Digital

Single

Market

ICT for work:

Digital skills in the

workplace

FINAL REPORT

A study prepared for the European Commission

DG Communications Networks, Content & Technology

by:

This study was carried out for the European Commission by

Authors: Maurizio Curtarelli and Valentina Gualtieri with Maryam Shater Jannati and Vicki

Donlevy

Internal identification

Contract number: 30-CE-0676076/00-14

SMART number: 2014/0048

DISCLAIMER

By the European Commission, Directorate-General of Communications Networks,

Content & Technology.

The information and views set out in this publication are those of the author(s)

and do not necessarily reflect the official opinion of the Commission. The

Commission does not guarantee the accuracy of the data included in this study.

Neither the Commission nor any person acting on the Commission’s behalf may

be held responsible for the use which may be made of the information contained

therein.

ISBN 978-92-79-67761-8

doi:10.2759/498467

© European Union, 2016. All rights reserved. Certain parts are licensed under

conditions to the EU.

Reproduction is authorised provided the source is acknowledged.

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Content

ABSTRACT .............................................................................................................................. 3

EXECUTIVE SUMMARY ...................................................................................................... 4

CHAPTER 1. SETTING THE SCENE: THE CONTEXT OF THIS STUDY .............. 12

1.1 Definition of the digital economy .............................................................................. 12

1.2 Impacts and implications of the digital economy ............................................... 13

1.3 Digital skills and digital literacy ................................................................................ 16

1.4 Digital skills challenges ................................................................................................. 21

1.5 The digital skills challenge: policies and solutions............................................. 24

1.6 Purpose and scope of this study ............................................................................... 25

1.7 Overview of methodological approach ................................................................... 27

1.8 Structure of the report ................................................................................................. 29

CHAPTER 2. WORKPLACES’ FEATURES IN THE EUROPEAN UNION:

DESCRIBING THE SURVEY POPULATION ................................................................ 31

2.1 Profile of workplaces ...................................................................................................... 31

2.2 Profile of workforce ........................................................................................................ 37

2.3 Summary ........................................................................................................................... 41

CHAPTER 3. ICT AND DIGITAL TECHNOLOGIES FOR WORK .......................... 43

3.1 Digital technologies in European workplaces ........................................................... 43

3.2 Recent trends in the use of digital technologies ..................................................... 45

3.3 Investment strategies in ICT .......................................................................................... 49

3.4 Summary ............................................................................................................................... 59

CHAPTER 4. DIGITAL SKILLS IN EUROPEAN WORKPLACES ........................... 61

4.1 Defining digital skills in the context of this survey ................................................ 61

4.2 Digital skills for work: available evidence from other sources .......................... 62

4.3 What types of digital skills are required in European workplaces? ................. 64

4.4 Digital skills of employees in different jobs .............................................................. 69

4.5 Analysis of specific jobs .................................................................................................... 73

4.6 Summary ............................................................................................................................... 79

CHAPTER 5. THE DIGITAL SKILLS CHALLENGE IN EUROPEAN

WORKPLACES ..................................................................................................................... 81

5.1 Defining digital skills gaps ............................................................................................... 81

5.2 Digital skill gaps in European workplaces ................................................................. 82

5.3 Digital skills gaps density ................................................................................................ 83

5.4 Impacts of digital skills gaps .......................................................................................... 85

5.5 How do workplaces deal with digital skill gaps? ..................................................... 89

5.6 Barriers to initiatives tackling digital skills gaps ..................................................... 93

5.7 Summary ............................................................................................................................... 94

CHAPTER 6. CONCLUSIONS AND RECOMMENDATIONS .................................... 97

6.1 Main results and related learning points ................................................................... 97

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6.2 Learning points from the validation workshop ...................................................... 101

6.3 Recommendations ............................................................................................................ 102

ANNEX 1. SURVEY METHODOLOGY .......................................................................... 106

A1.1 The target population ................................................................................................... 106

A1.2 Sampling strategy .......................................................................................................... 108

A1.3 The sampling frames .................................................................................................... 115

A1.4 Data collection ................................................................................................................. 115

A1.5 Identification of parameters of interest and estimation phase .................... 116

A1.6 Sample profile ................................................................................................................. 119

A1.7 Data preparation and analysis .................................................................................. 122

A1.8 The survey questionnaire............................................................................................ 123

ANNEX 2. COMPLEMENTARY STATISTICAL EVIDENCE .................................... 138

ANNEX 3. BIBLIOGRAPHIC REFERENCES .............................................................. 188

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ABSTRACT

The digitisation of the economy is one of the most important drivers behind the

profound transformation of the labour market and the way people work, which is

thought likely to become even more significant in the years to come. This new paradigm

represents a major challenge for employers, workers and public authorities, and the

challenges needs to be fully understood in order to identify the most appropriate policy

options to transform them into opportunities for all. This study "ICT for Work: Digital

skills in the workplace", launched by The European Commission, DG CONNECT, and

carried out by Ecorys and Danish Technological Institute in 2015-2016 has been

conceptualised and implemented in order to examine the transformation of jobs in the

digital economy in the European Union, investigating the penetration of digital

technologies into workplaces, the digital skills required by employers and the digital

skills currently available in workplaces. In this light, an employers’ survey and

qualitative interviews on the impact of ICT on job quality have been carried out, .

Rooted in an extensive review of academic and scientific literature, existing surveys and

data from other statistical sources, this study aims to fill existing research gaps. This

report presents findings from the study.

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EXECUTIVE SUMMARY

The digitisation of the economy is one of the most important drivers behind the

profound transformation of the labour market and the way people work, with this

digitisation thought likely to become even more significant in the years to come. This

new paradigm represents a major challenge for employers, workers and public

authorities, and the challenges needs to be fully understood in order to identify the

most appropriate policy options to transform them into opportunities for all. This study

"ICT for Work: Digital skills in the workplace", launched by The European Commission,

DG CONNECT, and carried out by Ecorys and Danish Technological Institute has been

conceptualised and implemented in order to examine the transformation of jobs in the

digital economy in the European Union, investigating the penetration of digital

technologies into workplaces, the digital skills required by employers and the digital

skills currently available in workplaces. In this light, a number of research tasks aimed

at gathering primary data on digital skills and digital skill gaps have been carried out,

primarily through an employers’ survey, and through qualitative interviews on the

impact of ICT on job quality. This report focuses primarily on findings from the

employers’ survey (the ‘European Digital Skills Survey’), with these being integrated

where relevant with findings from the qualitative analysis of the impact of digital

technologies on job quality and working conditions (these are presented in a separate

report “The impact of ICT on job quality: evidence from 12 job profiles”1). Rooted in an

extensive review of academic and scientific literature, existing surveys and data from

other statistical sources, this study aims to fill existing research gaps.

The conceptual framework. While the ‘digital economy’ can be broadly defined as the

economy that is extensively based on digital computing technologies, most of the

literature on the impact of digital technologies on the world of work converges in

pointing out that the phenomenon of digitisation is actually of great complexity, and is

changing rapidly to also embrace new technological developments. The concept of

‘digitisation’ covers a wide range of different digital technologies (e.g. computers,

mobile devices, internet and the ‘Internet of Things’, robotics and automation), which

have different implications in terms of their impacts on production and work.

The impacts of digitisation on the labour market are multiple, and have a high degree of

interconnection. The digitisation of the economy is seen in most of the literature as

contributing to the polarisation of the labour market in many countries. On one

hand, digitisation is thought to have led to a significant increase over time in the

demand for high-skilled individuals, equipped with cognitive skills and technical

knowledge to deal with tasks and procedures required by the new technologies. On the

other hand, it has led to a sharp decrease in the demand for the medium-skilled and

(although to a lesser extent) low-skilled or non-skilled workers.

Some other authors see digitisation as resulting in job losses. This is due to

automation, based on the development of a combination of increasingly smart

technologies - such as robotics, numerically-controlled machines,

computerised inventory management software, speech recognition, pattern recognition,

automated language translation, self-driving vehicles, e-commerce - which are

replacing humans in doing their jobs and therefore are contributing to job destruction. 1 Ecorys and Danish Technological Institute (2016) available at https://ec.europa.eu/digital-single-market/en/news/report-shows-digital-skills-are-required-across-all-types-work-also-jobs-outside-office

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Conversely, according to other scholars, digitisation is favouring the emergence of

new occupations and creating new jobs. Digitisation is leading to a higher demand

of human workers because humans have a comparative advantage over computers

when it comes to ‘cognitive tasks’ which require thinking, improvising creative solutions

and solving unexpected problems, and to also be better than machines with tasks

requiring flexibility to adapt and interpersonal interactions. These human cognitive and

interactive abilities are complementary to the work of computers. In addition,

digitisation is generating the emergence of completely new occupations and jobs.

Digitisation is also seen to bring transformations in existing jobs, by changing (to

different degrees) work practices, the way tasks are carried out, job contents and job

requirements, and, as a consequence, the skills needed to perform the job. Digitisation

has also been shown to impact on working conditions: digital technologies are

contributing significantly to changes in the way we work, with an expected negative

impact on the work-life balance and health and safety of workers (e.g. increased stress,

decreased protection, higher risks related to working remotely), as well as their

remuneration.

While the net impact of digitisation on employment is debatable, there are national-

level studies that indicate that digital technologies may contribute to productivity

increases and economic transformation and with multiplicative effects on non-ICT

sectors, if accompanied by work process reorganisation and skill upgrading. The growth

of digitisation has therefore resulted in an increased demand of digital skills in

recent years (as shown by several studies), which is expected to continue growing due

to the increasing number of jobs requiring employees to use ICT and possess digital

skills. Digital skills are required in many jobs and have become transversal skills.

The nature of demand for digital skills is, however, influenced by the strategies firms

pursue and it is highly dependent on the sector and the market position of the firm.

Given the recognised importance of digital skills for an effective implementation of ICT

in the workplace, the main challenge for employers is to make sure that the supply of

digital skills within the workforce matches its demand at the labour market and

enterprise level, in terms of both required type and level of skills. In other terms, the

digital skill challenge for businesses is twofold. It can refer either to the lack of workers

with the needed skills in the labour market (skills shortages), and/or to the presence of

overskilled or underskilled workers at company level (skills mismatches and skills gaps).

Challenges can therefore be external to the firm (as in the case of shortages) or internal

(as in the case of mismatches or gaps). In terms of impact, the adverse effects of

digital skills gaps (or digital underskilling of employees) are evidenced across

aggregated, company and individual levels. Of particular relevance is the company

level, where skills gaps cause a loss in terms of productivity, increasing labour costs,

slowing growth and adaptation of new technologies down.

Purpose and scope of this study. In this context, the study on ‘ICT for work: Digital

skills in the workplace’ has been conceptualised and implemented in order to examine

the transformation of jobs in the digital economy in the European Union, investigating

the penetration of digital technologies into workplaces, the digital skills required by

employers and the digital skills currently available in workplaces. Overall, the evidence

gathered corroborates existing research demonstrating that digital technologies are

becoming increasingly widespread across a wide range of workplaces, also in economic

sectors not traditionally related to digitisation. The study also confirms (as observed in

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existing research) that digitisation is resulting in an increasing demand for digital skills

across different types of occupations and jobs in a range of industries, and that

employers are encountered with a shortfall in the availability of appropriate digital

skills.

European workplaces and survey population. The European Digital Skills Survey

was carried out among a representative sample of 7,800 workplaces in six EU member

states (Germany, Finland, United Kingdom, Portugal, Sweden and Slovakia), which are

statistically representative of 4,295,345 workplaces in the six countries as a whole, and

of 13,803,113 workplaces in the whole Europe Union (EU28). Such workplaces operate

in 12 economic sectors with different levels of digital intensity: agriculture;

manufacturing; electricity and gas supply; construction; wholesale and retail trade,

repair of motor vehicles and motorcycles; transportation and storage; accommodation

and food service activities; information and communication; professional, scientific and

technical activities; administrative and support service activities; education; human

health and social work activities. The vast majority of the workplaces represented

employ between two to nine employees and therefore fall in the category of micro-sized

workplaces (more than 80%), with the remaining being mainly small enterprises

(16%); only 0.5% are large enterprises. Workplaces belong mainly to the private sector

(90%) and are not part of a group of companies (85%). The main reference market for

these workplaces is the local or regional market (65%), while a more limited proportion

of workplaces operate or trade at national level (about 21%) and only 13% at

international level.

The 13,803,113 European workplaces (EU28) employ a total of 150,563,540

employees. Overall around 39% of employees are women, 20% are older than 50

years of age and 19% are younger than 30 years of age. Over a quarter of employees

(27%) hold a university degree. Employees are classified in nine occupational

categories identified by the ISCO 1-digit code: managers; professionals; technicians

and associate professionals; clerical support workers; sales, customer or personal

service workers; skilled agricultural, forestry and fishery workers; building, craft and

related trade workers; plant and machine operators and assemblers; and elementary

occupations. Of the 150,563,540 total employees in EU28 workplaces, 7.5 million (or

5%) work as managers. Workers employed as professionals in the workplaces covered

by this survey number 28.5 million (or 19%). Technicians number 25.5 million (17% of

total employees), sales workers number 26 million (17%), clerical workers account for

17 million (11%), and skilled agricultural workers total 1.5 million (1% of employees).

Finally, the survey identified about 17 million building workers (11%), 12 million plant

machine operators (8%) and 15.5 million workers employed in elementary occupations

(10%).

ICT and digital technologies for work. In this context, the survey results show that

digital technologies are widely used by workplaces in the European Union. The vast

majority of European workplaces use desktop computers (93%), broadband technology

to access the internet (94%), portable computers (75%) and other portable devices

(63%). Much smaller proportions of workplaces use an intranet platform (22%), CNC

machine or tools (8%) or programmable robots (5%). Specific sector-based trends,

with the use of certain technologies concentrated in specific sectors can be observed, as

for example the use of CNC machine and robots in the agriculture and manufacturing

sectors and the use of laptops in the information and communication or education and

human health sectors. Large-sized workplaces report the highest use of all the digital

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technologies listed. The use of ICT in workplaces has increased significantly in the last

five years at EU level with less than 10% of workplaces reporting that there has been

no increase. In this period, micro-sized workplaces were more likely to report no

increase or a limited increase in the use of ICT compared to workplaces of a larger size.

Generally, about 69% of workplaces anticipate that the use of digital technology will

increase in the next five year, while 25% expect no increase and around 7% have no

specific expectation. The current level of use of digital devices seems to have been

supported by specific investment strategies over the last five years, mainly those aimed

at introducing ICTs to improve either the overall efficiency or the business volume of

workplaces. However, investments in ICTs appear to be less common among micro-

sized workplaces, which report more frequently than other workplaces a total lack of

investment in ICT in the recent past. At sectoral level, recent investments in ICTs seem

to be more frequent among workplaces in sectors with traditionally low levels of digital

intensity (e.g. agriculture, manufacturing or construction), most probably in light of

recent changes in the production strategy pursuing higher efficiency, but also in sectors

with higher levels of digital intensity (e.g. information and communication sector).

Digital skills in European workplaces. In the European Union the proportion of

workplaces requiring their employees to possess digital skills varies greatly according to

the type of job and the type of digital skills. The demand for digital skills is clearly

related to the job role of the worker, and the evidence gathered through the European

Digital Skills Survey indicates that in some job categories more than 90% of jobs

require specific types of digital skills. Basic digital skills are the most commonly required

in all the occupations. However, the evidence indicates that this requirement is

particularly the case for high and medium-skilled jobs. Almost all workplaces require

their managers to possess basic digital skills and around 90% of employers state that

professionals, technicians, clerical workers or skilled agricultural workers are required to

possess at least basic digital skills. Eight out of ten workplaces require basic digital

skills for sales workers. Although in much smaller proportions, workplaces also often

require basic digital skills for building workers (almost half of workplaces), plant

machine operators (34% of workplaces) and even employees in elementary occupations

(27% of workplaces). Advanced digital skills are much less required by employers. It is

mostly professionals (54% of workplaces), technicians (52%) and to a lesser extent

clerical workers (45%), managers and building workers (31% of workplaces in both

cases) who are required to have this type of digital skills, while they are considered

much less important for all other occupations. Specialist digital skills are required

mostly for workers employed as professionals and technicians (43% and 44%

respectively), and to a lesser extent as managers (33% of workplaces). Advanced and

specialist digital skills are very much related to specific sectors (in particular

manufacturing and information and communication) and are more likely to be required

in larger workplaces. There are a very high proportion of workplaces which do not

consider digital skills to be important at all for several medium, low or non-skilled

occupations. The proportion of employees equipped with the required digital skills

broadly reflects the level of importance attached by the employers to the specific types

of digital skills in the different job categories.

The digital skills challenge in European workplaces. Nevertheless, 15% of

workplaces report the existence of digital skill gaps in their workforce, indicating that a

proportion of their employees are not fully proficient in carrying out tasks involving the

use of digital technologies. Large workplaces, and workplaces in the manufacturing or

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construction sectors, are more likely to report digital skill gaps. Overall, the density of

the digital skills gap varies greatly according to the type of digital skills in relation to the

different occupations. Larger digital skills gaps are more likely to be found in the high-

skilled (managers, technicians) and in medium-skilled (clerical workers, sales workers)

occupations, and to a lesser extent in the low-skilled occupations, with the exception of

workers in elementary occupations. Skills gaps related to basic digital skills are more

concentrated among technicians (22%), elementary occupations (21%), sales workers

(20%) and clerical workers (17%). Skill gaps related to advanced digital skills are more

concentrated among sales workers (18%), technicians (17%), plant machine operators

(17%), clerical workers (16%) and elementary occupations (15%). Skills gaps related

to specialist digital skills are more concentrated among sales workers (23%), followed

by elementary occupations (18%) and technicians (16%).

Most workplaces (62%) that report an issue of digital skills gaps do not consider that

existing digital skills gaps have an impact on workplace performance, while more than

one third of workplaces with digital skills gaps express concern about the impact that

gaps could have on the workplace performance (36%). About half of those expressing

concern about the impact on workplace performance expect a major impact (18%) and

the other half a minor impact (19%). Micro-sized and, to a lesser extent, large

workplaces, and workplaces in the manufacturing and construction sectors are among

those more likely to consider that digital skills gaps are not impacting on performance.

The type of impact most frequently reported is a loss of productivity (46%) followed by

an expected decrease in the number of customers (43%), another (unspecified)

negative impact (41%) and a decrease in the number of contracts (32%). Awareness of

the existence of digital skills gaps is frequently not accompanied by initiatives

undertaken to address the issue: 77% of workplaces reporting digital skills gaps have

not undertaken any actions, while only 12% have done so, and 11% plan to. Micro-

sized workplaces have been least active in this respect, with only 9% having taken

action to tackle digital skill gaps and 81% having not undertaken any actions at all.

Overall, training (both in the form of on-the-job training and development programmes

and external training) appears to be the most common action undertaken to tackle the

digital skills gaps, while changes to work organisation and the hiring of new staff appear

to be much less common.

Excessive cost seems to be the main barrier encountered when undertaking actions to

deal with digital skills gaps. Micro-sized workplaces are the most likely to report the

excessive cost of most of the available options. Only limited proportions of large

workplaces encounter difficulties when taking action to tackle the digital skill gaps, with

the exception of digital skill shortages in the overall labour market, which is reported by

37% of large-sized workplaces.

Conclusions and recommendations. The evidence shows that digital technologies

are increasingly and extensively used across the economy. However, digital skills

appear to be currently required mostly for the high-skilled and, to a lesser extent,

medium-skilled employees to perform their job tasks, and are less likely to be required

for the low-skilled or the unskilled (or frequently not required at all, even at basic

level). These polarising trends, confirmed also by other available evidence, draws

attention to the fact that a high share of workers in low-skilled occupations whichdo not

require (or require to a very limited extent) digital skills. This dichotomy risks widening

the digital divide, leaving a proportion of workers lagging behind and at risk of digital

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exclusion, who would hence benefit from specific attention. Another finding regards the

availability of digital skills, which is not always sufficient to meet employers’ needs, as

demonstrated by the reported existence of digital skills gaps in the workforce, even as

regards basic digital skills. Different factors contribute to this situation. The speed at

which workers are being provided with the right digital skills in the right locations is

frequently slower than the speed at which digital technologies are evolving. As a result,

digital skills are often also more subject to obsolescence. An age-related issue can also

be identified, as older workers are less likely to be equipped with digital skills than

younger workers. Results show as well that even if workplaces report that a proportion

of their workforce is not fully proficient in carrying out tasks involving the use of digital

technologies, they often do not recognise that existing in-house skills gaps impact on

workplace performance and hence often do not take action to deal with the issue.

