supply of qualifications 
under the different scenarios. The model estimates 
employment growth by occupation, replacement 
demand, and new job openings. Based on the current 
qualifications profile, it also projects skills deepening 
and skills broadening to forecast the implied demand 
for qualifications. The supply of qualifications has been 
modelled as additional qualifications resulting from 
domestic student qualification completions, 
considering current qualifications completion profiles 

and demographic projections; it also includes the 
contribution to qualifications from net migration 
(Deloitte Access Economics, 2012). Detailed 
projections on both the demand and supply side have 
been produced across a range of occupations, 
industries, years, qualification levels and regions. 

The third phase includes the development of 
additional information and analysis for each industry, 
based on the scenarios and the modelling. These 
industry snapshots include analysis of industry-specific 
data from sources like the Australian Bureau of 
Statistics and the Department of Education, 
Employment and Workplace Relations. They also use 
the ‘environmental scans’ and other information 
provided by the ISCs.

This approach seems effective in combining qualitative 
and quantitative intelligence from a broad range of 
sources, using sophisticated economic modelling and a 
rigorous analytical process.

A9.3.2. 

Industry skills councils

ISCs bring together industry, educators and 
governments to create a common industry-led agenda 
for skills and workforce development at the national 
level. Their key role is to represent industry in the 
management and planning of vocational education and 
training and in the development of training products, as 
well as providing advice to Australian, state and 
territory governments about industry skills needs.

The formal roles of ISCs include:

(a)  collecting information on industry training needs 

from employers, unions and professional industry 
associations;

(b)  providing industry intelligence and advice on current 
and future workforce development and skills needs 
to the AWPA, government and enterprises; 

198

Guide to anticipating and matching skills and jobs(c)  supporting the development, implementation and 

continuous improvement of high-quality training and 
workforce development products and services;

(d)  primary responsibility for the development and 
maintenance of training packages (competency 
standards);

(e)  providing independent skills and training advice to 
enterprises, including matching identified training 
needs with appropriate training solutions;

(f)  together with the AWPA, coordination of the 

national workforce development fund. 

ISCs do not provide training or assessment services, or 
work with individual companies, but focus on 
strategies that serve the needs of the wider industry.

The network of ISCs was created in 2004 by 
significantly rationalising the former system of national 
industry training boards. A total of 11 ISCs have been 
established to align with the main areas of the real 
economy. They are independent, not-for-profit 
companies, governed by industry-led boards. They are 
bipartite in ownership and through the membership of 
their boards, but they are not required to have equal 
representation. ISCs are funded by the Australian 
Government through base and project funding. The 
amount of funding to each ISC varies in line with the 
number and size of sectors, enterprises, workforce and 
occupations it covers.

ISCs provide key labour market intelligence to the 
AWPA and other stakeholders through the annual 
‘environmental scans’ of their respective industries. 
The information in these scans is unique, as it is based 
on real-time industry views and evidence from across 
Australia. A common feature of them is that ISCs use 
various surveys and consultation to collect information 

about the drivers of workforce development and the 
perceived effectiveness of Australia’s training system. 
ISCs also use various methodologies to identify current 
and future skills needs in quantitative and qualitative 
terms, and to validate the information gathered from 
industry (87). Some use high-quality macroeconomic 
models through contracted professional agencies (88). 
Others rely more on in-house analysis of historical data 
and projections prepared by the Australian Bureau of 
Statistics, the Department of Education, Employment 
and Workplace Relations and other central agencies. 

Besides providing intelligence about industry skill 
needs to stakeholders, environmental scans form the 
basis for developing training packages.

ISCs work as a network, mirroring the 
interrelationships between industries and the supply 
chain nature of the economy. From time to time, they 
also conduct joint work to provide consolidated, formal 
advice to government on specific issues that affect skill 
needs across sectors (ISC, 2009; 2011).

A9.3.3. 

 State/territory industry training  
advisory boards (ITABs)

The Australian skills development framework operates 
across two-tiers of governance. State and territory 
governments have control over education policy, and 
encompass training authorities that administer 
vocational education and training in their jurisdictions. 
These agencies are responsible for allocating funds, 
registering training organisations and accrediting 
courses; they also have a key role in the development 
and endorsement of training packages. The state 
training authorities are accountable to their minister, 
who is a member of the Council of Australian 
Governments, standing council on tertiary education, 
skills and employment.