Another important result regards the relationship between workplace size and access to

digital technologies. For micro and small-sized workplaces, it may not be viable to

invest in order to increase ICT use. Also, for those micro and small-sized employers

who have a high demand for digital skills, simply allocating staff time to acquire them is

both difficult (loss of productive time), and expensive (training and development

programmes need to be brought in). This is less an issue for bigger employers with

more available resources who can manage capacity, develop training programmes or

buy them in. But it is also important to remember that some micro or small-sized

companies consider that they do not need ICT at all, and therefore do not demand

digital skills.

Finally, the skills challenges appear highly dispersed, as different sectors have different

demands, and the balance of supply and demand is different across Member States.

The sectoral analysis indicates that the use of digital technologies is uneven across

economic sectors, particularly concerning the types of digital technologies, their speed

of penetration and also the related demand for digital skills, with some sectors clearly

leading the ‘digital revolution’ and some others following at a slower pace.

The recommendation formulated at the end of the study can be summarised as follows.

1. Raise awareness on digital technologies and the need for digital skills

Awareness-raising campaigns, should be implemented to raise awareness both of the

importance of the use of digital technologies to support and improve business

performance, productivity and internal organisation, and of the need for digital skills in

relation to new digital technologies.

2. Promote access to digital technologies

Many companies, particularly micro and small-sized ones, are not fully aware of the

importance of investing in digital technologies, and often do not have the financial

capacity to do so. Mechanisms (loans, grants etc.) should be used to enhance and

support access to digital technologies, particularly for micro and small-sized companies.

3. Expand the availability of digital skills through the education and training

system

The education and training sector should be supported to develop and adapt its offer to

meet the changing needs of the digital economy. Programmes at all levels and sectors

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of education should be updated and digital skills should be part of the core competences

required at every level.

4. Promote access to training

Access to training to address digital skills gaps in the existing workforce should be

supported through a variety of means. Information about existing training initiatives

and procedures to access them should be made available to employers through their

professional or sectoral organisations and associations, or through governmental

channels.

5. Build multi-stakeholder partnerships based also on effective social dialogue

to increase the availability of digital skills

Policymakers should support digital skills development within multi-stakeholder

partnerships. Partnerships are proven to generate a more inclusive and targeted

approach to skills development and training provision that is more responsive to labour

market needs, in line with vocational programmes and qualifications. A digital skills

strategy should therefore be the result of a discussion based on effective social

dialogue.

6. Provide access to funding for digital technologies and digital skills

development

Funding is critical to enhance the availability of digital skills in the current workforce.

Employers could benefit from access to funds (including EU funds) to support more

investment in digital technologies and the development of digital skills, especially for

initiatives that are cross-border and which share experiences in the generation and use

of digital skills. Better access to funding should also be provided to social partners.

7. Include digital skills in a wider skills strategy

Although it remains crucial to develop a range of specific digital skills which respond to

the needs of the digital economy, wider digital skills for the whole population should be

embedded in a broader and comprehensive skills strategy in which other transversal

skills relevant to employers such as soft skills and communication skills are also

included. Evidence shows that the most effective means of improving employability and

closing skills gaps are more generic measures aimed at improving the capacity of

workers to acquire new skills and learn in an evolving economy.

8. Consider diversity and avoid the ‘one-size fits all’ approach

Employers require different types and levels of digital skills according to the sector in

which they operate, their size, their market, and the country in which they are based.

In designing a digital skills strategy or any other type of initiative to help employers to

access the required digital skills, diversity needs to be clearly addressed through a

tailored approach.

9. Reduce the digital divide

Policymakers should take action to reduce the existing digital divide, focusing in

particular on the categories of individuals who do not possess digital skills and are

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consequently at risk of marginalisation not only in the labour market, but also in day-

to-day life, which can contribute to social and economic exclusion.

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CHAPTER 1. SETTING THE SCENE: THE CONTEXT OF THIS STUDY

The digitisation of the economy, along with market globalisation and demographic

change, is reported in the research literature to be one of the most important drivers

behind the profound transformation of the labour market and the way people work with

this digitisation thought likely to become even more significant in the years to come.

This new paradigm represents a major challenge for employers, workers and public

authorities, and the challenges needs to be fully understood in order to identify the

most appropriate policy options to transform them into opportunities for all.

This introductory chapter summarises the debate, as it is communicated in research

literature and policy documents, around the implications of digitisation for the economy,

and in particular for the world of work. As such, it sets out the general context in which

this study on ‘ICT for work: Digital skills in the workplace’ has been conceptualised and

conducted.

The chapter focuses on the definition of the digital economy, the impacts of digitisation

on the labour market and its implications for employers and policymakers. In particular,

it focuses upon the expected impacts of digitisation in terms of job destruction, job

creation and job transformation, as well as setting out the implications of the latest

developments in needs for digital skills.

In addition, the chapter presents the aims and specific objectives of the employers’

survey and of the wider study. It also presents an overview of the research questions

and the methodology used to respond to the research questions.

1.1 Definition of the digital economy

While the ‘digital economy’ can be broadly defined as the economy that is extensively

based on digital computing technologies, most of the literature on the impact of digital

technologies on the world of work converges in pointing out that the phenomenon of

digitisation is actually of great complexity, and is also changing rapidly to embrace new

technological developments.

The concept of ‘digitisation’ covers a wide range of different digital technologies (e.g.

computers, mobile devices, internet and the ‘Internet of Things’, robotics and

automation), which have different implications in terms of their impacts on production

and work.

The OECD, for example, acknowledges this complexity, stating in recent research that

the digital economy is growing quickly and is now permeating “the world economy from

retail (e-commerce) to transportation (automated vehicles), education (Massive Open

Online Courses), health (electronic records and personalised medicine), social

interactions and personal relationships (social networks). […] ICTs are integral to

professional and personal life; individuals, businesses and governments are increasingly

inter-connected via a host of devices at home and at work, in public spaces and on the

move. These exchanges are routed through millions of individual networks ranging from

residential consumer networks to networks that span the globe. The convergence of

fixed, mobile and broadcast networks, along with the combined use of machine-to-

machine (M2M) communication, the cloud, data analytics, sensors, actuators and

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people, is paving the way for machine learning, remote control, and autonomous

machines and systems. Devices and objects are becoming increasingly connected to the

Internet of Things, leading to convergence between ICTs and the economy on a grand

scale” (OECD 2015). In line with this description, the ‘digital economy’ has been defined

through its “four specific features: the irrelevance of geographical location, the key role

played by platforms, the importance of network effects and the use of big data. These

features distinguish it from the traditional economy, particularly as a result of the

associated value chain transformations” (Charrié and Janin 2015).

The World Economic Forum’s analysis of the ‘Future of jobs’ defines the digitisation of

the economy, underlining that the “development in genetics, artificial intelligence,

robotics, nanotechnology, 3D printing and biotechnology, to name just a few, [which]

are all building on and amplifying one another, will lay the foundation for extensive

transformations in the way we live and work”. In this context, “smart systems – homes,

factories, farms, grids or cities – will help tackle problems ranging from supply chain

management to climate change. The rise of the sharing economy will allow people to

monetize everything from their empty house to their car” (World Economic Forum

2016). The digitisation of the economy, involving the extensive changes driven and

supported by digital technologies, is also referred to as the ‘Fourth Industrial

Revolution’, characterised by a blend of technologies that is progressively blurring the

boundaries between the physical, digital and biological spheres. It follows the Third

Industrial Revolution, which started in the second half of 20th Century and was defined

by the use of electronics and information technology to automate production (Schwab

2016)2.

1.2 Impacts and implications of the digital economy

The impacts of digitisation on the labour market are multiple, and have a high degree of

interconnection.

First, the digitisation of the economy is seen in most of the literature3 as contributing

to the polarisation of the labour market in many countries. On one hand,

digitisation is thought to have led to a significant increase over time in the demand for

high-skilled individuals, equipped with cognitive skills and technical knowledge to deal

with tasks and procedures required by the new technologies. On the other hand, it has

led to a sharp decrease in the demand for the medium-skilled and (although to a lesser

extent) low-skilled or non-skilled workers (Berger and Frey 2016).

Second, some authors see digitisation as resulting in job losses. This is due to

automation, based on the development of a combination of increasingly smart

technologies - such as robotics, numerically-controlled machines,

computerised inventory management software, speech recognition, pattern recognition,

automated language translation, self-driving vehicles, e-commerce - which are

replacing humans in doing their jobs and therefore are contributing to job destruction,

as extensively discussed by Brynjolfsson and McAfee (2011). Frey and Osborne (2013)

2 According to the author, ‘the First Industrial Revolution used water and steam power to mechanize production’, while the ‘Second used electric power to create mass production’ (Schwab, 2016) 3 For example: Dolphin T (Ed.) (2015); empirical evidence for the US: Autor et al. (2006, 2008) ; Autor and Dorn (2013); for the UK: Goos and Manning (2007); for Germany: Spitz-Oener (2006); Dustmann et al. (2009); for Western Europe: Goos et al. (2009); Michaels et al. (2014); for Japan: Ikenaga (2009); Ikenaga and Kambayashi (2016).

14

predicted that 47% of jobs in the US were at high risk of being automated over the

following decade or two, and could therefore be lost. In particular, workers in the

transportation and logistics sectors, but also those involved in production, office and

administrative support, were considered at risk of being replaced by ICT or computer-

controlled devices (Frey and Osborne 2013). Valsamis et al. (2015) reported Bruegel

think-tank estimations that 40% to 60% of the jobs in the European Union were at risk

due to digitisation-induced automation. They expressed strong doubts that a fully

digitalised economy will produce sufficient demand for labour to compensate expected

job losses (Valsamis et al. 2015). More generally, the existing empirical evidence

indicates that jobs entailing a high degree of routine-based tasks4 are those most at

risk of automation. These tasks are mostly performed as part of their core job tasks by

middle-skilled workers, as pointed out by Autor, Levy and Murnane (2003), and could

also be behind the polarisation of the labour market mentioned previously. Jobs

requiring intellectual, cognitive or creative skills, and jobs requiring human interaction,

would be less at risk. Nevertheless, some authors express concern also for this type of

job, as the most recent technological developments in the area of artificial intelligence

or the development of highly sophisticated software are considered to put other types

of job at risk,– for example, translators, medical doctors, journalists (Staglianò 2016)

and teachers, the latter in connection with the growing importance of MOOCs (Massive

Open Online Courses) (Staglianò 2016, Bainbridge 2015). Job losses can also occur

because digital technologies can support global outsourcing of tasks to another location

or country with different cost and productivity conditions, which results in direct job

losses in one country but job gains in another (OECD 2014).

Third, a number of other authors maintain that digitisation is favouring the emergence

of new occupations and creating new jobs. Bainbridge (2015) for example

underlines that digitisation is leading to a higher demand of human workers because

digital technology “enables enterprises to make existing products better and more

efficiently, and to make new things. […] Small firms and individuals are already

publishing and distributing books and music via the Internet and creating apps for

smartphones, computers and enabled TVs. Three-dimensional printing enables bespoke

manufacturing by small firms. Aside from opening up opportunities, lowering barriers to

entry also reduces risk and so may increase the number of budding entrepreneurs

starting businesses, perhaps encouraged by online ‘crowdsourcing’ sources”

(Bainsbridge 2015). De La Rica (2016) considers humans to have a comparative

advantage over computers when it comes to ‘cognitive tasks’ which require thinking,

improvising creative solutions and solving unexpected problems, and to also be better

than machines with tasks requiring flexibility to adapt and interpersonal interactions.

These human cognitive and interactive abilities can be “complementary to the work of

computers, rather than necessarily substituting for it, and hence computerisation is

likely to increase the demand for people with this skills” (De La Rica 2016). Digitisation

is generating the emergence of completely new occupations and jobs, as pointed out for

example by the OECD (2014). Examples of brand new occupations which emerged

recently in connection to digital technologies include big data architects, internet

engineers, networking specialists, hardware engineers, mobile app developers, data

4 According to Autor and Handel (2013) routine tasks can be both cognitive and manual. On the one hand the routine cognitive tasks involve the importance of repeating the same tasks, the importance of being exact or accurate, and more in general the structured versus unstructured work. On the other hand, the routine manual tasks involve controlling machines and processes, keeping a pace set by machinery or equipment, and spend time making repetitive motions.

15

scientists, and digital marketing specialists. Digitisation can also lead “to job growth in

traditional occupations by supporting the creation of new businesses (e.g.

entrepreneurship) or the expansion of existing firms (e.g. growth from tapping into

foreign/new markets or more effective marketing)” (OECD 2014). Estimates from the

World Economic Forum (2013) indicate that digitisation is responsible for the net

creation of 213,578 jobs in Western Europe. A study found that one in ten companies in

Germany has employees dealing specifically with activities in Web 2.0. The study

estimates that digitisation created about 1.4 million new workplaces in Germany in

2012. Most of the new workplaces (976,000) were created in the service sectors, while

300,000 were created in manufacturing (Bitkom 2014). Nevertheless, it is worth

mentioning that there is no definitive evidence regarding the net impact of digital

technologies on jobs.

Fourth, digitisation brings transformations in existing jobs, by changing (to

different degrees) work practices, the way tasks are carried out, job contents and job

requirements, and, as a consequence, the skills needed to perform the job (OECD 2014,

Valsamis et al. 2015). As pointed out by Berger and Frey (2016) among several other

authors, “as digital technology becomes more heavily integrated into the daily

operations of firms across a wide range of industries, digital literacy will become

critically important for the vast majority of workers” (Berger and Frey 2016). The World

Economic Forum (2016) shares this perspective, stating that “as entire industries

adjust, most occupations are undergoing a fundamental transformation. While some

jobs are threatened by redundancy and others grow rapidly, existing jobs are also going

through a change in the skill sets required to them” (World Economic Forum 2016). This

would be “highly specific to the industry, region and occupation in question as well as

the ability of various stakeholders to manage change” (World Economic Forum 2016).

The growth of digitisation has therefore resulted in an increased demand of digital

skills in recent years, which is expected to continue, as shown by several studies (e.g.

Empirica 2015, World Economic Forum 2016). Overall, the literature shows that an

increasing number of jobs require employees to use ICT and possess digital skills.

Estimates suggest that 90% of jobs need at least basic computer skills (European

Commission 2014). A different estimate based on the OECD definition of “intensive ICT-

using occupations” (OECD 2016) shows these accounted for 22% of the whole economy

in EU15 in 2010 (ibid)5. More recent OECD statistics on employment of “ICT specialists”

displays a growing trend in most OECD countries. In particular, in EU226 the share of

“ICT specialists” employment was on average 3.6% of the total employment in 2014

(and 3.3% in 2011), ranging from as much as 6% in Finland or 5.3% in Sweden to 2%

in Latvia or 1.7% in Greece (OECD 2015). A recent US study for example shows that, in

the middle-skill job market, the world is increasingly divided between jobs that demand

digital skills and those that do not (Burning Glass Technologies 2015). Being able to use

spreadsheets, word processing programs, digitalised systems such as accounting

systems, is required in many middle-skilled jobs, as well as more occupationally specific

digital skills (ibid.). These figures indicate that digital skills are required in many jobs

and have become transversal skills. The nature of demand for digital skills is, however,

influenced by the strategies firms pursue (Danish Technological Institute 2014).

Moreover, it is highly dependent on the sector and the market position of the firm. A

5 This data is based on the broad definition based on the methodology described in OECD Information Technology Outlook 2004 6 Finland, Sweden, Luxembourg, UK, Ireland, Netherlands, Denmark, Estonia, Belgium, Germany, Czech

Republic, Slovenia, Austria, Hungary, Portugal, Spain, France, Italy, Poland, Slovakia, Latvia and Greece.

16

shortage in the supply of digital skills, or the lack of an appropriate level of digital skills

among existing employees, poses serious challenges to employers in terms of

competitive position.

Fifth, digitisation has been shown to impact on working conditions. More specifically,

a recent study by Eurofound (2014) has demonstrated how digital technologies are

contributing significantly to changes in the way we work, with an expected negative

impact on the work-life balance and health and safety of workers (e.g. increased stress,

decreased protection, higher risks related to working remotely), as well as their

remuneration. This is due to working from non-conventional workplaces (as in the case

of mobile work), on poorly paid micro-tasks (as in crowd employment), and long, or so-

called ‘anti-social’, hours to meet clients’ or employers’ demands (Eurofound 2014). The

study by Störmer et al. (2014) also highlights that work is increasingly more networked

and less rigidly focused on a specific workplace or around fixed working hours. Another

impact on working conditions is related to the progressive increase in self-employment,

as digital technologies make outsourcing of specific tasks much easier than in the past.

Self-employment is notoriously a form of employment that has a much lower level of

protection in several areas of working conditions (Eurofound 2010 and 2014).

In addition, while the net impact of digitisation on overall employment levels is

debatable, there are national-level studies that indicate that digital technologies may

contribute to productivity increases and economic transformation and with

multiplicative effects on non-ICT sectors, if accompanied by work process

reorganisation and skill upgrading (OECD 2004). For example, an Italian study shows

that the spread of new information and communication technologies in enterprises has

led to a profound transformation of the Italian production system and has impacted

efficiency and innovative capacity (Di Carlo, et al. 2010). A Danish study found that, if

ICT investments are not accompanied by skill upgrading, ICT investments may even

lead to a reduction in productivity (Danish Technological Institute 2013).

On a final note, as pointed out by Valsamis et al. (2015), entire industries are being

transformed by digital technologies (e.g. the financial sector and the manufacturing

industry). Digitisation can sometimes bring down leading players in the industry, as

happened to Kodak which, at its peak in 1988, had 145,300 employees, and in 2012

this was down to 13,100 (Valsamis et al. 2015). The digitisation of the economy is not

only transforming entire economic sectors, but also the traditional split between

‘industry’ and ‘services’, which has become less relevant than it used to be. As pointed

out by Christophe Degryse (2016), “the ‘Fourth Industrial Revolution’ seems to be

making the frontier between the two sectors much more porous: an emblematic case of

this characteristic merging of industry and services is the so-called intelligent car,

potentially a ‘computer on wheels’ incorporating all the services that mobile applications

are increasingly able to offer the user” (Degryse 2016).

1.3 Digital skills and digital literacy

As seen above, the digitisation of economy has a number of complex effects on the

labour market, work organisation, workers’ skillsets and working conditions. In

particular, it entails a shift in the type of skills required by workers in relation to ICT

utilisation, exploitation and advancement in the workplace. It thus becomes

fundamental that the effective introduction of ICT in the workplace is accompanied by

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an appropriate upskilling of workers. In this context, it is essential to understand what

digital skills are and how they have been conceptualised to date.

Scholars, governments and international organisations have generally and progressively

recognised the importance of digital skills within knowledge economies, both in the

workplace and more generally. The literature on the conceptualisation and importance

of digital skills is thus broad.

Digital skills and related concepts, such as digital competence, have become key terms

in the discussion on the kind of skills needed by citizens – in Europe and beyond – to

participate and thrive in our society (European Commission 2010; Ferrari 2012;

Gallardo-Echenique et al. 2015), not only in terms of citizens’ social and digital

inclusion, but also in terms of employability and economic growth (European

Commission 2016; Ferrari 2013; Kolding, Robinson, & Ahorlu 2009; Lavin & Kralik

2009; Lanvin & Bassman 2008; Vuorikari et al. 2016).