(87) For example, see http://tlisc.org.au/wp-content/themes/tlisc/downloads/escan_flyer.pdf
(88) For example, Service skills Australia commissioned detailed Labour and skills forecasts for the service industries. http://www.serviceskills.

com.au/labour-and-skills-forecasts

199

Developing skills foresights, scenarios and forecastsStates and territories also have their own industry-led 
sectoral skills development networks (89). These are 
state-funded, with funding tied to the provision of 
services to support critical areas of TVET. Industry 
training advisory boards or councils are autonomous, 
industry-led bodies that aim to identify and prioritise 
their industry’s skills and workforce development 
needs. Their general role is to ensure the availability 
of suitable training and skills development 
arrangements in vocational education and training. In 
addition to representing industry’s skills needs and 
providing advice to stakeholders, ITABs assist in the 
development of training products, develop training and 
career information resources, promote the benefits 
and opportunities of vocational education and training 
to their industries, and provide support for training 
providers.

Similar to the environmental scan of the ISCs, most 
ITABs produce annual change drivers reports that 
provide qualitative and quantitative information on 
current and future skills needs in their sectors, and 
identify training solutions. Besides surveying industry 
to collect first-hand information, the reports use various 
methodologies and data sets to provide an analysis of 
and a broader context for industry skill needs.

The functions of ITABs significantly overlap with those 
of the ISCs. They seem better able to capture industry 
needs at state-regional level, while ISCs focus more on 
issues affecting the overall strategic development of 
their sectors and the national training system. ITABs 

often work together with their national industry skills 
council counterparts. For example, their intelligence 
feeds into discussions on training, skills and workforce 
development issues impacting the industry’s growth in 
the national context. Areas of collaboration include 
attraction of people into the industry, particularly in 
terms of improving its image; building workforce 
capability and engagement; and improving the quality 
of training delivery in thin markets.

A9.4.  Lessons learned

Australia has a well-developed and effective system of 
anticipation of future skills needs. It is based on a 
combination of qualitative and quantitative approaches. 
The sectoral doorway has been a key feature of 
vocational education and training generally, as well as 
in processes linked to future skill needs. 

The main tool used for designing possible futures is 
scenarios development. Many actors, stakeholders and 
experts from industry, state training boards, the 
education and training sector, intermediaries and 
enterprises participate in this; such collaboration offers 
valuable insights into the future. The collaborative 
approach is one of the typical characteristics of the 
foresight exercise, as are consensus on shared visions 
and commitment to the results. The fact that 
administration of the process comes from the 
government guarantees that findings will be used to 
improve the education and training packages for the 
benefit of the future labour force.

(89) Various arrangements exist across the states and territories. The numbers of state-level ITABs are: ACT (2), Northern Territory (6), Western 

 Australia (10), Tasmania (1+industry liaison officers), Victoria (used to have 16, they were abolished in 2012), Queensland (11), South Australia 
(9), New South Wales (11).

200

Guide to anticipating and matching skills and jobsReferences

[URLs accessed 22.5.2014]

AWPA (2010). Australian workforce futures: a national 
workforce development strategy. Skills Australia. 
Canberra: Australian Workforce and Productivity 
Agency.

AWPA (2012). Australia’s skills and workforce 
development needs. A discussion paper for the 2012 
national workforce development strategy. Canberra: 
Australian Workforce and Productivity Agency.  
http://www.awpa.gov.au/publications/documents/
Future-Focus-Australias-skills-and-workforce-
development-needs-Discussion-Paper.pdf 

AWPA (2013). Future focus: national workforce 
development strategy. Canberra: Australian 
Workforce and Productivity Agency.  
http://www.awpa.gov.au/publications/Documents/
Future%20Focus%20key%20messages.pdf 

Deloitte Access Economics (2012). Economic modelling 
of skills demand and supply. Report prepared for the 
Australian Workforce and Productivity Agency. Barton: 
Deloitte Access Economics.

ISC (2009). Environmental sustainability: an industry 
response. Industry skills councils of Australia.  
http://www.isc.org.au/pdf/FA_ISC_Sustainability_
Report_Single_LR.pdf 

ISC (2011). No more excuses: an industry response to 
the language, literacy and numeracy challenge. 
Industry skills councils of Australia.  
http://www.isc.org.au/pdf/NoMoreExcuses_FINAL%20
FINAL%20single%20page.pdf 

201

Developing skills foresights, scenarios and forecastsACRONYMS

AWPA

ISC(s)

ITABs

TVET

Australian Workforce and Productivity Agency

Industry skills council(s)

Industry training advisory boards

technical and vocational education and training

202

Guide to anticipating and matching skills and jobsANNEX 10. 
United Kingdom

A10.1. 