Despite the overall agreement on the importance of digital skills, no common definition

has been agreed upon, with different terms and interpretations of the content of digital

competence and of the skills, knowledge and abilities it implies. Different terms have

been used over time with a more or less broad scope to identify skills linked to the use

and understanding of ICT (Gallardo-Echenique et al. 201; Ilomaki, Kanotsalo, & Lakkala

2011)7. These concepts are used in policy documents, academic literature and learning

practices to refer to more or less sophisticated skills and to different groups of users,

ranging from the general public to ICT professionals.

The evolution and diversity of definitions used is linked to several factors. The difficulty

in reaching one common definition stems first of all from the fluidity of the concept,

which is continuously expanding and changing as a consequence of the rapid evolution

of information and digital technologies and their use over time (Mutka 2011). The broad

range of terms and definitions used is not however only linked to societal and

technological change, but also reflects different research interests and aims across the

literature Ilomaki, Kanotsalo & Lakkala 2011).

The first definitions used in relation to this type of skills referred to ‘computer or ICT

literacy’ as declarative and procedural knowledge about computer use (Fraillon, Schulz

& Ainley 2013). With time, as technologies have embedded more complex

functionalities (so that they can be used for more advanced purposes) and the use of

ICT has evolved, not least through the internet, broader definitions have emerged, to

reflect the more pervasive and encompassing role that these concepts currently have

(Lavin & Kralik 2009; Mutka 2011). Even though agreement on a definition still does

not exist, a common trend is identifiable in the literature of the past decade, which is

the expansion of the scope of what is meant by digital skills (Lavin & Kralik 2009).

In 2006, European institutions defined digital competence as "the confident and critical

use of ICT for work, leisure, learning and communication", recognising it as one of eight

key competences for lifelong learning (European Parliament and Council of the EU

2006). This definition implies that digital competence does not only encompass the

operational usage of digital devices, but also cognitive skills and attitudes (Martin 2006;

Mutka 2011). It furthers implies that digital competence is a transversal competence

7 For a more detailed overview of how different concepts have been used, see Ilomaki, Kanotsalo, & Lakkala (2011); Gallardo-Echenique et al. (2015); Ecorys (2016).

18

which enables individuals to acquire other competences (Ferrari 2012).8 While this term

has been broadly used in policy documents and academic literature, both in a

descriptive and normative manner, it is still not considered a particularly stable notion

(Ferrari 2012; Ferrari, Punie, & Redecker 2012; Ilomäki, Kantosalo & Lakkala 2011).

Several studies have attempted to operationalise the concept of digital skills, by

identifying the components and elements it consists of and describing in practice the

skills it includes (Eshet-Alkalai 2004; Eshet-Alkalai & Chajut 2010; Ferrari 2012; Martin

2006; OECD 2013). The objective, in many instances, was to allow for an assessment

or a measurement of this competence and to enhance the understanding and the

development of digital competence among citizens, students or other segments of the

population (Ferrari 2012; Ferrari, Punie, & Redecker 2012). The conceptual and

practical frameworks developed have been used for both policy-making purposes and

for the development and assessment of certification and learning practices (see Ferrari,

2012 for an overview). The Digital Agenda for Europe for example, which envisaged the

development of "EU-wide indicators of digital competence and media literacy", led to

the development of the DigComp framework on digital competence (European

Commission 2010a) which was designed to help policymakers formulate appropriate

education and lifelong learning policies.

Overall, digital competence and skills include a range of inter-related concepts (Ecorys

2016). Examining the literature, it is possible to identify three main categories of digital

skills, which are replicated in various ways in different frameworks for the measurement

or development of digital competence. These three categories apply to different types of

abilities, linked to the capacity to carry out tasks of increasing complexity or specialised

nature, thus also applying to different type of users:

a) Basic digital literacy skills empower individuals to become digitally literate; these

skills can be applied both to the workforce and generally to individuals in knowledge

society;

8 Similar definitions are provided by scholars, using terms such as information literacy or digital literacy (Martin 2006)

Digital Competence Framework for Citizens (DigComp)

The Digital Competence Framework for Citizens (DigComp) was published in

2013 by the European Commission. It is a tool to “improve citizens’ digital

competence, help policy-makers formulate policies that support digital competence

building, and plan education and training initiatives to improve the digital competence

of specific target groups” (Vuorikari et al. 2016).

DigComp is based on four dimensions. Dimension 1 and 2 represent a conceptual

reference model identifying the areas to be part of digital competence and the

competence descriptors that belong to each area. Dimensions 3 and 4 relate to the

levels of proficiency for each competence and to examples of knowledge and skills

applicable to the competences (Ferrari 2013). The framework builds on a task based

approach. It can be used to measure digital competence across Europe (European

Commission 2014).

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b) Digital skills which relate to employment, encompassing basic skills plus skills

which are needed in a workplace and generally are linked to the use of ICT

applications developed by professionals of information technology;

c) Digital skills for ICT professions, which include both categories above and the

skills needed in the ICT sector as well as having an innovative and creating

component, as linked to the ability to develop new digital solutions, products or

services.

Among the frameworks which have operationalised the definition of digital skills, a

number have focused specifically on the skills needed in the workplace, focusing on the

digital skills needed by the workforce and employed as drivers of employability, growth

and competiveness.

Some of these frameworks have only looked at the skills of ICT workforce. The

definition set out by the European e-Skills Forum for example aims to classify the e-

skills of the workforce, with a specific focus on ICT practitioners, to better define the e-

skills gap and policy initiatives to address it across Europe. Building on this framework,

several other works have described the development of e-skills demand and supply with

regard to ICT workforce (Empirica 2009; Empirica 2015). The conceptual framework

distinguishes between ICT user skills, ICT practitioner skills and e-business skills (see

below).

Two other approaches have been developed with the aim of investigating and assessing

the digital skill level of the overall workforce. Cedefop (2015) distinguishes between

basic, moderate and advanced ICT skills, which relate to different levels of competence

and type of tasks carried out by the worker.

European e-Skills Forum – 2004

In 2004, the European e-Skills Forum adopted a definition of the term ‘e-skills’. The

term was then used by the European Commission to respond to the growing demand

for highly-skilled ICT practitioners and users (European Commission 2007; Gallardo-

Echenique et al 2015). The classification put forward distinguished between:

ICT user skills, required for effective use of ICT systems and devices; “ICT users

apply systems as tools in support of their own work, which is, in most cases, not

ICT”;

ICT practitioner skills, required for researching, managing, developing and

designing, consulting, marketing and selling, integrating, installing and

administering, maintaining, servicing ICT systems;

E-business skills (or e-leadership skills), needed to exploit opportunities

provided by ICT, producing more efficient and effective performance of different

types of organisations, exploring possibilities for new ways of conducting business

and organisational processes, establishing new enterprises.

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Similarly, the OECD (2004) identifies basic users, advanced users and ICT specialists,

following a task-based approach with regard to the employed workforce. More recently,

the organisation has adopted the concept of generic, specialist and complementary

skills to identify the three main lines along which new digital skills are required (OECD

2016).

The OECD Programme for the International Assessment of Adult Competencies (PIAAC)

survey builds on this distinction and offers a direct measurement of adult digital skills.

It has been used to measure the demand for ICT skills at work (OECD 2015), and to

measure and assess level of skills and mismatches in the workplace (Pellizzari, Biagi &

Brecko 2015)9.

9 “The survey provides a rich source of data on adults’ proficiency in literacy, numeracy and problem solving in technology-rich environments – the key information-processing skills that are invaluable in 21st-century economies – and in various “generic” skills, such as co-operation, communication, and organising one’s time.” (OECD, 2013)

The CEDEFOP ESJ Survey

The Cedefop European Skills and Jobs (ESJ) survey (2015) analyses digital skills by

identifying three bundles of ICT skills:

Basic ICT skills: using a PC, tablet or mobile device for email, internet browsing;

Moderate ICT skills: Word-processing, using or creating documents and/or

spreadsheets;

Advanced ICT skills: Developing software, applications or programming; use

computer syntax or statistical analysis packages.

OECD’s Skills for the Digital Economy

The OECD (2004) suggests a distinction between:

ICT specialists: user whose competences cover the “ability to develop, operate

and maintain ICT systems”. ICTs make up for the main part of their job;

Advanced users: this group of users are “competent users of advanced, and

often sector-specific, software tools”. ICT is a tool in a workplace context;

Basic users: basic users are “competent users of generic tools (e.g. office

software, e-mailing and other internet-related tools) needed for the information

society, e-government and working life”.

The OECD (2016) introduced another classification, referring to ICT generic,

complementary and specialist skills. More specifically, “ICT specialist skills [are those

necessary] to programme, develop applications and manage networks; ICT generic

skills to use such technologies for professional purposes; ICT complementary skills to

perform new tasks associated to the use of ICTs at work, e.g. communicate on social

networks, brand products on e-commerce platforms or analyse big data” (OECD

2016, p.5). Similar to e-business skills, complementary skills are thus “skills that are

not related to the capability to use the technology effectively but to carry out the

work within the new environment shaped by ICTs, i.e.: a “technology-rich

environment”” (ibid, p.7).

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At the national level, the Canadian study “Defining Essential Digital Skills in the

Canadian Workplace” (WDM Consultants 2011) is an example of a sector-wide digital

skill framework which focuses on digital skills in the workplace.

In terms of methods, Van Deursen, Helsper, & Eynon (2014) identify three main basic

methodologies employed to investigate levels of digital skills, describing the main

pitfalls and benefits of each method:

Surveys with questions that are assumed to deliver indirect evidence for

the command of skills: this method is very common with large benchmark

surveys, with the one downfall of not clearly identifying the relation between the use

of an application and the skill;

Surveys with questions that request self-assessments of skills: this is the

most used method, which however can originate bias in terms of overrating and

underrating skills;

Performance tests: these are considered the most reliable methods in terms of

internal validity, but have the main problem of being very costly, time consuming

and difficult to implement on large scale.

1.4 Digital skills challenges

Given the recognised importance of digital skills for an effective implementation of ICT

in the workplace, the main challenge for employers is to make sure that the supply of

digital skills within the workforce matches its demand at the labour market and

enterprise level, in terms of both required type and level of skills. In other words, the

digital skill challenge for businesses is twofold. It can relate to the lack of workers with

the needed skills in the labour market (skills shortages), or to the presence of

overskilled or underskilled workers at company level (skills mismatches and skills gaps).

Challenges can therefore be external to the firm (as in the case of shortages) or internal

(as in the case of mismatches or gaps).

Cedefop (2015a) has provided relevant definitions that are useful to better understand

the digital skills challenges that employers can face, although they do not refer directly

to digital skills but more generally to skills and thus need to be adapted to the specific

reality of digital skills.

Defining Essential Digital Skills in the Canadian Workplace

The framework is based on a survey among a sample of SMEs drawn from various

industry sectors across the country and defines digital skills as a multifaceted

concept which encapsulates four skill clusters: (1) Digital Technical Skills; (2)

Digital Information Processing Skills; (3) Foundational Skills; and (4) Transversal

Skills.

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A number of academic studies and policy-oriented studies have focused on the supply

and demand of digital skills in the economy, relating to the workforce as a whole, or

more specifically among ICT professionals.

Most studies focus on the shortage of a skilled workforce, pointing at a mismatch

between demand and supply of ICT professionals, both in terms of shortages and skill

mismatches (CEPIS 2014; Empirica 2014, 2015; Hüsing, Werner & Dashja 2015; UK

Digital Skills Taskforce 2014) and an overall shortage of digital skills in the labour

market (Burning Glass Technologies 2015, European Commission 2016). These studies

normally relate to digital skills at the labour market level, focusing on their impact in

terms of overall economic trends, mainly identifying a loss of potential economic growth

as a consequence of the insufficient supply of workers with the right skillset across the

European Union (CEPIS 2014). A smaller number of studies focus on the impact of

shortages at the company level, as a barrier to digital transformation (Capgemini

Consulting 2013) and in terms of mitigating strategies (Ramboll 2014). At individual

level, studies mainly report the importance of this type of skills in terms of

employability as “survival” (Eshet-Alkalai 2004) or “gateway skills” (Van Deursen A.

2010).

Similarly, both the academic literature and policy-oriented research on skill matching

have been largely focused on the problem of overskilling (Cedefop 2015b; Livanos &

Nunez 2016; McGuinness & Ortiz 2016), well-documenting the negative effects of the

phenomenon for both firms and workers (Green & Zhu 2010).

Skills shortages, mismatches and gaps

Skill shortages are usually defined as “instances when the demand for a particular

skill exceeds the supply of available people with that skill at market-clearing wage

rates” (Cedefop 2015a, p.24). This means that a shortage exists when “there are not

enough individuals with the required skills within the economy to fill existing

vacancies at market-clearing wages” (ibid., p. 26).

Skill mismatches on the other hand refer to those situations in which there is a

“(qualitative) discrepancy between the qualifications and skills that individuals

possess and those that are needed by the labour market” (ibid, p. 27). This means

that skills mismatches relate to situations in which the workforce is overskilled or

underskilled when compared to the demand of skills in the labour market and in the

enterprise. As such, the concept is broader than that of skills gap or shortages.

Skills gaps finally are to be interpreted as situations where “the level of skills of the

existing workforce in a firm is less than required to perform a job adequately or to

match the requirements of a job” (ibid, p.27). This definition is important as it allows

us to understand what the focus of the study is. We are looking at a situation in

which the workforce is underskilled at the level of the enterprise. This concept

thus concerns the employed (differently from skills shortages which normally relate

to employers’ difficulty to hire someone who has the skills required for the vacant

job).

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Fewer studies, on the other hand, deal with digital kills gaps specifically, thus referring

to the workforce employed at the company level and the impact which digital

underskilling can have on the performance of the organisation, leaving a research gap

which this study aims to fill (Pellizzari, Biagi & Brecko 2015).

Overall these studies point out that, as well as the growing demand for this type of

skills, a large part of the workforce still has insufficient digital skills. According to the

European Commission's Digital Scoreboard, for example, in 2016 37 % of the EU labour

force and around 45 % of the EU population had insufficient digital skills (low or no

digital skills at all), , with significant differences across Europe.10 In the UK, the House

of Commons (2016) reported that digital skills shortages persist at all stages in the

education and training pipeline, from schools to the workplace. Different factors can

account for this shortage. These include structural changes, such as the adoption of

new technology, since the educational and lifelong learning systems need time to

produce skills which can keep up with technological change (Quintini 2011). Similarly, a

lack of investment in upskilling employees, out-of-date training systems,

inability/difficulty in attracting workers with adequate skills (also financial), and

information asymmetries can also be influencing factors (Livianos & Nunez 2016).

In terms of impact, the adverse effects of digital skills gaps (underskilling) are

evidenced at both aggregated, company and individual level (OECD 2015). The

problems arising from skills gaps are very similar to those arising from overskilling, i.e.

overall lower productivity, lower salaries or higher risk of being fired for the employee

(Livianos & Nunez 2016). At the aggregated level, the literature shows how the overall

economic output is linked to how well workers can carry out their tasks (OECD 2015).

Economic growth can be negatively affected because of skill shortages at the labour

market level and because of skills gaps within companies. The cost of skills mismatches

has become a particular concern for policymakers nationally and internationally (OECD

2015; ILO 2008; European Commission 2016).

At the company level, skills gaps cause a loss in terms of productivity, increasing labour

costs, slowing growth and adaptation of new technologies down (OECD 2012; OECD

2015; Idea Consult, AIAS/UvA, Ecorys, Wifo, 2015). Organizations are beginning to

recognise the scope of their problem. In Comptia’s IT Skills Gap Report for 2014, IT

skills gaps remain one of the main challenges, with 58% of companies reporting

concerns about the quality and quantity of IT talents available to them (Comptia 2014).

A study carried out by Capgemini (2013) in collaboration with the MIT Center for Digital

Business showed that 77% of companies perceived missing digital skills within their

organisation as the key obstacle to their digital transformation. Over 90% of companies

surveyed on this occasion stated that they did not have the necessary “skills in the

areas of social media, mobile, internal social networks, process automation and

performance monitoring and analysis” (ibid). However, only 46% were investing in the

development of digital skills (ibid). Similarly, in the UK, the House of Commons (2016)

reported that organisations were “not maximising the potential of new digital

technologies or utilising the skills and talents of their employees in the most productive

way” (House of Commons 2016), and that almost 50% of employers had a digital skills

gap (ibid.). The study stated that the economic impact of these gaps was clear, with

10 See https://ec.europa.eu/digital-single-market/en/news/new-comprehensive-digital-skills-indicator (26/01/2017). The Digital Agenda Scoreboard applies a newly constructed Digital Skills Indicator, which is based on the Digital Competence Framework developed by DG EAC and IPTS.

24

SMEs primarily losing out, and thus making it “imperative for businesses to develop the

digital skills of their employees’ being this ‘now a matter of survival” (House of

Commons 2016).

The literature clearly points out that firms may find it more problematic to mitigate

underskilling, compared to overskilling, as this normally requires investing in training

(Cedefop 2016; Livanos & Nunez 2016). Optimal training decisions however require

employers to have accurate information about their workers’ needs (McGuinness & Ortiz

2016). It is therefore becoming evident why developing methods to test and measure

these skills are important.

1.5 The digital skills challenge: policies and solutions

Two approaches can be identified in the literature on how to respond to the digital skills

challenge as described in the previous section: firstly, the need to intervene on the

educational system and, secondly, the need to introduce training for the current

workforce.

Regarding the first point, some authors point to the need to intervene on the

educational system in order to make sure that the required digital skills are available on

the labour market and among the general population. As indicated by Berger and Frey

(2016), “the educational system should be aligned to provide students with basic digital

and ICT skills. Such initiatives are currently being undertaken in several European

countries. […] Integrating digital skills in the curriculum early on will be crucial for

maintaining a competitive labour market in the future” (Berger and Frey 2016).

However, as recognised by the same authors, a narrow focus on digital skills will not be

sufficient, as “the digital skills of today are likely to be obsolete sooner than we may

think” (Berger and Frey 2016) and therefore it is advisable to provide (future) workers

with integrated skillsets of technical, creative and social skills, as “analytical and

creative capabilities will be the core ingredients of successful careers in the future”

(Meyer 2016), with this being more likely to meet employers’ needs .Educational

systems have a strategic part to play to tackle the digital skills challenge, as do

governments and policymakers, as indicated for example by the launch of the European

Commission’s ‘Grand Coalition for Digital Jobs 2013-2016’ “which emphasises the need

for embedding basic ICT training throughout the European educational system” (Berger

and Frey 2016)11.

With reference to the second approach mentioned above, Schwab (2016) suggests

focusing also on today’s workforce, as “while much has been said about the need for

reform in basic education, it is simply not possible to weather the current technological

revolution by waiting for the next generation’s workforce to become better prepared.

Instead it is critical that businesses take an active role in supporting their current

workforces through re-training, that individuals take a proactive approach to their own

lifelong learning and that governments create the enabling environment, rapidly and

creatively, to assist these efforts” (Schwab 2016).

Training aimed at fostering digital competence and digital skills is commonly believed to

be the most appropriate tool to tackle the digital challenge. As pointed out by the

11 https://ec.europa.eu/digital-single-market/en//digital-skills-jobs-coalition

25

OECD, several longitudinal studies have demonstrated that training (in particular on-

the-job training) is “an appropriate policy response to cope with the rapid pace of

technological change that characterises the digital economy” (OECD 2015). In addition,

while on-the-job training is a key policy to maximise skills utilisation and contrast skills

obsolescence, “existing practices at firm level show that firms themselves have great

incentives to provide employees with continuous professional training if they want to

innovate and remain competitive in the market” (OECD 2015).

Challenges are reported to remain regarding the training participation gap that exists in

most countries between “the less-qualified and the more-qualified, prime age and older

workers, those in large and small and medium enterprises” (OECD 2015).

Technological change, combined with the long period out of formal education, requires

older workers in particular to update skills. As displayed by empirical evidence, on-the-

job training increases the proportion of older workers in employment and reduces their

turnover, although these effects do not compensate for the negative age bias

associated with digital technologies (Behangel et al. 2014). Older workers in low-skilled

occupations are reported to have even less opportunities to access training on digital

technologies than older workers with higher skills (Behangel and Greenan 2012).