 Economic and social context

In the early 1990s, the research and development in 
the United Kingdom (UK) that should reinforce 
technology infrastructure and business was losing its 
drive compared to many other countries. In response, 
the newly established minister for science founded an 
office of science and technology that began to work on 
developing national science and technology foresight 
activities and culture. In 1994 a technology foresight 
programme was commissioned. It initially concentrated 
on fostering a national foresight culture and closer 
interaction between scientists, industry and 
government, as well as pragmatic issues such as 
identifying science and technology development 
opportunities and exploring how these developments 
could address key future challenges. As the 
programme reached across the largest sectors of the 
UK economy and explored various social, 
environmental, economic and policy dimensions, it was 
soon renamed the UK foresight programme. 

Progress was made towards creating a foresight 
culture; foresight activities gained considerable support 
at the government level and engaged the interest of 
participants. Government departments and agencies 
became the major sponsors of various foresight 
exercises.

Since 2008 the economic crisis has affected the 
economy of the UK along with most other developed 
countries. The competitiveness of the UK’s workforce 
seems to be threatened as its skill levels are falling 
behind other countries (Spilsbury and Campbell, 2009; 
2010). In 2008 a Commission for Employment and Skills 
was established to raise UK levels of prosperity and 
opportunity by improving employment and skills and by 
providing strategic information and guidance for the UK 
government and other stakeholders. Among other 
activities, the commission carried out a national 
strategic skills audit aiming to provide valuable insights 
into strategic skills needs; an audit for England and 
Wales has been carried out so far. As one of the 
research activities feeding into the audit, a foresight 
exercise was carried out to assess the future drivers, 

challenges and opportunities for UK skills. The example 
examined is the national strategic skills audit for 
England and particularly the 2020 horizon scanning and 
scenario project that was a source of key foresight data 
for the audit.

A10.2. 

 Main challenges and  
policy objectives

The main general objective of the national strategic 
skills audit was to provide information on the 
challenges and opportunities presented by, and 
underlying trends for the future development of the 
English workforce in relation to the country’s strategic 
goals in this area. It should provide intelligence for the 
decisions of government, employers and education and 
training providers, as well as individuals. 

The 2020 horizon scanning and scenario project used 
foresight methodologies (especially horizon scanning, 
interviews and scenario approaches) in support of its 
aims:

(a)  to identify key issues (within the UK as well as 

worldwide) that may in the long term impact the UK 
employment sector and its skill requirements;

(b)  to determine employment and skills drivers of change;

(c)  to produce a working set of scenarios for the 2020 
employment and skills landscape and use them to 
analyse the challenges and opportunities for 
government and employers.

A10.3. 

 Key institutions, processes, 
approaches, methodologies and 
results

Among the key institutions involved was the UK 
Commission for Employment and Skills (UKCES), 
which was responsible for the process. The audit built 
on five separate research undertakings: 

(a)  an initial labour market information (LMI) 

assessment drawing on a wide range of source 
materials, predominantly based on quantitative data;

203

Developing skills foresights, scenarios and forecasts(b)  25 sector skills assessment reports produced by 

each of the sector skills councils (SSCs), based on a 
mix of national data supplemented by sectoral 
surveys, interviews and other information;

(c)  six additional skills assessment reports focusing on 
emerging sectors produced by SSCs grouped into 
clusters;

(d)  three additional skills assessment reports on three 
of the emerging sectors, produced for the UKCES 
by experts;

(e)  a horizon scanning and scenario report provided by 
the St Andrews Management Institute (SAMI), 
based on foresight methods and identifying key 
issues and changes which may impact on 
employment and skills over the long term.

The scheme of the national strategic skills audit 
illustrates the position of horizon scanning among the 
other activities, as shown in Figure A13.

Figure A13. 

 Scheme of the national strategic skills audit

Source: UKCES (2010b).

204

Guide to anticipating and matching skills and jobsA10.4. 

 2020 horizon scanning and scenario 
project

The UKCES commissioned the 2020 horizon scanning 
and scenario project from the SAMI, who also 
produced the report.

In its initial phase, the project’s ‘focal question’ was 
formulated: ‘What will be the drivers and impact of 
change on the employment and skills landscape in 
England by 2020; what are the challenges and 
opportunities for government and employers?’ The 
basic project outline consisted of:

(a)  identification of key issues and changes that have 
(in the long term) the potential to influence UK 
employment and skills;

(b)  identification of the main employment and skill 

drivers;

(c)  overlaying existing economic and social scenarios 
for 2020 (the scenarios developed within the 
foresight futures vision project (2002) ‘foresight 
2020’ (90) were used) with the identified skills 
drivers resulting in a working set of scenarios for 
the employment and skills landscape;

(d)  analysis of the challenges and opportunities for 

government skills policy in relation to the developed 
scenarios;

(e)  drafting a report to contribute to the national 

strategic skills audit 2010.