Workers employed in small and medium enterprises are also considered less likely to

access training to develop digital skills. As pointed out by the OECD (2015) this is due

on the one hand to the fact that small and medium enterprises “lack sufficient resources

to develop training programmes; on the other, training providers have not sufficiently

developed training content specific to the needs of SMEs” (OECD 2015).

In order to make training more widely available, two main types of initiatives are

envisaged, , fully state-funded initiatives to provide training and skills development

programmes based on the example of Korea or Ireland (OECD 2015) and/or the

creation of multi-stakeholder consortia at the sector, local or industry level to share

training costs for the workforce, as been successfully implemented in some countries

(OECD 2015). Multi-stakeholder partnerships (e.g. governments, training providers,

employers and social partners) are considered as contributing to the building of

capacity, and in order to promote a more inclusive and targeted approach to skills

development, as they “encourage training provision that is more responsive to labour

market needs, familiarise with vocational programmes and qualifications and help

vocational trainers to keep up-to-date” (OECD 2015). Also, collaboration between

businesses is seen as a possible way to enhance the provision of training. As pointed

out by Schwab (2016), “business collaboration within industries to create larger pools of

skilled talent will become indispensable, as will multi-sector skilling partnerships that

leverage the very same collaborative models that underpin many of the technology-

driven business changes underway today” (Schwab 2016).

1.6 Purpose and scope of this study

In the context described in the previous sections, this study on ‘ICT for work: Digital

skills in the workplace’ has been conceptualised and implemented in order to examine

the transformation of jobs in the digital economy in the European Union, investigating

the penetration of digital technologies into workplaces, the digital skills required by

employers and the digital skills currently available in workplaces.

26

In this light, a number of research tasks aimed at gathering primary data on digital

skills and digital skill gaps have been carried out, primarily through an employers’

survey, and through qualitative interviews on the impact of ICT on job quality. Rooted

in an extensive review of academic and scientific literature, existing surveys and data

from other statistical sources, the study aims to fill existing research gaps.

This report mostly presents findings from the employers’ survey (the ‘European

European Digital Skills Survey’), integrated where relevant with findings from the

qualitative analysis of the impact of digital technologies on job quality and working

conditions, presented in a separate report “The impact of ICT on job quality: evidence

from 12 job profiles”12.

In order to fill the research gaps identified, the survey was designed to answer the

following research questions:

How many jobs in the EU require digital skills?

What types of digital skills are the most required by employers?

How can the jobs requiring digital skills be classified according to the level of digital

skills required?

What are the differences across EU Member States, economic sectors and

occupations in terms of digital skills required by employers?

What are the most common digital skill gaps in workforce according to employers?

What are the differences across economic sectors and occupations in terms of digital

skill gaps?

What are the actions undertaken by employers to address existing digital skill gaps?

What are the differences across economic sectors and occupations in terms of

actions undertaken by the employers to address the lack of digital skills?

The specific objectives of the survey were to:

Quantify the jobs, in the EU that require digital skills by economic sector;

Provide evidence on the level/type of digital skills required by different jobs and in

different sectors;

Identify the main digital skills gaps in different occupational categories and

economic sectors;

Investigate how employers deal with ICT/digital skills gaps (e.g. providing training,

out-sourcing);

Examine the main bottlenecks/barriers to improved availability of digital skills.

The related variables of interest to investigate were:

Types and level of digital skills existing by occupational category;

Types and level of digital skills gaps by occupational category;

Actions taken by employers to deal with digital skills gaps;

Types of barriers to improved availability of digital skills by occupational categories.

12 Ecorys and Danish Technological Institute (2016) available at https://ec.europa.eu/digital-single-market/en/news/report-shows-digital-skills-are-required-across-all-types-work-also-jobs-outside-office

27

1.7 Overview of methodological approach

The survey was carried out on a sample of 7,800 workplaces, representative of

13,803,113 workplaces across the European Union in the following 12 economic sectors

(in bold):

A - Agriculture, forestry and fishing

B - Mining and quarrying

C - Manufacturing

D - Electricity, gas, steam and air conditioning supply

E - Water supply; sewerage; waste management and remediation activities

F - Construction

G - Wholesale and retail trade; repair of motor vehicles and motorcycles

H - Transporting and storage

I - Accommodation and food service activities

J - Information and communication

K - Financial and insurance activities

M - Professional, scientific and technical activities

N - Administrative and support service activities

O - Public administration and defence; compulsory social security

P - Education

Q - Human health and social work activities

R - Arts, entertainment and recreation

S - Other services activities

T - Activities of households as employers

U - Activities of extraterritorial organisations and bodies

The survey was carried out on six countries, selected according to their level of

digitisation to represent the European Union as a whole. The countries covered are

Finland, Germany, Portugal, Slovakia, Sweden and the United Kingdom.

A two-stage, stratified sampling design was used to select and include workplaces in the

sample. In particular, the primary sampling units were the countries, the secondary

sampling units were the workplaces and the stratification variables were the economic

sector and the workplaces size. To make sure that results could be correctly inferred to

the general target population, calibration estimators were employed, and the

parameters of interest were analysed both at sampled countries level and at EU28 level.

In particular, with the aim of producing estimations both at EU and sampled countries

level, two different calibration weights were calculated. The first allowed the estimation

28

of the amount and the characteristics of workplaces falling in the selected economic

sectors at EU level. The second allowed the estimation of the workplaces in each

country included in the sample.

Using the survey data, it was possible to produce statistics, both at EU and sampled

country level, on the structure and characteristics of workplaces, characteristics of

establishments’ workforce, types of occupations, type and level of digital skills, type and

level of digital skill gaps, and actions and related barriers to reduce digital skill gaps.

Survey data also allowed for the analysis, at EU level, of some specific characteristics of

certain occupations selected by the respondents as being amongst the most relevant for

the day-to-day activities of their business.

All the analyses were carried out at EU level and, wherever possible, estimations were

carried out for individual Member States. Descriptive and multivariate analyses were

carried out. Composite indicators of digital skills, which summarise different pieces of

elementary information describing digital skills, were calculated using appropriate

statistical procedures.

Data were collected using a mixed web and phone approach to boost the response rate

and increase participation, based on a questionnaire designed to meet project

requirements. For each workplace, the respondent was selected among the members of

staff with the best overview of working tasks, normally the human resources manager

or the managing director.

This circumstance is however one of the main limitations of this survey in terms of

accuracy of the information collected, common to all the employers’ surveys which

investigate employees’ skills, attitudes or behaviours. Indeed in employers’ surveys the

selected respondents do not report individual/personal information regarding

themselves, but report information about characteristics of their employees or sub-

groups of them (e.g. the level of skills possessed). Therefore, the reported information

reflects the respondents’ assessment of a perceived characteristic possessed on average

by employees (or sub-groups of employees). This is an issue particularly in large-sized

workplaces, where the human resources manager is requested to respond with

reference to a significant number of employees and can result in a response bias (e.g.

inaccurate answers) or in a high number of missing responses (e.g. “don’t know”). An

additional limitation of this specific survey – despite the rigorous sampling strategy -

comes from the limited sample size, which ensures a high level of representativeness

for the planned domains of study, but does not allow for more granular analyses of

results.

Conversely, the main methodological strength of this survey is the robust sampling

design and estimation phase (calibration weights) adopted, which allows highly reliable

and EU28-level representative results in spite of the limited funds available to carry it

out. In particular, thanks to the calibration procedure, data collected from workplaces

(and their employees) in six Member States can be inferred to workplaces in the whole

EU28: the structure of workplaces and the distribution of employees, in terms of

economic sector and the workplaces size, are constrained to known values proceeding

from official statistics. In other words, results from 7,800 interviews can be inferred to

29

13,803,113 workplaces and 150,563,540 employees in 12 selected economic sectors in

the whole European Union (EU28).

The full survey methodology, including the characteristics of the sample and target

population and the survey questionnaire, are set out in detail in Annex 1.

The findings from both the survey and the qualitative analysis of specific job profiles

was presented and validated by experts and stakeholders in a one-day workshop

(Section 6.2), which allowed identification of the main areas of concern and the policy

recommendations reported in Section 6.3.

The study was carried out by a research team led by Maurizio Curtarelli, which included

researchers from Ecorys (Vicki Donlevy, Maryam Shater Jannati, Mike Blakemore,

Elizabeth Kwaw), the Danish Technological Institute (Martin Eggert Hansen, Hanna

Shapiro, Gwendolyn Carpenter), and a Statistical Expert (Valentina Gualtieri, Head of

Statistical Service, National Institute for the Evaluation of Public Policies). The team was

supported by a Scientific Committee (Irene Mandl, Head of Unit - Employment,

Eurofound; Ferràn Mañé, Associate Professor, University Rovira i Virgili of Tarragona;

Graham Vickery, former Head of OECD Information Economy Group), and the study

benefitted as well from inputs from experts at the validation stage (Barbara

Gestenberger, Head of Unit – Working Life, Eurofound; Konstantinos Pouliakas,

Department for Skills and Labour Market, Cedefop; Katerina Ananiadou, Division for

Policies and Lifelong Learning Systems, UNESCO).

1.8 Structure of the report

In order to reach the aims illustrated above, the report is structured as follows:

This first chapter has presented the available evidence and the discussion around

the digitisation of the economy and its impacts on the labour market in terms of job

destruction, job creation and job transformation. Furthermore it has considered the

implications and challenges for employers in terms of digital skills needs. In this

context, the chapter sets out the main aims of the study and of the employers’

survey, the research questions, and a brief overview of the survey methodology.

Chapter 2 presents and analyses the characteristics of the workplaces in the

economic sectors covered by the survey and their workforce, in order to set the

context for later responses to the research questions.

Chapter 3 discusses the results of the survey in relation to the use of digital

technologies in European workplaces, and presents the investment strategies in ICT

of workplaces across the European Union.

Chapter 4 sets out the results from the survey in relation to the type and level of

digital skills required in specific occupations in workplaces across the European

Union, including a focus on specific jobs requiring digital skills.

Chapter 5 analyses one of the core aspects of this survey. Digital skill gaps (defined

as the proportion of workers not fully proficient to carry out tasks involving the use

of digital technologies) are calculated and presented across different types of digital

skills and different occupations in European Union workplaces. The chapter also

presents the perceived impacts of digital skills gaps on business performance, the

30

actions undertaken to tackle digital skill gaps in the existing workforce and the main

difficulties encountered when taking action to address gaps.

Chapter 6 presents conclusions and recommendations, which have been enriched

with findings from consultation of relevant stakeholders.

In addition:

Annex 1 presents the detailed survey methodology.

Annex 2 contains a number of statistical tables and charts which complement the

data presented in specific sections of this report.

Annex 3 details the bibliographic references for this study.

31

CHAPTER 2. WORKPLACES’ FEATURES IN THE EUROPEAN UNION:

DESCRIBING THE SURVEY POPULATION

This chapter presents key information about the target population of the European

Digital Skills Survey. . In particular, the chapter describes the main characteristics of

the workplaces in terms of sector and size, organisational structure, type of ownership

and main market of reference. The characteristics of the workforce currently employed

in such workplaces are also analysed (looking at gender, age, level of education), with a

specific focus on the existing occupations in workplaces.

This information is then used in the remainder of the report, in combination with all the

information gathered, to present results on the type and level of required digital skills,

on existing digital skill gaps and on the action undertaken to tackle gaps in European

workplaces.

2.1 Profile of workplaces

The European Digital Skills Survey was carried out among a representative sample of

7,800 workplaces in six EU member states (Germany, Finland, United Kingdom,

Portugal, Sweden and Slovakia), which are statistically representative of 4,295,345

workplaces in the six countries as a whole, and of 13,803,113 workplaces in the whole

Europe Union (EU28). Such workplaces operate in 12 economic sectors with different

levels of digital intensity identified by the respective NACE 1-digit code:

A. Agriculture, forestry and fishing;

C. Manufacturing;

D. Electricity, gas, steam and air conditioning supply;

F. Construction;

G. Wholesale and retail trade; repair of motor vehicles and motorcycles;

H. Transportation and storage;

I. Accommodation and food service activities;

J. Information and communication;

M. Professional, scientific and technical activities;

N. Administrative and support service activities;

P. Education;

Q. Human health and social work activities.

Grouping such sectors for issues of representativeness in six macro-sectors, the figure

below (Figure 2.1) displays the distribution of workplaces by macro-sector in the EU28

as a whole.

32

The vast majority of the workplaces surveyed operate in the services; about six

workplaces out of ten belong to the macro-sectors ‘Commerce, transport and

accommodation’, ‘Information and communication’; ‘Professional, scientific and

technical activities’; ‘Administrative and support services’ and, ‘Education and human

health’. Around two workplaces out of ten are active in Agriculture, and roughly one

workplace out of ten in the Manufacturing sector and also in the Construction sector

(Figure 2.1).

Figure 2.1 – Workplaces by economic sector groupings, EU28 (%)

Q10. What is the main area of activity of your workplace? Note: totals from the European Digital Skills Survey correspond mathematically to those from Eurostat Structural Business Statistics, Business Demography Statistics and Farm Structure Statistics (2013) regarding the distribution of workplaces. As a result of the calculation of sample weights according to the calibration procedure as described in Annex One. Number of valid responses: 7,800 N=13,803,113 Source: European Digital Skills Survey (weighted values), Eurostat (2013)

22.2

9.2

10.4

33.6

17.0

7.6 A. Agriculture

CD. Manufacturing and utilities

F. Construction

GHI. Commerce, transport, accommodation andfood service

JMN. Information and communication;professional, scientific and technical activities;Administrative services

PQ. Education and human health

33

About eight out of ten workplaces in the EU28 employ between two to nine employees

and therefore fall in the category of micro-sized workplaces. A much smaller proportion

of workplaces (15%) in the selected sectors have 10 to 49 employees (categorized as

small-sized workplaces). Less than 4% of workplaces fall in either the medium or large

categories: less than 3% of workplaces are in the EU28 medium-sized (50 to 49

employees) and only 0.5% are large-sized (250 employees and more) (Figure 2.2).

Figure 2.2 – Workplaces by size (expressed as number of employees), EU28 (%)

Q6: In total and including yourself, approximately how many employees work in THIS workplace? Q7: Could you please give your best estimate using the following categories? (2-9, 10-49, 50-249, 250+) Note: totals from the European Digital Skills Survey correspond mathematically to those from Eurostat Structural Business Statistics, Business Demography Statistics and Farm Structure Statistics (2013) with regards to the distribution of workplaces, as a result of the calculation of sample weights according to the calibration procedure as described in Annex One. Number of valid responses: 7,800 N=13,803,113 Source: European Digital Skills Survey (weighted values), Eurostat (2013)

The six countries surveyed display proportions at country level which are roughly in line

with the EU average, with the remarkable exception of Germany, where only 62% of

establishments fall in the group 2-9 employees, 29% are classified as small and around

8% of workplaces (more than double than the EU28 average) have 50-249 employees.

Portugal displays a higher than average incidence of micro establishments (89%) and a

relatively smaller proportion of establishments falling in the 10-49 and 50-249

employee categories (9% and 2% respectively).

Detailed tables by sector and size, at country level and for the EU28 as a whole,

reporting both figures and percentages are displayed in Annex 2 (Tables A2.1 and

A2.2).

81.6

15.1

2.9 0.5

2-9

10-49

50 - 249

250 +

34

In terms of organisational structure, the vast majority (86%) of EU workplaces do not

belong to a wider group of workplaces, but are a single office/plant (Figure 2.3). This

breakdown is relatively consistent across the six sampled countries, although small

differences can be observed. These range from Portugal where 94% of the workplaces

are a single-office workplace, to Slovakia where the equivalent figure is 82%.

Figure 2.3 – Workplaces by type of organizational structure in sampled countries and EU28 (%)

Q2: Is your workplace one of many different workplaces belonging to the same organisation, or is it the only workplace your organisation has? Number of valid responses: 7,800 N=13,803,113 Source: European Digital Skills Survey (weighted values)

14.2

85.6

0.2

Workplaces belonging to a group Single office Don't know

35

Of the 14% of workplaces belonging to a wider group, almost 70% are the

headquarters of the group, while the rest are a subsidiary site (Figure 2.4). A marked

variability, mostly related to the average size of workplaces in selected sectors, is

displayed at country level (Figure A2.2 in Annex 2).

Figure 2.4 – Workplaces belonging to a group by role within the group in sampled countries and EU28 (%)

Q3: Is your workplace the headquarters of your organisation, or is it a subsidiary site? Number of valid responses: 1,893 N=1,979,446 Source: European Digital Skills Survey (weighted values)

69.5

28.9

1.6

Headquarters Subsidiary site Don't know

36

In terms of ownership (public or private-owned workplaces), at EU level the vast

majority of workplaces (almost 95%) are private sector (Figure 2.5). At country level

this breakdown does not vary significantly from the EU average. It is important to point

out that these figures reflect the exclusion from the survey’s population, as mentioned

in section 1.7 and 2.1 and extensively in the methodology, of workplaces belonging to

NACE sector O - Public administration and defence; compulsory social security, which

has a significant proportion of workplaces belonging to the public sector.

Figure 2.5 - Workplaces by sector (public/private) in sampled countries and EU28 (%)

Q9. A public sector organisation is either wholly owned by the public authorities or they own more than 50%. Is your workplace part of…(the private sector/the public sector/don’t know) Number of valid responses: 7,800 N=13,803,113 Source: European Digital Skills Survey (weighted values)

4.5

94.7

0.8

Public sector Private sector Don't know

37

The survey identifies the main market of reference for EU28 workplaces. The majority

operate or trade in local and regional markets (65%) and to a lesser extent in national

and international markets (21% and 13% respectively) (Figure 2.6).

Figure 2.6 - Workplaces by main market of reference, in sampled countries and EU28 (%)

Q11. Which of the following markets is most important for the main activity of your workplace? Number of valid responses: 7,800 N=13,803,113 Source: European Digital Skills Survey (weighted values)

The data shows a correlation between market of reference and size and sector of

operation. In particular, micro-sized workplaces are more frequently operating in local

and regional markets (40% and 31%) while small and medium-sized workplaces are

more frequently active in national (28% each) and international markets (27% and

26% respectively). Large-sized workplaces are most likely to operate at international

level (40%) (Table A2.3 in Annex 2).

Results at country level are broadly in line with the EU figure, with the exception of

Sweden, with two thirds of workplaces operating in the local market (66%) and

relatively smaller proportions of workplaces trading in national and international

markets. Workplaces in Slovakia, Germany and Portugal trade more frequently in the

national market, while in Germany and Portugal much higher proportions of workplaces

trade internationally.

2.2 Profile of workforce

The 13,803,113 European workplaces (EU28) described above employ a total of

150,563,540 employees. Gender, age, level of education and occupational composition

of the workforce is explored below.

36.6

29.0

21.3

13.0

0.2

Local Regional National International Don't Know

38

Overall around 39% of employees are women, 20% are older than 50 years of age and

19% are younger than 30 years of age. Over a quarter of employees (27%) hold a

university degree (Figure 2.7).

Figure 2.7 – Employees by specific characteristics in workplaces in the EU by sector and

size (% of total employees)

Q8: Could you please indicate, for this workplace, the number or percentage of employees who…(are female/have a university degree, are younger than 30 years of age/are older than 50 years of age) Number of valid responses: 6,917 (female rate); 6,334 (university degree rate); 6,114 (younger than 30 rate); 6,245 (older than 30 rate) N=12,269,195 (female rate); N=11,596,077 (university degree rate); N=11,365,645 (younger than 30 rate); N=11,829,617 (older than 30 rate) Source: European Digital Skills Survey (weighted values)

Workplaces in the ‘Education and human health’ macro-sector (56%) and in large-sized

workplaces (44%) have the highest proportion of female employees while the lowest

proportions of female employees are reported in the agricultural sector (4%) and in

micro-sized establishments (33%).