Several interlinked methods were used to fulfil these 
objectives.

Figure A14. 

 The scheme of the project methods/activities

Source: Duckworth et al. (2010).

(90) The scenarios were developed for the then Department of Trade and Industry by a team from the University of Sussex (SPRU-Science and 
Technology Policy Research). Various experts from business, government and academia were involved in the scenario development, and an 
extensive review of national and global future scenarios were devised. The results have been widely used in the UK.

205

Developing skills foresights, scenarios and forecastsA10.4.1. 

Interviews

Some 21 interviews were conducted, with a main 
focus on the key question of the project: ‘What will be 
the drivers and impact of change on the employment 
and skills landscape in England by 2020; what are the 
challenges and opportunities for government and 
employers?’

Among the experts interviewed were senior members of 
the UKCES, other stakeholders cooperating with the 
Commission, and experts in economics, technology and 
society; their names were not published to maintain the 
strictly non-attributable character of the exercise. The 
main goal was to select key areas for further research: 
the results were added to scanning data and were used 
to prepare the skills overlay for the scenarios as well as 
for the optimistic and pessimistic scenarios for skills in 
2020.

A10.4.2.  Horizon scanning
A10.4.2.1.  General scanning

The purpose of the horizon scan was to identify the 
trends and drivers that may potentially affect the UK’s 
long-term employment and skills landscape and future 
skills needs. 

A comprehensive scan of global and national key 
factors was based on various published information 
sources. The factors examined were drawn principally 
from the four PEST areas (political, economic, social 
and technological factors) but also included legal, 
regulatory and environmental factors. 

Issues were considered in terms of potential impact up 
to the project horizon of 2020, and also beyond (over 
the next 25 years), to stretch the thinking about the 
future and also to provide useful material for eventual 
next development of the scenarios’ time lines. The 
activity produced 101 selected trends.

A10.4.2.2.  Prioritisation

The suggested 101 trends were reviewed in a project 
team workshop. They were sorted first according to 

the probability that they would be important to the 
world in general and then for impact on the UK skills 
landscape, and grouped into interrelated clusters. A 
four quadrant chart was created, based on these two 
dimensions. The trends with both low estimated 
importance for the world and also low impact for UK 
skills were excluded from further analyses. This 
resulted in 23 trends and drivers identified as the most 
important factors of future development. 

A10.4.3.  Scenarios
A10.4.3.1.  Selecting base scenarios

Existing scenarios for future development, created in 
the framework of the foresight futures vision 2020 
project, were chosen as a base for the next process. 
They were built on analysis of socioeconomic trends, 
taking into account possible new developments and 
changes. Two key dimensions were considered: 
orientation of social values (from an individual approach 
at one extreme to community-oriented values at the 
other); and autonomy of the national government, from 
decision-making staying firmly with national or regional 
governments at one extreme to decision-making shifted 
more to supranational bodies – for example, the 
European Union or multinational corporations – at the 
other. From the original four scenarios (based on four 
quadrants resulting from the two dimensions), three 
were selected that were considered to have greater 
impact on employment and skills in the UK. They were 
then further elaborated to reflect the global 
developments since the time when the original 
scenarios were created and to take into account 
potential recovery paths following the economic crisis. 
The scenarios are:

(a)  world markets: high individual aspirations and 

independence in a global economy sustained by 
international cooperation, internationally 
coordinated policy ensures efficiency of markets, 
minimal government, emphasis on the rights of 
individuals and high growth;

(b)  national enterprise: both individuals and 

governments seek autonomy and independence; 

206

Guide to anticipating and matching skills and jobspersonal independence and material wealth within 
a nationally-rooted cultural identity, liberalised 
national markets, aspirations towards a high degree 
of national self-reliance and security, minimal 
international cooperation and a more fragmented 
medium--low growth;

(c)  global sustainability: a caring world where 

individuals value the community and government 
implements policies for welfare and sustainability; 
aspirations towards high levels of welfare within 
communities, more equally distributed 
opportunities, emphasis on active public policy and 
international cooperation, competition within a 
regulated framework and medium--high growth.

A10.4.3.2.  Skills overlay to the base scenarios

This step sought to develop the general scenarios to 
reflect the employment and skills dimensions and 
developments represented by the drivers identified 
during the scanning process. Each of the selected 
drivers was assessed against each of the scenarios, 
with its relevance, likelihood and impact (on or within 
the scenario) being estimated. The drivers that 
represented probable trends over the next 10 years 
were overlaid with all three scenarios, while drivers 
that appeared rather uncertain were assigned to the 
scenarios in different degrees. Finally, the consistency, 
plausibility and accuracy of the scenarios were 
assessed. 

Although the broad scope of the research made it 
impossible to examine in detail the implications of each 
single driver against each scenario, or draw out the 
interdependencies between drivers, the exercise 

provided valuable relevant insight into the possible 
futures in terms of employment and skills.

A10.4.3.3.  Scenario workshop

The variants of scenarios with the analysis of the 
implications of each of the key drivers were reviewed 
at a workshop of SAMI team members, UKCES 
members and other relevant experts. The experts were 
allocated to three groups according to the three base 
scenarios. Their task was to assess the impact of the 
scenarios on the UK employment and skills landscape 
and to derive a basic view of the challenges and 
opportunities for the government and employers. 

A10.4.4.  Scenario ‘wind tunnelling’

In the final stage of the project, the technique called 
‘wind-tunnelling’ was used. Its purpose is to test 
current policies and strategies by exploring their 
possible impact in the different futures. The three 
scenarios were compared with the existing relevant 
policies and the future policy options. The process 
produced an outline of the implications for government 
strategy for future skills, and suggested potential 
challenges and opportunities. 

A10.5. 

 Lessons learned

The 2020 horizon scanning and scenario project 
identified the sectors, occupations and skills that 
should be of special attention in meeting the new 
challenges and developments of the economy and 
labour market. The resulting final report was intended 
to stimulate wider debate and appropriate action 
among all stakeholders involved in shaping a 
successful future for the UK. 

207

Developing skills foresights, scenarios and forecastsIt was one of the main inputs for the national 
strategic skills audit. Through the work of the 
UKCES, the intelligence gathered serves the UK 
government, employers, training providers and 
individuals as background information for their 
decisions. Various implications and recommendations 
have resulted for the continuation of the national 
strategic skills audit research programme in relation 
to foresight activities:

(a)  the ‘wind tunnelling’ technique can be used for 

testing further policies and strategies relevant to 
skills;

(b)  the horizon scanning activity should be extended to 
a 20-year scope in the next national strategic skills 
audit. As education is necessarily a long-term 
investment, and technologies under rapid 
development will probably have significant impact 
after 2020, the time horizon of 2030 seems 
appropriate for the next exercise;

(c)  an appropriate scenario framework is a key issue. It 

can help to inform strategic evaluation and 
implementation of policies, make them more robust 
against a range of possible futures, and reduce the 
level of risk. Complex interactions between 
technology and society mean that the 2020 
scenarios cannot be simply extended over a longer 
period. A scenario framework that includes these 
additional factors, such as the ethnographic futures 
framework, is recommended for the 2030 foresight 
horizon. The ethnographic futures framework is an 
approach that tries to characterise the future by 
addressing several broad aspects:

  (i)  define: the social values, culture, economic 

 (ii) 

systems, politics and public policy which 
shape the world around us;
relate: the social and organisational structures 
which link people, influenced by 
demographics, lifestyles, work and the 
economy, the environment, business models, 
government and education;

 (iii)  connect: the technologies used to connect 
people, places and things, including 
information technology, the media, language 
and planning;

 (iv)  create: how goods and services are produced, 

including manufacturing, energy generation, 
life sciences, material sciences;

 (v)  consume: how we acquire and use goods and 
services, such as consumer goods, energy, 
food and agriculture, housing, healthcare, 
natural resources and the environment;

(d)  a deeper and more extensive technology scan is 
needed within the next skills audit exercise. The 
2020 horizon scanning and scenario project 
concentrated on current technology trends, the 
impact of which up to 2020 is more or less 
predictable. The UK will also need an appropriately 
skilled workforce for new technologies currently in 
development, if it intends to be at the heart of 
these developments;

(e)  the addition of geopolitical factors, such as China 

potentially becoming the world’s largest economy, 
is recommended for future research.

208

Guide to anticipating and matching skills and jobsUKCES (2010b). Skills for jobs: today and tomorrow – 
the national strategic skills audit for England 2010. 
Volume 2: the evidence report. London: UK 
Commission for Employment and Skills.

UKCES. National strategic skills audit. http://www.
ukces.org.uk/ourwork/nssa 

References 

[URLs accessed 22.5.2014]

Berkhout, F.; Hertin, J. (2002). Foresight futures 
scenarios: developing and applying a participative 
strategic planning tool. University of Sussex: Greenleaf 
Publishing.