The highest proportion of younger employees is reported in the macro-sector

‘Commerce, transportation, accommodation and food service activities’ (22%) and in

small and medium-sized workplaces (21%). Agriculture has the lowest proportions of

younger employees (1%) and in both micro and large-sized establishments (17% in

both cases). Employees older than 50 are more strongly represented in the construction

sector (25%) and in small-sized workplaces (25%), and less well represented in

agriculture (11%) and in medium-sized establishments.

The proportion of employees with a university degree is highest in the ‘Information and

communication’ sector; ‘Professional, scientific and technical activities’; ‘Administrative

and support service activities’ (39%) and ‘Education and human health’ (39%).

Employees with a university degree are also better represented in medium-sized

establishments (32%), and lower in the agricultural sector (9%) and in medium-sized

establishments (32%) (see Figure A2.5, Annex 2).

38.9

26.8

18.9 20.5

Female employees Employees with auniversity degree

Employees younger than30 years of age

Employees older than 50years of age

39

The composition of the workforce in workplaces varies significantly at the country level.

Germany has the highest diversity among employees working in the sectors covered by

the survey, as shown by the high proportion of female employees (46%), of older and

younger employees (34% and 35% respectively) and the highest proportion of

employees with a university degree (44%). Slovakia has the highest overall incidence of

female employees (47%).

Portugal displays a comparatively smaller proportion of female workers (29%), older

and younger workers (11% and 12% respectively). Slovakia, Finland and Portugal have

the lowest proportion of employees with a university degree (22%, 18% and 20%

respectively) (Figure A2.6 and Table A2.4, Annex 2).

Focusing on the occupational structure of the workforce, at the EU level, 41% of

workplaces employ workers in the ‘managers’ category, 30% employ ‘clerical support

workers’, 27% employ ‘sales, customer or personal service workers’, and 23%

‘professionals’. Smaller proportions of workplaces employ workers in the categories of

‘building, craft and related trade workers’ (18%), ‘technicians’ (14%), and ‘skilled

agricultural, forestry and fishery workers’ (12%). Workers in the ‘plant machine

operators and assemblers’ and ‘elementary occupations’ occupational categories are

employed by 7% and 8% respectively of workplaces in the sectors selected for this

study at EU level (Figure 2.8).

Figure 2.8 – Workplaces employing workers in specific occupations in the European Union (%)

Q12: Does your workplace have any employees in any of the following job categories? Number of valid responses: 7,800 N=13,803,113 Source: European Digital Skills Survey (weighted values)

From a sectoral viewpoint, managers are relatively more common in the ‘commerce,

transport, accommodation and food service activities’ sectors. Professionals are more

frequently employed by workplaces active in the ‘education and human health’ macro-

40.8

23.0

14.0

29.9

27.2

12.3

17.7

7.4

8.2

Managers

Professionals

Technicians and associate professionals

Clerical support workers

Sales, customer or personal service workers

Skilled agricultural, forestry and fishery workers

Building, craft and related trade workers

Plant and machine operators and assemblers

Elementary occupations

40

sector. Technicians are more often employed by workplaces in the ‘information and

communication, professional, scientific and technical activities, administrative and

support service activities’ macro-sectors. ‘Clerical workers’ are more common in the

‘manufacturing and utilities’ macro-sector. Furthermore, workplaces employing workers

as ‘sales workers’ are more common in the ‘commerce, transportation, accommodation

and food service activities’ sectors, and those employing ‘skilled agricultural workers’

are active mostly in agriculture. Workplaces with ‘building workers’ are concentrated in

the construction sector and those employing ‘plant machine operators’ fall in the

‘manufacturing and utilities’ macro-sector. Finally, workplaces employing workers in the

‘elementary occupations’ are concentrated in the ‘education and human health’ macro-

sector (Table A2.5, Annex 2).

Of the 150,563,540 total employees in EU28 workplaces, 7.5 million (or 5%) work as

managers. Workers employed as ‘professionals’ in the workplaces covered by this

survey number 28.5 million (or 19%). Technicians number 25.5 million (17% of total

employees), sales workers number 26 million (17%), clerical workers account for 17

million (11%), and skilled agricultural workers total 1.5 million (1% of employees).

Finally, the survey identified about 17 million building workers (11%), 12 million plant

machine operators (8%) and workers employed in elementary occupations account for

15.5 million workers (10%). Skilled agricultural workers represent the smallest category

of workers (Table 2.1).

Table 2.1 - Employees in specific occupations, EU28 (total number and % of employees, and average number of workers in specific occupation per workplace)

Q13. Could you please indicate approximately how many employees your workplace has in these job categories? Q14. Could you please provide your best estimate of the approximate percentage of employees in your workplace in these job categories? Note: totals from the European Digital Skills Survey correspond mathematically to those from Eurostat Labour Force Survey (2015) with regards to the distribution of employees, as a result of the calculation of sample weights according to the calibration procedure as described in Annex 1. Number of valid responses: 7,800 N=13,803,113 Source: European Digital Skills Survey (weighted values), Eurostat (2015)

Occupations N %

Managers 7,564,363 5.0

Professionals 28,452,941 18.9

Technicians 25,519,514 16.9

Clerical workers 16,921,402 11.2

Sales workers 25,923,570 17.2

Skilled agricultural workers 1,526,330 1.0

Building workers 16,887,614 11.2

Plant machine operators 12,206,673 8.1

Elementary occupations 15,561,133 10.3

Total number of employees 150,563,540 100.0

Total number of workplaces 13,803,113

41

Looking at job categories by sector, a higher proportion of workers are employed as

skilled agricultural workers (46%) in the agricultural sector, similarly there are higher

proportions of technicians in the manufacturing sector (37%), building workers in the

construction sector (48%), sales workers in macro-sector ‘commerce, transportation,

accommodation and food services activities’ (31%) and of professionals in macro-

sectors ‘information and communication, professional, scientific and technical activities,

administrative and support service activities (28%) and ‘education and human health’

(42%) (Table A2.8, Annex 2).

At a country level, employees working as managers represent comparatively the largest

group in the UK (10%). Sweden has the highest proportion of professionals (26%), and

technicians and clerical workers are more commonly employed in Germany (23% and

14% respectively). Sweden also employs the highest proportion of sales workers

(21%). Skilled agricultural workers are more commonly employed in Portugal (3%),

while building workers and plant machine operators are prevalent in Slovakia (14% and

17%). Overall, elementary occupations have the highest incidence in Portugal (12%)

compared to the other five countries (Table A2.9, Annex 2).

2.3 Summary

This section has described the contextual data from the survey. This analysis will be

used as a basis upon which to address the research questions in the following chapters.

The main points presented in this section are:

The survey was conducted on a sample of 7,800 workplaces in six EU member

states (Germany, Finland, United Kingdom, Portugal, Sweden and Slovakia);

The survey and its findings are representative of 13,803,113 workplaces in 12

economic sectors across the EU;

The sectors of reference are agriculture; manufacturing; electricity and gas supply;

construction; wholesale and retail trade, repair of motor vehicles and motorcycles;

transportation and storage; accommodation and food service activities; information

and communication; professional, scientific and technical activities; administrative

and support service activities; education; human health and social work activities;

The vast majority of the workplaces represented are micro-sized enterprises (more

than 80%), with those remaining being mainly small enterprises (16%); only 0.5%

are large enterprises;

Workplaces belong mainly to the private sector (90%) and are not part of a group

of companies (85%);

The main reference market for these workplaces is the local or regional market

(65%), while a more limited proportion of workplaces operate or trade at national

level (about 21%) and only 13% at international level;

The sample of workplaces employ 150,563,540 employees; less than 40% of them

are women, 27% hold a university degree, about 19% are younger than 30 years of

age and 20% are older than 50 years of age;

Employees are classified in 9 occupational categories identified by the ISCO 1-digit

code: ‘managers’, ‘professionals’, ‘technicians and associate professionals’, ‘clerical

support workers’, ‘sales, customer or personal service workers’, ‘skilled agricultural,

forestry and fishery workers’, ‘building, craft and related trade workers’, ‘plant and

machine operators and assemblers’, ‘elementary occupations’.

42

Of the total number of employees, only 5% are employed as managers, while the

majority of employees work as professionals (19%), technicians (17%) and sales

workers (17%). Smaller proportions of employees work as clerical workers or

building workers (11% in both cases), or in the category of elementary occupations

(10%). Workers employed as plant machine operators represent 8% of total

employees and skilled agricultural workers are 1% of the total;

The breakdown by sector displays a number of sector-specific occupations;

similarly, variations exists across countries;

The analyses have been presented in their breakdown by country wherever possible

and related tables or charts are reported in Annex 2.

43

CHAPTER 3. ICT AND DIGITAL TECHNOLOGIES FOR WORK

As seen in the introductory Chapter 1, the research literature identifies digital

technologies as one of the main drivers of the profound transformation that the labour

market is undergoing in the vast majority of the economies in both the European Union

and in the other most advanced economies. According to several authors, different

types of digital technologies have become increasingly common across all economic

sectors and in most types of organisations.

In order to have a more complete picture of the degree of penetration of digital

technologies in the economy and in the workplace, the European Digital Skills Survey

investigated the use of information and communication technologies (ICT) and specific

digital devices in European workplaces across the 12 economic sectors of interest. The

survey also aimed to investigate the investment strategy in digital technology that

workplaces have undertaken recently, or are planning for the near future. With

reference to the use of ICT, the aim of the survey was to quantify how many

workplaces in the European Union currently use personal computers connected (or not)

to the internet or to an intranet, nettops, portable computers (e.g. laptops, notebooks,

netbooks, tablets) and/or other portable devices (e.g. smartphones, Personal Digital

Assistant (PDA), GPS navigator) and also CNC (Computer Numerical Control) machines

or tools, which are operated by precisely programmed commands encoded on a storage

medium, and programmable robots.

Overall, findings display a very high penetration of digital technologies in workplaces,

regardless of the sector or the size, which is in line with the literature. More than nine

out of ten workplaces in the European Union are currently using desktop computers or

broadband to access the internet, three-quarters are using portable computers, and six

out of ten use other digital portable devices.

3.1 Digital technologies in European workplaces

The vast majority of European workplaces use desktop computers (93%), broadband

technology to access the internet (94%)13, portable computers (75%) and other

portable devices (as defined above) (63%). Much smaller proportions of workplaces use

an intranet platform (22%), CNC machine or tools (8%) or programmable robots (5%)

(Figure 3.1).

13 This finding is in line with the most recent figure released by Eurostat for 2015 on the access to broadband to access the internet (95%) within the Eurostat ‘ICT usage in enterprises’ survey http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_ci_it_en2&lang=en

44

Figure 3.1 – Workplaces by use of computers and other digital devices by type of

device, EU28 (%)

Q15. Please indicate if your workplace currently uses computers, CNC machines or tools, and other digital devices to carry out its main business activity. By digital device an electronic device which uses discrete, numerable data and processes for all its operations should be meant. Number of valid responses: 7,800 N=13,803,113 Source: European Digital Skills Survey (weighted values)

A breakdown of the use of types of digital technologies in different economic sectors

can be found in Table A3.1 in Annex 2. Desktop computers are extensively used by

most workplaces regardless of the sector. Nevertheless, workplaces operating in the

‘Education and human health and social work activities’ macro-sector have the highest

incidence in the use of computers (97.9%). This figure should however be treated with

caution, as most of the workplaces that responded to the survey in this macro-sector

are privately-owned (82.5% of the total), while in most countries workplaces in this

sector are mainly publicly-owned. This is important as the use of computers tends to be

less frequent among workplaces in the public sector, with only 1.9% of privately-owned

workplaces in the ‘Education and human health sector’ not using computers, versus

6.7% of those which are publicly-owned.

The use of the broadband to access the internet is reported by almost the totality of

workplaces in the Agriculture sector14, while the use of portable computers, portable

devices and intranet platforms is most common among workplaces in the macro-sector

‘Information and communication, Professional, scientific and technical activities and

Administrative and support service activities’ (85.2%, 79.9% and 37.4% respectively).

The use of CNC machinery and tools, on the other hand, seems to be concentrated

mostly in the ‘Manufacturing and utilities’ macro-sector (21.4%) and to a lesser extent

14 This figure could actually be biased due to the expected prevalence of respondents with a valid email address.

92.7

75.3

63.3

93.6

22.5

7.8

5.2

Desktop computers

Portable computers

Other portable devices

Broadband technology to access the Internet

Intranet platform

CNC machines or tools

Programmable robots

45

in Agriculture (12.4%). The use of programmable robots is concentrated in the

Agriculture (12.3%) and ‘Manufacturing and utilities’ (8.4%) sectors.

While the figures related to the Manufacturing sector are backed by a considerable

corpus of evidence and literature, the findings about the Agricultural sector (Table A3.1,

Annex 2), which could appear as counterintuitive, are actually in line with most recent

evidence15, as also evidenced in the wider study of which the European Digital Skills

Survey is a part16. Relevant stakeholders confirm that the sector is increasingly turning

to digital technologies for agricultural purposes, with changes in the production

systems, productivity and outlook of traditional farming. Digital technologies are used

as well to respond to other management requirements, for example to access the social

security systems for employees, as in many countries these systems have been

digitalised and farmers need ICT skills to employ workers (e.g. seasonal workers)17.

The analysis by size shows a clear correlation between workplace size and use of

desktop computers, portable computers and other portable devices: the use of digital

technologies increases according to the size of the workplaces, with large-sized

workplaces reporting the highest use of all the digital technologies listed (Table A3.1,

Annex 2). The analysis by country shows that Germany leads in the use of desktop

computers (although all other countries with the exception of Sweden have similar

levels), of intranet platforms and CNC machine or tools. Finland leads in the use of

portable computers and other portable devices, in the use of broadband to access the

internet and in the use of programmable robots. Portugal is the country with the lowest

use of portable computers, other portable devices, and intranet platforms. Sweden

displays the lowest incidence of use of computers, but the second highest incidence in

the use of portable computers and of other portable devices (Figure A3.1, Annex 2).

3.2 Recent trends in the use of digital technologies

The use of digital technologies in the last five years has increased in almost nine

workplaces out of ten in the EU28, with less than 10% of workplaces reporting that

there has not been any increase (Figure 3.2). In addition, seven out of ten workplaces

anticipate that the use of digital technology will increase in the next five years; one

15 For example: European Commission, ERANET, 2010. ' Coordination of European Research within ICT and Robotics in Agriculture and related Environmental Issues'. http://cordis.europa.eu/fp7/coordination/docs/ictagri_en.pdf; European Commission and rural development, 2011. 'Structural development in EU agriculture- Brief N° 3 – September 2011'. http://ec.europa.eu/agriculture/rural-areaeconomics/briefs/pdf/03_en.pdf; Ict in Agriculture. 2012. ' Overview of ICT in Agriculture: Opportunities, Access, and Cross-Cutting Themes'. http://www.ictinagriculture.org/sites/ictinagriculture.org/files/final_Module1.pdf; ICT AGRI, 2015. 'ICT-AGRI 2015 Action Plan for implementation of the Strategic Research Agenda with focus on Precision Agriculture. http://ict-agri.eu/sites/ictagri.eu/files/ICT-AGRI_2015_Action_Plan_0.pdf; ICT AGRI, 2014. 'Precision Agriculture: An opportunity for EU Farmers'. http://ictagri.eu/node/14013 JP, 2015: (translated) 'Farmers tired of milking robot go back to milk with their hands'. Jyllands-Posten 11 September 2015. http://jyllandsposten.dk/ECE8012845/Tr%C3%A6t+af+robotter%3A+Flere+landm%C3%A6nd+malker+med+h%C3%A6nderne/ Stienen, Jac; Bruinsma, Wietse & Neuman, Frans, International Institute for Communication and Development (IICD) 2007. 'How ICT can make a difference in agricultural livelihoods'. 16 https://ec.europa.eu/digital-single-market/en/news/report-shows-digital-skills-are-required-across-all-types-work-also-jobs-outside-office 17 Information reported by COPA-COGECA, the European association of farmers and agri-cooperatives represented by Mrs Federica Zolla, at the experts’ workshop which took place on the 7th of October 2016 in Brussels.

46

quarter however think that there will not be any increase in the use of ICT in the near

future, and 6.5% do not know.

Figure 3.2 - Workplaces by trends and importance in the use of ICT in the last and in

next five years, EU28 (%)

Q31. Thinking about your workplace as a whole, would you say (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), that the use of ICT in your workplace…? Number of valid responses: 7,773

N=13,763,547 Source: European Digital Skills Survey (weighted values)

Workplaces in ‘Education and human health’ and ‘Information and communication,

Professional, scientific and technical activities, Administration support activities’ sectors

report more frequently an increase in the use of digital technologies (96% and 97%

respectively). Micro-sized workplaces (10%) are more likely to report no increase at all

in the use of ICT in the last five years compared to workplaces of a larger size. 94% of

small and medium-sized workplaces, and 95% of large workplaces, report a significant

increase in the use of ICT in the recent past (Table A3.2, Annex 2).

The logistic regression model18 below (Figure 3.3) allows the identification of additional

characteristics of the workplace that correlate to the probability that the use of ICT in

the workplace has increased in the last five years (the full model is reported in Table

A3.3 in Annex 2).

18 Logistic regression is used to investigate the effect of two or more independent (or predictor) variables on a two-category (binary) outcome variable. The independent variable can be continuous or categorical (grouped) variable. The parameter estimates from a logistic regression model for each independent variable give an estimate of the effect of that variable on the outcome variable, adjusted for all other independent variables in the model. Logistic regression models the log ‘odds’ of a binary outcome variable. The ‘odds’ of an outcome is the ratio of the probability of it occurring to the probability of it not occurring. The parameter estimates obtained from a logistic regression model have been presented as ‘odds ratios’ for ease of interpretation. Reference categories (reported as ‘omitted’ categories in the subsequent models) are usually chosen on the basis of being the most numerous or suitable category to compare everything against.

9.8

25.0

87.1

68.5

3.1

6.5

The use ofICT has

increased inthe last 5

years

The use ofICT will

increase inthe next 5

years

Not at all Yes Don't know

47

Workplaces in which the use of ICT has increased in the last five years are more likely

to be part of an economic group (in particular being the headquarters) rather than

being a single workplace. In particular, analysing the odds ratios, it can be pointed out

the odds of the headquarters of a group and a subsidiary site reporting an increased

use of ICT in the last five years are 4.8 and 4.0 times more likely than those for a single

workplace (not belonging to a group).

Also, in line with the descriptive analysis above, the likelihood of reporting an increase

in the use of ICT is higher for the macro-sectors ‘Education and human health’ (3.5

times more likely than the agricultural sector) and ‘Information and

communication/Professional, scientific and technical activities/Administrative and

support service activities’ (2.9 times more likely than the agricultural sector).

Workplaces with a higher incidence of female employees are more likely to report an

increase in the use of ICT: compared to workplaces with less than 26% female staff, the

odds of having introduced digital technologies in the last five years are 1.3 times higher

for workplaces where women represent between 26% and 50% of the workforce and

1.4 times higher for 1.4 times for those with a share of women between 51% to 75% of

the total workforce, and 1.3 times more likely for those employing more than 75%

female staff. . While these odds ratios suggest the existence of a positive correlation

between the share of female staff and the use of digital technologies, further research is

needed to identify characteristics of such correlation.

Workplaces employing highly educated staff are more likely to have introduced digital

technologies: workplaces with 75% or more employees holding a university degree are

1.3 times more likely than those with less than one quarter of highly educated

employees to report an increase in the use of ICT in the last five years.

Also workplaces active on the national market are more likely to report an increase in

the use of digital technology compared to workplaces operating mostly at local level (2

times more likely).

On the other hand, lower odds compared to the reference group were seen for

workplaces with over 26% older employees (compared to those with a smaller

proportion), or over 26% younger employees (compared to those with a smaller

proportion), , workplaces in the public sector (compared to those in the private sector)

and those active on the regional market compared to those in the local market.

48

Figure 3.3 – Logistic regression: probability that the use of ICT in the workplace has

increased in the last five years (Odds ratio).

* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; J,M,N:

Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; P,Q: Education and human health and social work activities. Q31. Thinking about your workplace as a whole, would you say (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), that the use of ICT in your workplace…? Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Note: The regressors ‘Female rate’, ‘University rate’, ‘Young rate’. ‘Old rate’ have four categories, reflecting the proportion of female, university degree holders, younger than 30, older than 50 workers of the total workforce. The four categories are as follows: between 0% and 25%, 26% to 50%, 51% to 75%, more than 75%. Source: elaboration on European Digital Skills Survey

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Only workplace (omitted)

Headquarters

Subsidiary_site

size < 10 (omitted)

size10_49

size50_249

size250

sectorA (omitted)

sectorCD

sectorF

sectorGHI

sectorJMN

sectorPQ

fem_rate<26 (omitted)

fem_rate_26_50

fem_rate_51_75

fem_rate_75

univ_rate<26 (omitted)

univ_rate_26_50

univ_rate_51_75

univ_rate_75

young_rate<26 (omitted)

young_rate_26_50

young_rate_51_75

young_rate_75

old_rate<26 (omitted)

old_rate_26_50

old_rate_51_75

old_rate_75

private (omitted)

public

Local_market (omitted)

Regional_market

National_market

International_market

Wo

rkp

lace

typ

eW

ork

pla

ce s

ize

Sect

or

Fem

ale

rate

Un

iver

sity

rat

eYo

un

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teO

ld r

ate

Ow

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sh

ip (

q9

)M

arke

ts (

q11

)

49

In terms of future trends, an increase in the use of ICT is foreseen by 75% of EU28

workplaces, with a higher incidence in the ‘Education and human health’ (85%),

‘Manufacturing and utilities’ (83%), ‘Information and communication/Professional,

scientific and technical activities/Administrative and support service activities’ (80%),

and ‘Construction’ (80%) macro-sectors. While only a limited proportion of workplaces

at EU level reported no increase in the use of ICT in the last five years (around 10%),

workplaces appear more cautious when reporting the expected increase over the next

five years; a quarter of workplaces expect an increase in the use of ICT in the next five

years, with an important degree of variability among sectors. Medium and large-sized

workplaces are more positive regarding their likely increase in the use of ICT compared

to micro-sized workplaces (Table A3.2, Annex 2).

3.3 Investment strategies in ICT

The survey also investigated the investment strategy of European workplaces in digital

technologies in 11 areas related to the improvement of management and business

volume. These areas are listed in Table 3.3 below (right column). For the purposes of

the analysis, they have however been clustered into four meaningful synthetic

indicators related to the main areas of investment identified as ‘improving overall

efficiency’, ‘marketing and sales’, ‘internal organisation’, and ‘delocalisation strategy’

(reported in the left column).

Table 3.3 – Areas of investment in ICT

Area of investment in ICT Specific topic

Improving overall efficiency Improving overall efficiency

Marketing and sales Improving quality of existing products and services

Launching new marketing methods Engaging customers, users, suppliers or other companies

to improve or create products or services Tracking and analysing data from business processes, customers, and transactions to improve or create products or services.

Internal organisation Making the production process leaner Improving work organization or working procedures

Making work easier and less stressful for employees

Delocalisation strategy Delocalising the production of goods or services within the country Delocalising the production of goods or services abroad

Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not

at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree

has your workplace invested in ICT for…? (Please select all that apply)

Source: Elaboration on European Digital Skills Survey

As displayed in Figure 3.4 below, workplaces in the EU28 have mostly invested in ICT in

the past five years with the aim of improving overall efficiency. Almost seven

workplaces out of ten report this as the main important area of investment in digital

technology. Furthermore, a majority of workplaces (56%) report investments in digital

technology in the area of marketing and sales, while less than half of workplaces

invested in ICT to improve the internal organisation. Only 31% of European workplaces

invested in digital technology in line with a delocalisation strategy. This figure is in line

with the data presented in Figure 2.6 in Chapter 2, according to which the vast majority

50

of workplaces operate or trade in local or regional markets, while only a much smaller

proportion have national and international markets as the main markets of reference.

Figure 3.4 - Workplaces by area and importance of investment in ICT in the past five

years, UE28 (%)

Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has your workplace invested in ICT for…? (Please select all that apply) Note: the levels of importance of investment have been recoded in a dichotomic variable (No/Yes) Number of valid responses: 7,773 N=13,763,547 Source: European Digital Skills Survey (weighted data)

Workplaces in the macro-sectors ‘Information and communication/Professional,

scientific and technical activities/Administrative and support service activities’,

‘Education and human health’ and ‘Commerce, transport, accommodation and food

service’ are those which invested most in ICT to improve the overall efficiency in the

past five years (80%, 75% and 70% respectively). Investments in digital technologies

in the area of marketing and sales and internal organisation are reported comparatively

more by workplaces in the macro-sectors ‘Information and communication/Professional,

scientific and technical activities/Administrative and support service activities’ and

‘Manufacturing and utilities’ (70% and 62% for marketing and sales and 60% and 54%

for internal organisation, respectively). Finally, Agriculture and ‘Information and

communication/Professional, scientific and technical activities/Administrative and

support service activities’ are the macro-sectors with the highest relative share of

workplaces which invested in ICT in line with a delocalisation strategy (42% and 38%

respectively) (Figure A3.4, Annex 2). Medium-sized and large workplaces invested

comparatively more in digital technologies in all the areas. Micro-sized workplaces (2-9

employees) refer more frequently a lack of investment in ICT in all the areas, compared

to larger-sized workplaces. In particular, 24% of micro-sized workplaces have not

invested at all in the last five years in ICT with the aim of improving the overall

efficiency, 35% have not invested at all in ICT in the area of marketing and sales, 40%

21.7

32.2

37.0

56.4

68.1

55.9

49.5

31.4

10.2

11.9

13.5

12.2

Overall efficiency

Marketing and sales

Internal organisation

Delocalisation

No Yes Don't know

51

report a lack of investment in ICT related to internal organisation.Finally, 59% have not

invested at all in ICT in relation to a delocalisation strategy (Table A3.5, Annex 2).

Four logistic regression models have been carried out to identify the characteristics of

workplaces that are more likely to invest in digital technologies in each of the areas.

The first model displayed in Figure 3.5 (the full model is reported in Table A3.6 in

Annex 2) has been calculated with reference to the likelihood of investing in ICT to

improve overall efficiency. In this case, if the workplace belongs to a group, the

likelihood of having invested in ICT to improve overall efficiency is higher than for single

workplaces. More in detail, the odds that headquarters and subsidiary sites have

invested in digital technologies to improve overall efficiency are respectively 7.8 and 5.3

times higher than those for single workplaces. In terms of sector, workplaces operating

in the macro-sectors ‘Education and human health’ or ‘Information and

communication/Professional, scientific and technical activities/Administrative and

support service activities’, have odds of investing in ICT to prove efficiency 5 and 5.1

times higher than those in ‘agriculture’. This is also the case – although to a lesser

extent – of workplaces in the macro-sectors ‘Manufacturing and utilities’, ‘Construction’

or ‘Wholesale and retail trade, repair of motor vehicles and motorcycles/Transportation

and storage/Accommodation and food service activities’ (odds which are respectively

2.3, 2.5 and 3.4 times higher than those in the agricultural sector). Similarly,

workplaces employing more than one quarter of employees holding a university degree

are more likely to have invested in ICT to improve efficiency (odds between 2.1 and 2.3

higher than those with a quarter or less).

The model also shows that workplaces active in the regional, national and especially

international markets are more likely to have invested in digital technologies to improve

the overall efficiency. In particular, workplaces operating in international markets and

those operating mostly at national level are respectively 4.2 and 3 times more likely

than workplaces active locally to have invested in ICT to improve the overall efficiency.

Finally, the regression analysis shows that the demographic characteristics of the

employees can have a weak negative correlation with investment in ICT to improve the

overall efficiency. Workplaces with more than 26% of female staff or staff over 50, and

those with more than 50% of employees aged under 30, are slightly less likely to have

invested in digital technologies to improve overall efficiency.

52

Figure 3.5 – Logistic regression: probability that workplace has invested in ICT to

improve the overall efficiency in the last five years (Odds ratios).

* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities;

J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; P,Q: Education and human health and social work activities. Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has your workplace invested in ICT for…? (Please select all that apply) Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Note: The regressors ‘Female rate’, ‘University rate’, ‘Young rate’. ‘Old rate’ have four categories, reflecting the proportion of female, university degree holders, younger than 30, older than 50 workers of the total workforce. The four categories are as follows: between 0% and 25%, 26% to 50%, 51% to 75%, more than 75%. Source: Elaboration on European Digital Skills Survey (weighted values)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8

Only workplace (omitted)

Headquarters

Subsidiary_site

size < 10 (omitted)

size10_49

size50_249

size250

sectorA (omitted)

sectorCD

sectorF

sectorGHI

sectorJMN

sectorPQ

fem_rate<26 (omitted)

fem_rate_26_50

fem_rate_51_75

fem_rate_75

univ_rate<26 (omitted)

univ_rate_26_50

univ_rate_51_75

univ_rate_75

young_rate<26 (omitted)

young_rate_26_50

young_rate_51_75

young_rate_75

old_rate<26 (omitted)

old_rate_26_50

old_rate_51_75

old_rate_75

private (omitted)

public

Local_market (omitted)

Regional_market

National_market

International_market

Wo

rkp

lace

typ

eW

ork

pla

cesi

zeSe

cto

rFe

mal

e ra

teU

niv

ersi

tyra

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ld r

ate

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ship

(q9

)M

arke

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q1

1)

53

The second logistic model is displayed in Figure 3.6 (the full model is reported in Table

A3.8, Annex 2) and has been calculated with reference to the likelihood of investing in

digital technologies in the area of marketing and sales. This model shows that belonging

to a group as headquarters increases significantly (5.9 times) the odds of having

invested in digital technologies for marketing and sales compared to single workplaces.

Small and medium-sized workplaces are respectively 1.5 and 1.7 times more likely than

micro-enterprises to have invested in ICT in the area of marketing and sales. Also,

workplaces operating in particular in the macro-sectors ‘Information and

communication/Professional, scientific and technical activities/Administrative and

support service activities’ and ‘Manufacturing and utilities’ display higher odds (3 and

2.1 times respectively) of having invested in ICT in the area of marketing and sales

compared to workplaces in the agricultural sector. This is also the case – although to a

lesser extent – of workplace in ‘Education and human health’, ‘Construction’ and

‘Wholesale and retail trade/Repair of motor vehicles and motorcycles/Transportation

and storage, Accommodation and food service activities’ sectors, display respectively

1.6, 2 and 1.5 times higher odds than the agricultural sector to have invested in ICT in

the area of marketing and sales.

Workplaces employing high proportions of employees holding a university degree, are

more likely to have invested in digital technologies for marketing and sales: in

particular, for workplaces with more than three quarters of staff holding a university

degree the likelihood of investing in ICT for marketing and sales is 2.6 times higher

compared to workplaces with less than 26% of highly-educated staff. It is also worth

pointing out that the regression model displays a strong probabilistic correlation

between the market of reference and the investment in ICT for marketing and sales.

Workplaces active in the regional, and in particular in the national and international

markets, have odds that are higher (2, 4.6 and 5.1 times respectively) than those for

workplaces operating mostly locally in terms of having invested in ICT for marketing

and sales. Conversely, workplaces with a proportion of more than 75% of female

employees, or those with 26-75% of older employees, are less likely to have invested in

ICT in the area of marketing and sales.

54

Figure 3.6 – Logistic regression: probability that workplace has invested in ICT in the

area of marketing and sales in the last five years (Odds ratios).

* A: Agriculture; CD: Manufacturing and utilities; F: Construction; GHI: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; JMN: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; PQ: Education and human health and social work activities. Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has your workplace invested in ICT for…? (Please select all that apply) Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Note: The regressors ‘Female rate’, ‘University rate’, ‘Young rate’. ‘Old rate’ have four categories, reflecting the proportion of female, university degree holders, younger than 30, older than 50 workers of the total workforce. The four categories are as follows: between 0% and 25%, 26% to 50%, 51% to 75%, more than 75%. Source: Elaboration on European Digital Skills Survey (weighted values)

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6

Only workplace (omitted)

Headquarters

Subsidiary_site

size < 10 (omitted)

size10_49

size50_249

size250

sectorA (omitted)

sectorCD

sectorF

sectorGHI

sectorJMN

sectorPQ

fem_rate<26 (omitted)

fem_rate_26_50

fem_rate_51_75

fem_rate_75

univ_rate<26 (omitted)

univ_rate_26_50

univ_rate_51_75

univ_rate_75

young_rate<26 (omitted)

young_rate_26_50

young_rate_51_75

young_rate_75

old_rate<26 (omitted)

old_rate_26_50

old_rate_51_75

old_rate_75

private (omitted)

public

Local_market (omitted)

Regional_market

National_market

International_market

Wo

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q9

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q11

)

55

The third logistic model in Figure 3.7 (full model in Table A3.9 in Annex 2) displays

calculations with reference to the likelihood of investing in ICT to improve internal

organisation. Also in this case workplaces that are the headquarters of a group, or are

small and medium-sized workplaces, have higher odds of having invested in ICT to

improve internal organisation than their reference group. Headquarters have odds 6

times higher than single workplaces in terms of having invested in ICT to improve the

internal organisation. More modest effects are displayed in relation to the size: small

and medium-sized workplaces have odds that are 1.2 and 1.9 times higher than micro-

sized workplaces for the same issue. Furthermore, workplaces in the macro-sectors

‘Information and communication/Professional, scientific and technical

activities/Administrative and support service activities’, ‘Education and human health’

and ‘Wholesale and retail trade/Repair of motor vehicles and

motorcycles/Transportation and storage/Accommodation and food service activities’

manifest higher odds (respectively 2.4, 2.1 and 2 times) of having invested in digital

technology to improve internal organisation compared to workplaces in the agricultural

sector. This is the case also of workplaces operating in the ‘Manufacturing and utilities’

and ‘Construction’, which are respectively 1.7 and 1.4 times more likely than

workplaces in the agricultural sector to have invested in ICT for the internal

organisation. Also workplaces active in the regional, national and international markets

are 2 , 3.5 and 3.3 times (respectively) more likely – compared to those operating

mostly at local level - to have invested in ICT for internal organisation. Also, employing

high proportions of employees holding a university degree has a positive correlation

with the investment in ICT to improve internal organisation in the last five years as

indicated by the odds ratios calculated for workplaces employing between 26% and

50% of highly-educated staff (1.7 times more likely), for workplaces employing 51% to

75% of staff holding a university degree (1.3 times more likely) and for workplaces

employing more than 75% of employees with a university degree (1.4 times more

likely).

On the contrary, odds to have invested in ICT to improve internal organisation

were smaller for workplaces with a proportion of more than 75% of female (0.8

times in comparison to those with 25% or less), younger employees (0.8 times

for those with 75% or more in comparison to those with 25% or less), those

with 25-75% of older employees (0.7 times compared to those with less than

26% older employees), or those belonging to the public sector (0.7 times in

comparison to those that are not),.

56

Figure 3.7 – Logistic regression: probability that workplace has invested in ICT to

improve internal organisation in the last five years (Odds ratios).

* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; P,Q: Education and human health and social work activities. Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has your workplace invested in ICT for…? (Please select all that apply) Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Note: The regressors ‘Female rate’, ‘University rate’, ‘Young rate’. ‘Old rate’ have four categories, reflecting the proportion

of female, university degree holders, younger than 30, older than 50 workers of the total workforce. The four categories are

as follows: between 0% and 25%, 26% to 50%, 51% to 75%, more than 75%.

Source: Elaboration on European Digital Skills Survey (weighted values)

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6

Only workplace (omitted)

Headquarters

Subsidiary_site

size < 10 (omitted)

size10_49

size50_249

size250

sectorA (omitted)

sectorCD

sectorF

sectorGHI

sectorJMN

sectorPQ

fem_rate<26 (omitted)

fem_rate_26_50

fem_rate_51_75

fem_rate_75

univ_rate<26 (omitted)

univ_rate_26_50

univ_rate_51_75

univ_rate_75

young_rate<26 (omitted)

young_rate_26_50

young_rate_51_75

young_rate_75

old_rate<26 (omitted)

old_rate_26_50

old_rate_51_75

old_rate_75

private (omitted)

public

Local_market (omitted)

Regional_market

National_market

International_market

Wo

rkp

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typ

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pla

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zeSe

cto

rFe

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tyra

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ship

(q9

)M

arke

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q1

1)

57

The final logistic model displayed in Figure 3.8 (full model in Table A3.10 Annex 2) has

been calculated with reference to the likelihood of investing in ICT to support a

delocalisation strategy. As it can be expected, this model shows a strong correlation

between the market of reference and the investment in ICT to support the

delocalisation strategy in the last five years. Workplaces active in the national and

particularly in international markets are more likely (respectively 4.8 and 6 times) to

have invested in ICT to support delocalisation strategies compared to workplaces

operating locally. Also, workplaces which are the headquarters of a group are more

likely (3.3 times) than single workplaces to have invested in digital technologies to

support delocalisation strategies. In addition, small or medium-sized workplaces have a

higher probability (1.3 and 1.4 times respectively) of having invested in digital

technology to support delocalisation compared to micro-sized workplaces. Furthermore,

employing high proportions of employees holding a university degree has a positive

correlation with the investment in ICT to support delocalisation strategies in the last five

years. Conversely, negative correlations have been calculated for the rest of

characteristics analysed: workplaces in all macro-sectors (compared to Agriculture),

those with a proportion of 26% to 75% and 75% and more of female employees, those

with more than 75% of younger employees or those with 25% to 75% of older

employees are slightly less likely to have invested in ICT to support a delocalisation

strategy.

58

Figure 3.8 – Logistic regression: probability that workplace has invested in ICT to support

delocalization strategy in the last five years (Odds ratios).

* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; P,Q: Education and human health and social work activities. Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has your workplace invested in ICT for…? (Please select all that apply) Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Note: The regressors ‘Female rate’, ‘University rate’, ‘Young rate’. ‘Old rate’ have four categories, reflecting the proportion of female, university degree holders, younger than 30, older than 50 workers of the total workforce. The four categories are as follows: between 0% and 25%, 26% to 50%, 51% to 75%, more than 75%. Source: Elaboration on European Digital Skills Survey (weighted values)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5

Only workplace (omitted)

Headquarters

Subsidiary_site

size < 10 (omitted)

size10_49

size50_249

size250

sectorA (omitted)

sectorCD

sectorF

sectorGHI

sectorJMN

sectorPQ

fem_rate<26 (omitted)

fem_rate_26_50

fem_rate_51_75

fem_rate_75

univ_rate<26 (omitted)

univ_rate_26_50

univ_rate_51_75

univ_rate_75

young_rate<26 (omitted)

young_rate_26_50

young_rate_51_75

young_rate_75

old_rate<26 (omitted)

old_rate_26_50

old_rate_51_75

old_rate_75

private (omitted)

public

Local_market (omitted)

Regional_market

National_market

International_market

Wo

rkp

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typ

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q11

)

59

Finally, the descriptive analysis at country level reported in Figure A3.5 and Table A3.6

in Annex 2 shows that workplaces based in Portugal are those that invested relatively

more in ICT in all the areas while, at the other end of the spectrum, workplaces based

in Sweden are those reporting more frequently a lack of investment in ICT in all the

areas in the last five years. Such results are not surprising, considering that Portugal is

one of the European countries with the lowest level of digitisation. These figures

suggest that Portuguese workplaces have been investing in ICT to put their business

and the country in a better position in the digital map of Europe, in line with the

national government priorities and the recent plan for the “Digital employability”19 for

2015-2020. On the other hand, Sweden is among the countries with the highest level of

digitalization in the European Union, and therefore significant investments in ICT have

been already started much earlier, therefore in the last five years the investments in

ICT have been more incremental or not as significant as in an initial stage of

digitisation.