Duckworth, M. et al. (2010). Horizon scanning and 
scenario building: scenarios for skills 2020. A report for 
the National Strategic Skills Audit for England. 
Newbury: SAMI Consulting. http://www.
samiconsulting.co.uk/4UKCEShorizonscanning.pdf

European Commission (2009). Mapping foresight 
revealing how Europe and other world regions navigate 
into the future. Luxembourg: Publications Office of the 
European Union. ftp://ftp.cordis.europa.eu/pub/fp7/ssh/
docs/efmn-map¬ping-foresight.pdf

Spilsbury, M.; Campbell, M. (2009). Ambition 2020: 
world class skills and jobs for the UK. The 2009 report. 
London: UK Commission for Employment and Skills.

Spilsbury, M.; Campbell, M. (2010). Ambition 2020: 
world class skills and jobs for the UK. The 2010 report. 
London: UK Commission for Employment and Skills.

UKCES (2010a). Skills for jobs: today and tomorrow – 
The national strategic skills audit for England 2010. 
Volume 1: key findings. London: UK Commission for 
Employment and Skills.

209

Developing skills foresights, scenarios and forecastsACRONYMS

LMI

SAMI 

SSCs

UKCES

labour market information

St Andrews Management Institute

sector skills councils

UK Commission for Employment and Skills s

210

Guide to anticipating and matching skills and jobsANNEX 11.  
United States of America

A11.1. 

 Economic and social context

The United States (US) economy is the world’s largest, 
with about USD 15 trillion gross domestic product 
(GDP) (purchasing power parity (PPP)) and a population 
of 312 million. The US has still not recovered fully from 
the 2008 financial crisis and ensuing recession.

The US is one of the leading countries in occupational 
and skill needs anticipation in the world. Considerable 
efforts and resources are devoted to the composition 
of supply and demand for skilled labour and on limiting 
the extent of skill shortages within the economy. Skills 
forecasts are actively used not only by government 
officials but also career counsellors, enterprises and 
individuals.

There are many (sometimes competing) forward-
looking activities to identify trends, future opportunities 
and funding priorities in science and technology, 
developed by different institutions in various fields. The 
project Future work skills 2020, sponsored by the 
University of Phoenix in 2011, has been chosen as an 
example of foresight used for anticipation of future skill 
needs. The activity resulted in recommendations 
relevant for skills development in areas of education, 
business and government.

A11.2. 

 Main challenges and policy objectives 

The research institute of the University of Phoenix 
pursues academic research on working learners, higher 
education and industry with the aim of improving 
educational outcomes and promoting a better prepared 
workforce. In 2011 it commissioned research to 
increase understanding of the skills workers will need 
over the next decade in a technologically advanced and 
changing world. The resulting report, Future work skills 
2020 (authored by the Institute for the Future) analyses 
the key drivers that will reshape the landscape of work, 
and identifies the most crucial work skills that will be 
needed in the next 10 years. It does not seek to 
determine what the jobs of the future will be, but to 
identify the proficiencies and abilities required across 
different jobs and work settings.

A11.3. 

 Key institutions, processes, 
approaches, methodologies and results

The University of Phoenix Research Institute sponsored 
the research exercise by the Californian foresight 
organisation Institute for the Future (IFTF), an 
independent, non-profit, strategic research group that 
has pioneered foresight tools and methods for more 
than 40 years.

The research methodology was based on the following 
components.

A11.3.1.  Drawing on existing foresights 

During the work, the IFTF drew on its previous 
forecasts in areas as diverse as education, technology, 
demographics, work, and health, and their annual 
10-year forecast. The 10-year forecast is based on IFTF’s 
‘signals methodology’, which builds on aggregating 
data, expert opinion and trends research to understand 
patterns of change. A signal is understood as a typically 
small or local innovation or disruption that has the 
potential to grow in scale and geographic reach. It can 
be a new product, a new practice, a new market 
strategy, a new policy, or new technology that points to 
larger, perhaps global, implications. Signals tend to 
capture emergent phenomena sooner than traditional 
social science methods, so are very useful in 
anticipating highly uncertain futures.

Signals differ from trends as they can point to possible 
innovations before they become obvious. They also 
differ from indicators because they often cover 
marginal, but potentially game-changing, phenomena 
rather than mainstream developments. Though local 
trends and indicators can function as signals when 
they reach a certain threshold, they may indicate a 
change spreading to the larger population.

A11.3.2.  Expert workshop brainstorming 

The research was complemented by the expert 
workshop held at the IFTF’s headquarters. Experts in a 
diverse range of disciplines and from various 
professional backgrounds were brought together. They 

211

Developing skills foresights, scenarios and forecastswere engaged in brainstorming exercises to identify 
key drivers of change and how these will shape future 
skill requirements. 

A11.3.3.  Analysis and filtering of collected data

In the final stage, all of the information gathered was 
analysed and filtered. Six key drivers and 10 skills areas 
that will be most relevant to the future workforce were 
identified. 