3.4 Summary

This section has provided an overview of the use of ICT and specific digital devices and

of the investment strategy in ICT in European workplaces across the 12 economic

sectors of interest of the European Digital Skills Survey. The main findings presented in

this section include the following:

The vast majority of European workplaces use desktop computers (92.7%),

broadband technology to access the internet (93.6%), portable computers (75.3%)

and other portable devices (63.3%), while a much smaller share makes use of

intranet platforms (22.5%), CNC machines or tools (7.8%) and programmable

robots (5.2%);

Specific sector-based trends, with the use of certain technologies concentrated in

specific sectors can be observed, as for example the use of CNC machine and

robots in the ‘Agriculture’ and ‘Manufacturing’ sectors and the use of laptops in the

‘Information and communication’ or ‘Education and human health’ sectors;

Large-sized workplaces report the highest use of all the digital technologies listed;

The use of ICT in workplaces has increased significantly in the last 5 years at EU

level with less than 10% of workplaces reporting that there has been no increase.

In this period, micro-sized workplaces were more likely to report no increase or a

limited increase in the use of ICT compared to workplaces of a larger size;

Workplaces in which the use of ICT has increased in the last five years are more

likely to be part of a group (in particular being the headquarters), to belong to the

macro-sectors ‘Education and health’ or ‘Information and communication’, to have a

higher incidence of women employees and of employees holding a university

degree, and operate on national markets;

Workplaces are less likely to report expected increases in ICT investment over the

next five years, although medium and large workplaces were more positive than

smaller ones. Generally, 68.5% of workplaces anticipate that the use of digital

technology will increase in the next five year, while 25% expect no increase and

6.5% have no specific expectation;

In terms of investment strategy, workplaces have invested in ICT in the last five

years mainly to improve their overall efficiency (68% of workplaces in the EU28).

19 http://www.fct.pt/dsi/docs/EstrategiaPlanoAcaoEmpregabilidadeDigital_v0.1.pdf

60

Investments in ICT were linked to a lesser extent to marketing and sales (60%),

internal organization (50%) and delocalisation strategy (31%). Nevertheless a

significant proportion of European workplaces did not invest at all in these areas:

21.7% of workplaces did not invest in the area of improving overall efficiency, 31%

did not invest in marketing and sales, 37% did not invest in internal organization

and as much as 56% did not invest in delocalisation strategy);

Medium and large-sized workplaces invest relatively more at a significant level in

practically all the areas, while micro-sized workplaces invest more frequently due to

an overall lack of investment in ICT;

Four logistic regression models have allowed for the identification of the

characteristics of workplaces that are more likely to influence the probability of

investing in digital technologies;

Differences in the use of ICT are registered across countries. Workplaces based in

Portugal are those that invested relatively more in ICT in all the areas at both

moderate and significant level while, at the other end of the spectrum, workplaces

based in Sweden are those reporting more frequently a lack of investment in ICT in

all the areas in the last five years. This is linked to the current levels of digitisation

of workplaces in these countries (lower in Portugal, with a need to bring businesses

closer to the levels in other EU countries, and high in Sweden).

61

CHAPTER 4. DIGITAL SKILLS IN EUROPEAN

WORKPLACES

As noted in Chapter 1, many researchers observe that the digitisation of the economy is

profoundly transforming the labour market. Digital technologies are changing jobs and

work practices; the way job tasks are performed, job content and requirements, and

the skills needed to perform a job. As digital technologies become more and more

embedded into the daily operations of firms across a large number of industries and

economic sectors, digital skills are becoming increasingly important for a large number

of workers.

The aim of this chapter is to respond to the following research questions:

How many jobs in the European Union (EU) require digital skills?

What types of digital skills are most required by employers?

How can the jobs requiring digital skills be classified according to the level of

digital skills required?

What are the differences across economic sectors and occupations in terms of

digital skills required by employers?

This chapter displays the type and level of digital skills required by employers to carry

out jobs in specific occupations, in different economic sectors and in workplaces of

different sizes. The chapter also looks at the proportion of the workforce currently

employed in different types of occupations by type of digital skills, and also by sector

and size. Finally, it provides details on the types of digital skills required in selected

examples of specific jobs based upon qualitative information gathered through the

research.

Overall, the results confirm that the extensive use of digital technologies in workplaces

(displayed in Chapter Three) is transforming jobs in particular in terms of digital skills

required, and in line with what is suggested in the research literature. Research findings

show that digital skills appear to be required - to a varying degree - across a wide

range of jobs and working contexts regardless of the economic sector or the size of the

workplace. Nevertheless, a correlation between type and level of digital skills required

and jobs in specific high- or medium-skilled occupations has been identified, indicating

that employees in low or unskilled jobs are much less likely to be required to possess

digital skills to perform their job.

4.1 Defining digital skills in the context of this survey

As noted in Section 1.3 of Chapter 1, there is no standard or agreed upon definition of

digital or ICT skills, and efforts are ongoing to characterise the various types of digital

or ICT skills. However, as pointed out by existing literature, digital skills can be broadly

understood as the ability to locate, organise, understand, evaluate, create and share

information using digital technology, at different levels of competence. Using the Job

62

Requirements Approach20 employed by the OECD21 and CEDEFOP22 to assess skills,

digital skills can be defined with reference to the tasks carried out and the level of

competence required.

Following this approach and considering the scope and purposes of this survey, a set of

10 digital skills has been considered, and workplaces were surveyed in relation to each

skill by type of occupation and level of proficiency of employees:

1. Using a word processor (e.g. Word);

2. Create a spreadsheet (e.g. Excel);

3. Search for, collect and process information using ICT (e.g. online/Internet);

4. Communicate through ICT using email;

5. Communicate through ICT using social media, Skype/videocalls;

6. Use software for design, calculation or simulation;

7. Undertake programming and software development;

8. Design and maintain ICT architecture for the workplace;

9. Programme and use CNC machines;

10. Programme and use robots.23

Research carried out by OECD and CEDEFOP (explored in Chapter 1), and the

exploratory analysis undertaken for this research shows correlations between specific

groups of indicators. For the purpose of this report these have been clustered into three

main synthetic indicators:

Basic digital skills: includes the skills one to five from the list above;

Advanced digital skills: includes digital skills listed at points 6, 9 and 10 of the list

above;

Specialist digital skills: covers digital skills reported at points 7 and 8 of the list.

The results of the analysis across the three clusters is presented in this chapter and the

one that follows.

4.2 Digital skills for work: available evidence from other sources

The evidence regarding the availability of digital skills among workers is rather limited.

Nevertheless, existing evidence seems to indicate that digital technologies are

increasingly and steadily penetrating workplaces, bringing along a need for related skills

required by a significant proportion of workers, to perform their day-to-day job tasks.

Data from Cedefop’s 2014 ‘European skills and jobs survey’ carried out on adult

employees (aged 24 to 65), indicate that ‘about seven in 10 EU adult workers need to

20 The Job Requirements Approach allows to assess skills based on the tasks that the workers is required to carry out in a specific job. 21 Mañé, F. (2013). Using the job requirements approach and matched employer-employee data to investigate the content of individuals’ human capital. In: Green, F.; Keese, M. (eds). Job tasks, work skills and the labour market. Paris: OECD Publishing. 22 Cedefop (2015). Skills, qualifications and jobs in the EU: the making of a perfect match? Evidence from Cedefop’s European skills and jobs survey. Luxembourg: Publications Office. 23 The number of digital skills (or tasks requiring digital skills) initially included in the survey’s questionnaire was 12, as the programming and use of CNC machines and robots respectively were investigated separately. For the purposes of the analysis, however, they were ex post clustered, and two new variables (‘Programme and use of CNC machines’ and ‘Programme and use of robots’) were accordingly calculated and used in the analysis presented in the chapter and also in Chapter 5.

63

possess at least a moderate level of ICT skills to be able to do their jobs (52%

moderate level and 14% advanced)’ (Cedefop 2015). This appears strictly related to the

job role. More than 20% of those in managerial and professional roles or working as

technicians need advanced ICT skills for their jobs, and about 70% need basic or (more

frequently) moderate digital skills. More than 70% of workers in clerical roles require at

least a moderate level of digital skills to perform their job. Even 42% of those employed

in elementary occupations need some level of digital skills to be able to do their job

(Cedefop 2015).

Data from Eurofound’s ‘Sixth European Working Conditions Survey’, carried out in 2015

on a representative sample of employed individuals in the EU28 Member States, show

that 56% of European workers work with digital technologies (computers, laptops,

smartphones, etc.)24. This data also seems to show a correlation between type of

occupation and use of digital technologies. High-skilled clerical workers are more likely

to use digital technologies, 49% of workers in this occupational group do so all of their

working time and 36% of them do so for at least between a quarter and three quarters

of their working time. Low-skilled clerical workers are less likely to use digital devices,

40% of them do so all of their working time and 26% between one quarter and three

quarters of their working time. Conversely, manual workers are considerably less likely

to use digital devices at work.

Additional evidence from the most recent Eurostat ‘ICT usage in enterprises’ survey

indicates that in EU28 workplaces 54% of employees use computers to perform their

job, and 88% of them use a computer with access to internet25.

These figures remain under the suggested figure that ‘90% of jobs will require digital

skills’ at some point in the future, reported by many official sources. For example, this

figure is often reported in European Commission documents and websites26 and quoted

by several other reports and sources27 with reference to the EU28, and at the national

level this figure is also often reported28. The timespan of when this is expected to

happen varies, as in some cases this is thought to happen in ‘the near future’ and in

other cases is ‘by 2020’. A study carried out in 2009 by IDC claimed that ‘in five years’

time less than 10% of job roles will require no ICT skills at all’ (Kolding et al. 2009).

While the existing evidence displayed in this section suggests that the 90% figure could

be achieved at some time in the near future, the evidence gathered through the Digital

Skills Survey indicates that in some job categories and in relation to specific types of

digital skills already more than 90% of jobs require digital skills. This will be extensively

illustrated in the following sections.

24 http://www.eurofound.europa.eu/surveys/data-visualisation/sixth-european-working-conditions-survey-2015 25 Data available on Eurostat database http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_ci_cm_pn2&lang=en; survey methodology available at http://ec.europa.eu/eurostat/cache/metadata/en/isoc_e_esms.htm 26 http://ec.europa.eu/europe2020/pdf/themes/2016/digital_single_market_skills_jobs_26105.pdf 27http://www.digitaleurope.org/DesktopModules/Bring2mind/DMX/Download.aspx?Command=Core_Download&EntryId=1089&language=en-US&PortalId=0&TabId=353; http://www.unica-network.eu/news/european-commission-launches-opening 28 For example, the UK Skills Funding Agency, a government body, report the figure in a in a recent report https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/499031/Review_of_Publicly_Funded_Digital_Skills_Qualifications_2016_FINAL.pdf; similarly, the Malta Information Technology Agency https://www.meusac.gov.mt/file.aspx?f=3234

64

4.3 What types of digital skills are required in European workplaces?

The Digital Skills Survey investigated the digital skills existing in European workplaces.

Workplaces were surveyed about the level of importance they assign to the digital skills

possessed by their workforce (measured on a scale from 1 to 5, where 1 is ‘not at all

important’ and 5 is ‘essential’), classified in the nine occupational categories identified

by the ISCO 1-digit code29.

Broadly speaking, the evidence collected shows that significant proportions of

workplaces require their workforce to possess at least basic digital skills to perform

their job tasks (Figure 4.1). Almost all the workplaces (98%) suggest that managers

should possess at least basic digital skills. Also, around nine workplaces out of ten state

that employees working as professionals (94%), technicians and associate professionals

(88%), clerical support workers (98%)or skilled agricultural workers (90%) are required

to possess at least basic digital skills. A slightly lower proportion of workplaces (81%)

report that sales, customer or personal service workers need to possess basic digital

skills. Although in much smaller proportions, workplaces require basic digital skills also

to building workers (almost half of workplaces), plant machine operators (34% of

workplaces) and even to employees in elementary occupation (27% of workplaces).

The basic digital skills more frequently required by employers are those related to

searching for, collecting and processing information via the internet and communicating

using email, and this applied across all the job categories (see Tables A4.1 and A4.2 in

Annex 2). These types of digital skills are often seen as being highly important for

managers, professionals, technicians and clerical workers, moderately important for

sales workers and skilled agricultural workers and of lower importance to other groups

of workers (Table A4.3).

Employers mainly require advanced digital skills among the professionals (54% of

workplaces) and technicians (52%) they employ, and to a lesser extent among clerical

workers (45%), managers and building workers (31% of workplaces each). Advanced

digital skills are much less important for all the rest of occupations, but one quarter of

workplaces require advanced digital skills for sales workers, and for skilled agricultural

workers. One fifth of employers report that plant machine operators should possess

advanced digital skills. Advanced digital skills are required to a moderate to low level of

importance for all the jobs (see Table A4.3, Annex 2).

Finally, regarding the third group of digital skills - specialist digital skills – which

includes programming and software development, and the design and maintaining of

ICT architecture for the workplace, a very significant proportion of workplaces require

workers to possess this type of digital skills where they are employed as professionals

and technicians (43% and 44% respectively), and to a lesser extent as managers (33%

of workplaces). Workplaces require specialist digital skills also among skilled agricultural

workers and clerical workers (25% and 22% respectively). Smaller proportions of

workplaces require individuals to possess specialist digital skills where they are

employed as sales workers (16%), building workers (11%), plant machine operators

(10% of workplaces) or in elementary occupations (7%). Specialist digital skills are

29 “Managers”, “Professionals, “Technicians and associate professionals”, ”Clerical support workers”, “Sales, customer or personal service workers”, “Skilled agricultural, forestry and fishery workers”, “Building, craft and related trade workers”, “Plant and machine operators and assemblers”, ”Elementary occupations”.

65

more frequently required in the ‘design and maintaining of ICT for the workplace’ (see

Tables A4.1, Table A4.2 and A4.3 in Annex 2).

Figure 4.1 – Workplaces by level of importance of digital skills (by type) of employees in specific occupations, EU28 (% of total employers)

Q16. Thinking about the job categories in your workplace, please indicate, using a scale of 1 to 5 (where 1 means not at all important, 3 means moderately important and 5 means essential,) how important it is for employees in these categories to…: Number of valid responses=4,608 (job category: Managers); 2,224 (job category: Professionals); 1,886 (job category: Technicians); 3,929 (job category: Clerical workers); 2,298 (job category: Sales workers); 858 (job category: Skilled agricultural workers); 1,824 (job category: Building workers); 1,145 (job category: Plant machine operators); 1,319 (job category: Elementary occupations) N=5,644,799 (job category: Managers); 3,463,858 (job category: Professionals); 2,045,270 (job category: Technicians); 4,172,004 (job category: Clerical workers); 3,815,976 (job category: Sales workers); 1,740,841 (job category: Skilled agricultural workers); 2,475,089 (job category: Building workers); 1,059,179 (job category: Plant machine operators); 1,164,035 (job category: Elementary occupations) Source: European Digital Skills Survey (weighted values)

2.4

66.7

66.6

5.4

45.7

55.9

12.1

46.8

55.0

1.8

54.9

77.8

19.1

74.5

83.6

10.5

74.7

74.7

50.4

66.7

88.5

65.3

77.3

88.3

72.2

89.5

91.6

97.6

33.1

33.0

94.3

53.9

43.2

87.5

52.2

43.7

98.1

44.6

21.6

80.8

25.1

15.7

89.6

25.3

25.2

49.4

33.1

10.9

34.1

22.0

10.7

27.5

9.9

7.0

Basic

Advanced

Specialist

Basic

Advanced

Specialist

Basic

Advanced

Specialist

Basic

Advanced

Specialist

Basic

Advanced

Specialist

Basic

Advanced

Specialist

Basic

Advanced

Specialist

Basic

Advanced

Specialist

Basic

Advanced

Specialist

Man

ager

sP

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nal

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icia

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Not important at all Somehow important to essential Don't know

66

Given the limitations around the sample size, additional analysis around the specific

characteristics of the workplaces does not allow for more accurate analysis at, for

example, the sector level.

To explore what types of workplaces consider advanced or specialist digital skills

important for their workforce, two linear regression models have therefore been

developed.

The linear regression models (presented in full in Tables A4.5 and A4.6 in Annex 2 and

in a synthesised version in Figures 4.2 and 4.3 below), show the relationship between

each of the workplace’s characteristics and the importance of advanced or specialist

digital skills.

The importance assigned to advanced digital skill increases significantly (all other

characteristics being equal) for large-sized workplaces, and for workplaces operating in

‘information and communication, professional, scientific and technical activities,

administrative and support service activities’ and ‘manufacturing and utilities’ macro-

sectors (Figure 4.2). The importance placed on advanced digital skills increases

significantly (all other characteristics being equal) if the workplace belongs to a group,

in the role of headquarters, rather than being a single office, when the reference market

of the workplace are the national or the international markets, and when the workplace

is in the public sector. Conversely, workplaces for which advanced digital skills are not

as important are micro-sized workplaces, subsidiary site workplaces, and workplaces

that operate in local markets and employ a high proportion of female (51% to 75% of

staff or 75% and more) or older workers (26% to 50% of total staff).

67

Figure 4.2 – Linear regression: importance for workplaces of advanced digital skills

(Estimated parameters).

* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; P,Q: Education and human health and social work activities. Q16. Thinking about the job categories in your workplace, please indicate, using a scale of 1 to 5 (where 1 means not at all important, 3 means moderately important and 5 means essential,) how important it is for employees in these categories to…: Note: The regressors ‘Female rate’, ‘University rate’, ‘Young rate’. ‘Old rate’ have four categories, reflecting the proportion of female, university degree holders, younger than 30, older than 50 workers of the total workforce. The four categories are as follows: between 0% and 25%, 26% to 50%, 51% to 75%, more than 75%. Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light

grey. Source: European Digital Skills Survey

The linear regression model (Figure 4.3) shows that in line with the patterns observed

above for advanced digital skills, the importance given to specialist digital skill also

increases significantly (all other characteristics being equal) for workplaces belonging to

a group in the role of headquarters, for large-sized workplaces, and in particular for

workplaces operating in the ‘information and communication, professional, scientific and

technical activities, administrative and support service activities’ macro-sector. Also, the

importance given to advanced digital skills increases significantly (all other

characteristics being equal) for workplaces that are active on international markets,

-0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4

Only workplace (omitted)

Headquarters

Subsidiary_site

size < 10 (omitted)

size10_49

size50_249

size250

sectorA (omitted)

sectorCD

sectorF

sectorGHI

sectorJMN

sectorPQ

fem_rate<26 (omitted)

fem_rate_26_50

fem_rate_51_75

fem_rate_75

univ_rate<26 (omitted)

univ_rate_26_50

univ_rate_51_75

univ_rate_75

young_rate<26 (omitted)

young_rate_26_50

young_rate_51_75

young_rate_75

old_rate<26 (omitted)

old_rate_26_50

old_rate_51_75

old_rate_75

private (omitted)

public

Local_market (omitted)

Regional_market

National_market

International_market

Wo

rkp

lace

typ

eW

ork

pla

cesi

zeSe

cto

rFe

mal

e ra

teU

niv

ersi

tyra

teYo

un

g ra

teO

ld r

ate

Ow

ner

ship

(q9

)M

arke

ts (

q11

)

68

that are in the public sector or that employ a high proportion of employees holding a

university degree. Workplaces where which advanced digital skills are not especially

important include those employing a high proportion of female workforce.

Figure 4.3 – Linear regression: importance for workplaces of specialist digital skills

(Estimated parameters).