A11.4. 

 Lessons learned

The research outlined the basic challenges that the 
future workforce will face in the form of general skill 
requirements on the labour market, but also in society 
as a whole. Individuals will have to navigate paths 
through the rapidly changing landscape of 
organisational forms and skill requirements, continually 
reassessing, developing and updating their skills. High 
adaptation capabilities, flexibility and life-long learning 
will be necessary to succeed. 

The results of the exercise are summarised in  
Figure A15.

Figure A15. 

 Six drivers and 10 skills needed in the future

Source: IFTF (2011).

212

Guide to anticipating and matching skills and jobsThe results have implications for individuals, education 
institutions, business and government. The authors of 
the study suggest several directions for each of the 
stakeholder groups to take in tackling the challenges of 
future development:

(a)  for education institutions:

  (i) 

 (ii) 

 (iii) 

 (iv) 

 (v) 

to support developing skills such as critical 
thinking, insight and analysis capabilities;
to integrate new-media literacy into education 
programmes;
to include experiential learning that 
emphasises soft skills (for example, the ability 
to collaborate, work in groups, read social 
cues, and respond adaptively);
to target their provision not only to youth, but 
also to adults;
to integrate interdisciplinary training that 
allows students to develop skills and 
knowledge in several subjects.

(b)  for enterprises:

  (i) 

 (ii) 

 (iii) 

 (iv) 

to adapt their workforce strategies to respond 
to future skill requirements;
to reconsider traditional methods for 
identifying critical skills, as well as selecting 
and developing talent;
to take into account the disruptions likely to 
reshape the future, so it is possible 
continuously to reshape the company skills 
and talents pool for the sustainability of 
business goals;
to collaborate with universities to address 
lifelong learning and skill requirements.

(c)  for government policy-makers:

 (i)  to make education a national priority to ensure a 

country’s ability to prepare people for a 
sustainable future;

 (ii)  to consider the full range of skills citizens will 
require, as well as the importance of lifelong 
learning and constant skill renewal.

References 

[URLs accessed 29.5.2014] 

Holtmannspötter, D. et al. (2010). 
Technologieprognosen. Internationaler Vergleich 2010 
[Technology forecasts. International comparison 2010]. 
Düsseldorf: VDI Technologiezentrum GmbH. http://
www.vditz.de/publikation/technologieprognosen/ 

IFTF (2011). Future work skills 2020. Palo Alto: Institute 
for the Future. http://www.iftf.org/uploads/media/
SR-1382A_UPRI_future_work_skills_sm.pdf 

OECD (2011). OECD Factbook 2011--12: economic, 
environmental and social statistics. Paris: OECD 
Publishing.

OECD (2012). OECD science, technology and industry 
outlook 2012. Paris: OECD Publishing.

Website 

[URL accessed 29.5.2014]

Institute for the Future.  
http://www.itif.org 

213

Developing skills foresights, scenarios and forecastsKEY TECHNICAL TERMS

Anticipation 

Apprenticeships

Backcasting

Cluster

Competency

Denotes various qualitative and quantitative methods aimed at identifying future skill 
needs. 

Systematic, long-term training, alternating periods at the workplace and in an 
educational institution or training centre. The apprentice is contractually linked to the 
employer and receives remuneration (a wage or allowance).The employer assumes 
responsibility for providing the trainee with training leading to a specific occupation 
(Cedefop). 

Normative method that is used in complex situations where a desirable future is 
defined and agreed, and then the method works backwards and identifies several 
actions needed to achieve a desirable output.

A cluster is understood as a network of suppliers, producers, customers and 
competitors connecting between themselves and with institutions of knowledge 
production and diffusion to build new competitive factors and new competencies and 
to increase added value.

The proven or demonstrated individual capacity to use know-how, skills, qualifications 
or knowledge in order to meet usual and changing occupation situations and 
requirements (UNESCO). 

Delphi method

This is an expert survey implemented in two or more rounds, where in the second and 
later rounds of the survey, the results of the previous round are provided as feedback.

Employment service 
provider 

Public and private employment services whose main task is to aid job matching. (See 
Volume 4.)

European Qualification 
Framework (EQF)

Reference tool for describing and comparing frameworks for lifelong qualification 
levels, developed at national, international or sectoral levels (Cedefop).

Exploratory method

Future research that attempts to identify multiple possible future settings. It starts with 
the preconditions, beliefs and social or technological possibilities which already exist.

Forecasting

Foresight studies

Horizon scanning

Job

Labour market 
information

Labour market 
information system 
(LMIS)

Matching 

214

Quantitative forecasts are statistical projections, econometric models or similar 
methods that produce information on aspects of future labour markets (supply and 
demand, skills, qualifications, etc.). Forecasts use data about the present and past to 
estimate future developments. 

Foresight studies are typically multi-disciplinary, mostly qualitative approaches based 
on interviews, discussions, focus groups and Delphi methods, to analyse present and 
future developments. The key feature of foresights is their action orientation.

A systematic examination of opportunities and likely future developments which are 
at the margins of current thinking and planning. Horizon scanning explores novel and 
unexpected issues, as well as persistent problems and trends (OECD).

A set of tasks and duties performed, or meant to be performed, by one person, 
including for an employer or in self-employment (ILO, 2012).

Any information concerning the condition, functioning and problems of the labour 
market. The information includes opportunities which may be available to it, and the 
employment-related intentions or aspirations of those who are part of it. (See Volume 1.)

A labour market information system consists of a set of institutional arrangements, 
procedures and mechanisms that are designed to produce labour market information 
(ILO, 1997). (See Volume 1).

Matching denotes approaches and actions that aim to increase the employability of the 
workforce and reduce skills shortages, including filling jobs with qualified jobseekers. 
This term is broader than job referral or placement.

Guide to anticipating and matching skills and jobsMismatch 

Normative methods

Occupation

Public Employment 
Service (PES)

Qualification

Roadmapping

Skill

Skill gap

Skills shortage

Swot analysis

Mismatch refers to situations where a person’s skillset does not meet the job 
requirements and where there is a shortage or surplus of persons with a specific skill. 
It includes different types of skill gaps and imbalances such as over-education, under-
education, over-qualification, under-qualification, over-skilling, skills shortages and 
surpluses, skills obsolescence, and so forth. Skills mismatch can be identified at the 
individual, employer, sector or economy level.

Future research that attempts to identify desirable future settings and works 
backwards to see if and how this future might be achieved, or avoided, given existing 
constraints.

An occupation is defined as a set of jobs whose main tasks and duties are 
characterised by a high degree of similarity. A person may be associated with an 
occupation through the main job currently held, a second job or a job previously held 
(ILO, 2012).

Public Employment Services provide job search assistance and placement services. 
They analyse and disseminate labour market information, and develop and implement 
targeted labour market programmes and services (ILO, 2009). (See Volume 4.)

A formal expression of the vocational or professional abilities of a worker which 
is recognised at international, national or sectoral levels (ILO). An official record 
(certificate, diploma) of achievement which recognises successful completion of 
education or training, or satisfactory performance in a test or examination.

A normative method that aims to look at the future for a chosen field and to identify 
the most important drivers of change in that field (JRC-IPTS). It provides inputs for the 
formulation of policies and strategies (UNIDO).

Skill is understood as being the ability to carry out a mental or manual activity, acquired 
through learning and practice, where skill is an overarching term which includes 
knowledge, competency and experience, as well as the ability to apply these in order to 
complete tasks and solve work-related problems.

Used as a qualitative term to describe a situation in which the level of skills of the 
employee or a group of employees is lower than that required to perform the job 
adequately, or the type of skill does not match the job requirements (Cedefop, 2010). 

Used in this guide as a quantitative term to describe a situation in which certain skills 
are in short supply, for example where the number of jobseekers with certain skills is 
insufficient to fill all available job vacancies.

Analytical tool which helps to identify main internal (strengths and weaknesses) 
and external (opportunities and threats) factors that may shape the reality of a given 
situation (now or in the future).

215

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This guide is a part of the ETF, ILO and Cedefop series of guides on skills 

anticipation and matching. All the guides follow a common structure, although 
they vary in terms of level of detail, technical content and case studies. All guides 
have gone through extensive validation and peer review; they were also 
discussed in detail in international expert seminars in which academic 
representatives, anticipation and matching experts, and potential end-users from 
all over the world provided comments and feedback on content and usability. 
Experts and staff of the three organisations also peer reviewed the guides 
before their publication. 

This volume covers the development of skills foresights, scenarios and skills 
forecasts, and aims to support setting up skills forecasting systems at national 
level by means of quantitative and/or qualitative approaches. The guide is built on 
a number of experiences and case studies in both developed and developing 
countries. It proposes a set of instruments devised to help guide new initiatives 
in this area. Adapted to specific objectives and country contexts, elements of the 
methods described can be combined. 

The guide is intended specifically for countries which are starting to develop 
systems of skill needs anticipation. It provides information for sponsors and 
implementers of skill needs anticipation initiatives, such as policy-makers, 
education and training providers, public employment services, social partners 
and research and specialist organisations.

ISBN 978-92-9157-655-5

doi:10.2816/376143