Q16. Thinking about the job categories in your workplace, please indicate, using a scale of 1 to 5 (where 1 means not at all important, 3 means moderately important and 5 means essential,) how important it is for employees in these categories to…: Note: The regressors ‘Female rate’, ‘University rate’, ‘Young rate’. ‘Old rate’ have four categories, reflecting the proportion of female, university degree holders, younger than 30, older than 50 workers of the total workforce. The four categories are as follows: between 0% and 25%, 26% to 50%, 51% to 75%, more than 75%. Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Source: elaboration on European Digital Skills Survey

-0.4 -0.2 0 0.2 0.4 0.6 0.8

Only workplace (omitted)

Headquarters

Subsidiary_site

size < 10 (omitted)

size10_49

size50_249

size250

sectorA (omitted)

sectorCD

sectorF

sectorGHI

sectorJMN

sectorPQ

fem_rate<26 (omitted)

fem_rate_26_50

fem_rate_51_75

fem_rate_75

univ_rate<26 (omitted)

univ_rate_26_50

univ_rate_51_75

univ_rate_75

young_rate<26 (omitted)

young_rate_26_50

young_rate_51_75

young_rate_75

old_rate<26 (omitted)

old_rate_26_50

old_rate_51_75

old_rate_75

private (omitted)

public

Local_market (omitted)

Regional_market

National_market

International_market

Wo

rkp

lace

typ

eW

ork

pla

ce s

ize

Sect

or

Fem

ale

rate

Un

iver

sity

rat

eYo

un

g ra

teO

ld r

ate

Ow

ner

ship

(q

9)

Mar

kets

(q

11)

69

4.4 Digital skills of employees in different jobs

The type of digital skills required by employers mirrors the evidence collected on

employees, in each job category, by the type of digital skills they possess.

The majority of employees in each job category possess at least basic digital skills

(Figure 4.4), with the proportion varying greatly according to the occupation. Almost

nine out of ten workers employed as managers (88%), and more than half of clerical

workers (56%) and skilled agricultural workers (59%)30 possess basic digital skills.

Almost four out of ten workers employed as professionals (38%) or technicians (40%)

possess basic digital skills. About three out of ten workers in the rest of the

occupational groups possess basic digital skills. The basic digital skills more frequently

reported are around the use of internet and email, (Table A4.2 in Annex 2). Advanced

digital skills are more likely to be possessed by managers (19%), skilled agricultural

workers (21%) and to a lesser extent clerical workers (13% of total employees in this

category), followed by those employed as technicians (10%), professionals (9%),

building workers (8%), sales workers and elementary occupations (8% each) and finally

as plant machine operators (7%).

Among the advanced digital skills, skills in using software for design, calculation and

simulation is most common across all the occupational categories, while only very

limited proportions of workers in specific job categories (e.g. technicians) possess the

skills needed to use and programme CNC machines or robots (Table A4.2 in Annex 2).

Finally, specialist digital skills are more common among workers employed as skilled

agricultural workers and managers (25% and 23% respectively). Slightly more than

10% of individuals employed as clerical workers, technicians and professionals (14%,

12%, 10% respectively) have specialist digital skills. A small proportion of employees in

the sales workers, building workers, plant machine operators and elementary

occupations job categories possess specialist digital skills.

30 Figures regarding the proportion of skilled agricultural workers should be interpreted cautiously in absence of further research, as they seem to be anomalous in some cases. The existence of a bias of social desirability could be one possible explanation, although the significant changes and the restructuring of the agricultural sector, which has attracted as well younger and skilled employees, could also contribute to explain such results.

70

Figure 4.4 – Employees in specific occupations by type of digital skills (basic, advanced, specialized), EU28 (% of total employees in specific occupational category)

Q17. Please provide your best estimate of the approximate number or share of employees carrying out such tasks (listed in Q16). Number of employees in each job category (weighted values, EU28)= 7,564,363 (job category: Managers); 28,452,941 (job category: Professionals); 25,519,514 (job category: Technicians); 16,921,402 (job category: Clerical workers); 25,519,514 (job category: Sales workers); 1,526,330 (job category: Skilled agricultural workers); 16,887,614 (job category: Building workers); 12,206,673 (job category: Plant machine operators); 15,561,133 (job category: Elementary occupations) Total number of employees (weighted values, EU28)= 150,563,540 Number of valid responses=4,608 (workplaces) N=5,644,799 (workplaces) Source: European Digital Skills Survey (weighted values)

Illustrative examples of the digital skills required by specific jobs that are not office-

based are shown below with reference to different jobs categories. The qualitative

information has been gathered as part of the ‘ICT for work: Digital skills in the

workplace’ study of which the European Digital Skills Survey is part, with the aim of

providing a picture of how digitisation is increasingly penetrating and transforming

existing jobs and their quality31.

31 The full results and the methodology of such research activity, carried out in 2016 by the consortium Ecorys and Danish Technological Institute, are presented in the report The impact of ICT on job quality: evidence from 12 job profiles. An intermediate report from the study "ICT for work: Digital skills in the workplace – SMART 2014/0048" https://ec.europa.eu/digital-single-market/en/news/report-shows-digital-skills-are-required-across-all-types-work-also-jobs-outside-office

88.3

38.4

40.5

56.3

34.3

59.7

30.6

27.5

35.1

18.7

8.9

9.8

13.2

7.7

21.3

8.3

7.1

7.5

23.3

10.3

11.8

14.1

6.7

25.0

7.7

4.7

5.7

Managers

Professionals

Technicians

Clerical workers

Sales workers

Skilled agric workers

Building workers

Plant machine operators

Elementary occupations

Basic digital skills Advanced digital skills Specialist digital skills

71

For example in the ‘agricultural and forestry production managers’ (ISCO 1311) the

profiling of the specific job ‘dairy farmer’ allowed the following digital skills currently

needed by farmers to perform their day-to-day job tasks to be identified.

Box 4.1 – Digital skills required by Dairy farmers

Job: Dairy farmer

Digital skills required Description / Purpose

Basic digital skills The dairy farmer needs a basic level of digital skills to use the software applications and needs to continually update knowledge

Advanced user skills for herd management software and automatic feeding systems

To track and maintain detailed records on livestock and to schedule vaccinations, testing and breeding events. Skills also needed for automatic feeding systems

Advanced user skills to manage automatic milking systems (AMS)

To manage automated milking systems (although farmer may also buy expert advice from ICT

specialist who installs the system)

Advanced user skills for ERP systems Depending on the size of the farm, may need advanced skills to support planning and management of farm's functions

Source: The impact of ICT on job quality: evidence from 12 job profiles

The following example ‘product and garment designers’ (ISCO 2163) in the job category

‘professionals’ looks specifically at the job profile of an ‘industrial designer in

manufacturing company’. The digital skills required to perform the job tasks, which in

the past were carried out entirely manually, are currently the following.

Box 4.2 – Digital skills required by Industrial designers

Job: Industrial designer

Digital skills required Description / Purpose

Basic and advanced digital skills to use multiple ICT tools efficiently

Importance of familiarity with multiple ICT tools

Advanced user skills for computer aided design (CAD) software

To use computer systems to aid in the creation, modification, analysis or optimisation of a design; use CAD software key to increase productivity of designer, improve quality of the design, improve communications through documentation and create a

database for manufacturing

Advanced user skills for 3D printing To quickly create complex, 3D forms

Advanced user skills for analytical software

To do calculations and simulations and model-based design related to graphic programming and the design process; use of this software is important to research production specifications, costs, production materials and manufacturing methods and provide

cost estimates and itemised production requirements

Source: The impact of ICT on job quality: evidence from 12 job profiles

The ‘specialist physician (internal medicine) also in the job category of ‘professionals’

(ISCO 2212) and in particular the job profile of ‘doctor in a hospital (general internist

physician)’, which currently require the following types of digital skills.

72

Box 4.3 – Digital skills required by Doctors in a hospital

Job: Doctor in a hospital (general internist physician)

Digital skills required Description / Purpose

Basic user skills to apply transcription and translation software

To handle voice recognition software to transcribe dictation; computer based skills for computer based

dialogue systems that help doctors to translate and transcribe interviews with foreign language patients

Advanced user skills to apply digital patient administration systems

To operate key tools used for keeping track of the waiting list, appointments, coding of procedures and

diagnoses, and patient billing

Advanced user skills for patient monitoring systems

To monitor and interpret patient data

Source: The impact of ICT on job quality: evidence from 12 job profiles

In the job category ‘clerical workers’, the ‘transport clerks’ (ISCO 4323) and in

particular the job profile of the ‘transport clerk in transport/logistics company’, needs

the following digital skills to perform their job tasks.

Box 4.4 – Digital skills required by Transport clerks (TCs)

Job: Transport clerk

Digital skills required Description / Purpose

Advanced digital skills for analytical software

To find transport solutions that optimise the decided route, the use of energy resources and best possible utilisation of transport vehicles; TC must also be able to present solutions and various options to the client using these skills

Advanced user skills for shipment tracking software (or package logging)

For the process of tracking shipping containers, mail and parcel post at different points of time during the transportation process; to be able to provide a tracking number/ref to the client

Basic user skills in management system organising all the activities of the transport company

Overall management is not the responsibility of the TC, but to work efficiently within the company a basic competence is required. The management system organises inventory, remote warehouses, activity billing, client agreements, orders etc.

Source: The impact of ICT on job quality: evidence from 12 job profiles

A final illustrative example of how digitisation is changing jobs and occupations is the

‘car mechanic’ in the ‘motor vehicle mechanics and repairers’ (ISCO 7231) in the job

category ‘building workers and craft and related trades workers’, who currently needs

the following digital skills to repair cars and vehicles which are incorporating an

increasing amount of digital technology.

73

Box 4.5 – Digital skills required by Car mechanics

Job: Car mechanic

Digital skills required Description

Advanced user skills to apply analytical software to diagnose vehicles

To diagnose the cause of vehicle operating problems by tracing and locating defects and repairing malfunctions

Advanced user skills for online databases for repair of vehicles

To ensure repair is up to manufacture’s standard

Basic user skills for CRM-systems

Automotive Customer Relation Management (CRM)

CRM specifically made for the industry; to organise

and keep track of all activities and services taking place between the customer and repair shop

Digital skills integrated with

professional/technical skills of car mechanics

To use relevant software tools to carry out work;

digital skills alone are of no value and must be combined with mechanical skill

Source: The impact of ICT on job quality: evidence from 12 job profiles

4.5 Analysis of specific jobs

In order to provide additional evidence on the digital skills required by employers to

workers employed in specific jobs, within the European Digital Skills Survey workplaces

were asked to select up to three jobs amongst the most important for day-to-day

operations. They were asked to assess a number of aspects related to them, such as

the type and level of digital skills of employees in these selected jobs, and changes in

job tasks in the last five years or in the next five years related to the use of ICT. This

section presents the findings of the analysis of this data. Data are reported by economic

sector and are unweighted, since this information is meant to be qualitative in nature,

and also considering the limited number of responses collected and the related issues of

representativeness related to the sample size. A total number of 3,138 out of 7,800

workplaces selected only one job to be evaluated, 2,100 workplaces selected two and

1,026 selected three. A total number of 6,264 workplaces (corresponding to 80% of

total workplaces) undertook the assessment of selected jobs.

The job selected at the top of the ranking across all economic sectors is a managerial

job (Table 4.1). Although in most of the sectors the occupations most frequently

reported are very much sector-specific (e.g. in the agricultural sector forestry and

related workers or field crop and vegetable growers, in the construction sector home

builders, roofers, etc.) some occupations, such as for example managing directors and

chief executives, are reported among the first 10 in all the economic sectors. In

‘agriculture’ sector the ‘agricultural and forestry production managers’ were selected as

the most important for day-to-day operations by 147 workplaces; in the ‘manufacturing

and utilities’ macro-sector ‘manufacturing managers’ were chosen by 215 workplaces;

in the ‘construction’ sector the ‘construction managers’ have been selected by 158

respondents; in both ‘commerce, transport, accommodation and food service’ and

‘information and communication, professional and administrative services’ macro-

sectors the ‘managing directors and chief executives’ have been selected by 131 and

130 workplaces respectively; finally in the ‘education and human health’ macro-sector

‘education managers’ were indicated by 82 workplaces.

74

Table 4.1 – Occupations selected as the most important for day-to-day operations by sector and total number of workplaces (ranking based on number of workplaces reporting

occupation, top 10 occupations)

Sector Rank Job title Total

number of workplaces

A. Agriculture

1 Agricultural and forestry production managers 147

2 Forestry and related workers 116

3 Managing directors and chief executives 98

4 Mixed crop growers 61

5 Mixed crop and animal producers 58

6 Gardeners, horticultural and nursery growers 57

7 Field crop and vegetable growers 57

8 Livestock and dairy producers 47

9 General office clerks 45

10 Animal producers not elsewhere classified 34

CD. Manufacturing

and utilities

1 Manufacturing managers 215

2 Managing directors and chief executives 126

3 Clerical support workers not elsewhere classified 73

4 Sales and marketing managers 68

5 General office clerks 59

6 Building frame and related trades workers not elsewhere classified

45

7 Craft and related workers not elsewhere classified 43

8 Industrial and production engineers 43

9 Finance managers 43

10 Manufacturing supervisors 35

F. Construction

1 Construction managers 158

2 House builders 140

3 Managing directors and chief executives 123

4 Building frame and related trades workers not elsewhere classified

103

5 General office clerks 54

6 Civil engineers 48

7 Plumbers and pipe fitters 44

8 Clerical support workers not elsewhere classified 41

9 Craft and related workers not elsewhere classified 37

10 Carpenters and joiners 32

GHI. Commerce, transport,

accommodation and food service

1 Managing directors and chief executives 131

2 Shop sales assistants 123

3 Retail and wholesale trade managers 95

4 Sales and marketing managers 89

5 Waiters 79

6 Cooks 75

7 General office clerks 64

8 Shopkeepers 63

9 Sales workers not elsewhere classified 61

10 Supply, distribution and related managers 59

JMN. Information and communication;

Professional, scientific and

technical activities;

Administrative services

1 Managing directors and chief executives 130

2 Clerical support workers not elsewhere classified 56

3 Sales and marketing managers 52

4 Accountants 49

5 Information and communications technology service managers 45

6 General office clerks 44

7 Finance managers 38

8 Software developers 33

9 Engineering professionals not elsewhere classified 33

10 Business services and administration managers not elsewhere classified

30

PQ. Education and human

health

1 Education managers 82

2 Managing directors and chief executives 72

3 Specialist medical practitioners 59

75

Sector Rank Job title Total

number of workplaces

4 Health services managers 53

5 General office clerks 47

6 Dentists 43

7 Personal care workers in health services not elsewhere classified 39

8 Generalist medical practitioners 36

9 Health professionals not elsewhere classified 32

10 Early childhood educators 31

Q18. Please select up to THREE specific jobs existing in your workplace which are amongst the most important for your day-to-day operations. If possible, please select them from different job categories. N=6,264 Source: European Digital Skills Survey (unweighted values)

For each selected job, workplaces have assessed the importance of digital skills (by

type) required to perform day-to-day activities (Table 4.3). Overall, basic digital skills

are seen as at least somewhat important for almost all the jobs. All the other types of

digital skills do not appear as relevant across sectors and jobs. In several cases, digital

skills are not felt to be important at all. On the other hand, all the most crucial jobs

(those at the top of the ranking) are considered to require all types of digital skills to

some level of importance, although the basic digital skills are required to a moderate to

higher level of importance (Table 4.3, more detailed information is reported in table

A4.8 in Annex 2).

Table 4.3 – Occupations selected as the most important for day-to-day operations by

sector and by type and level of digital skills of employees in selected jobs (level of

importance 1 = not at all important, 3 = moderately important, 5 = essential)

Sector Rank Job title

Basic

dig

ital

skil

ls

Ad

van

ced

dig

ital skil

ls

Sp

ecia

list

dig

ital skil

ls

A. Agriculture

1 Agricultural and forestry production managers 4 2 2

2 Forestry and related workers 2 1 1

3 Managing directors and chief executives 4 2 2

4 Mixed crop growers 2 1 1

5 Mixed crop and animal producers 2 1 1

6 Field crop and vegetable growers 2 1 1

7 Gardeners, horticultural and nursery growers 2 1 1

8 Livestock and dairy producers 2 1 1

9 General office clerks 4 1 2

10 Animal producers not elsewhere classified 1 1 1

CD. Manufacturing

and utilities

1 Manufacturing managers 4 2 2

2 Managing directors and chief executives 4 2 2

3 Clerical support workers not elsewhere classified 5 1 2

4 Sales and marketing managers 4 2 2

5 General office clerks 4 1 2

6 Building frame and related trades workers not elsewhere classified

2 1 1

7 Finance managers 4 2 2

8 Industrial and production engineers 4 2 3

9 Craft and related workers not elsewhere classified 1 1 1

10 Supply, distribution and related managers 4 2 2

F. Construction

1 Construction managers 3 2 2

2 House builders 2 1 1

3 Managing directors and chief executives 4 2 1

76

Sector Rank Job title

Basic

dig

ital

skil

ls

Ad

van

ced

dig

ital skil

ls

Sp

ecia

list

dig

ital skil

ls

4 Building frame and related trades workers not elsewhere classified

2 1 1

5 General office clerks 4 1 2

6 Civil engineers 4 2 2

7 Plumbers and pipe fitters 2 1 1

8 Clerical support workers not elsewhere classified 5 1 2

9 Craft and related workers not elsewhere classified 1 1 1

10 Carpenters and joiners 2 1 1

GHI. Commerce, transport,

accommodation and food service

1 Managing directors and chief executives 4 2 2

2 Shop sales assistants 3 1 1

3 Retail and wholesale trade managers 4 2 2

4 Sales and marketing managers 4 2 2

5 Waiters 1 1 1

6 Cooks 1 1 1

7 General office clerks 4 1 2

8 Shopkeepers 4 1 1

9 Sales workers not elsewhere classified 3 1 1

10 Supply, distribution and related managers 4 1 2

JMN. Information and communication;

Professional, scientific and

technical activities;

Administrative services

1 Managing directors and chief executives 4 2 2

2 Clerical support workers not elsewhere classified 4 1 2

3 Sales and marketing managers 4 2 2

4 Accountants 4 2 2

5 Information and communications technology service managers

4 4 2

6 General office clerks 4 1 2

7 Finance managers 5 2 2

8 Engineering professionals not elsewhere classified 4 3 2

9 Software developers 4 4 2

10 Human resource managers 5 1 1

PQ. Education and human

health

1 Education managers 4 2 2

2 Managing directors and chief executives 4 2 1

3 Specialist medical practitioners 3 1 2

4 Health services managers 4 2 2

5 General office clerks 4 1 2

6 Dentists 3 2 1

7 Personal care workers in health services not elsewhere classified

3 1 1

8 Generalist medical practitioners 3 1 1

9 Health professionals not elsewhere classified 3 1 1

10 Primary school teachers 4 1 2

Q19. Thinking about these jobs in your workplace, please indicate using a scale of 1 to 5 (where 1 means not at all important, 3 means moderately important and 5 means essential) how important for day-to-day activities it is for employees in these jobs to…: N=6,264 Source: European Digital Skills Survey (unweighted values)

The use of digital technologies has changed the way job tasks are carried out in most of

the selected jobs for the vast majority of workplaces and in most of the sectors (Table

4.4). This is especially true for the top-ranked jobs, which are considered to have been

modified by ICT according to 90% of workplaces in the ‘commerce, transport,

accommodation and food service’, ‘information and communication’, and ‘education and

human health’ macro-sectors, by 80% of workplaces in the ‘agriculture’ and

‘manufacturing and utilities’ macro-sectors, and by 70% of workplaces in the

‘construction’ sector. Only in the ‘agriculture’, ‘manufacturing and utilities’,

‘construction’ and ‘commerce’ macro-sectors do a majority of workplaces (admittedly

77

limited to a number of specific low-skilled jobs) consider that digital technologies have

not changed at all the way tasks are performed over the last five years. This is the case

for ‘field crop and vegetable growers’ and ‘animal producers not elsewhere classified’ in

‘agriculture’, the ‘craft and related workers not elsewhere classified’ in both the

‘manufacturing and utilities’ and the ‘construction’ sectors and finally ‘cooks’ in

‘commerce, transport, accommodation and food service’ sectors. Furthermore, the

changes reported are often at a moderate level (see Table A4.9 in Annex 2).

The majority of workplaces consider that ICT will continue changing the way job tasks

are performed in the next five years for most of the selected jobs and in most of the

sectors, especially with regards to the top-ranked jobs. Nevertheless, reported changes

will be less intense in the near future, and a lack of change is more frequently reported

in relation to a number of jobs especially in specific sectors.

A majority of workplaces consider that there will be no change at all in relation to

‘forestry and related workers’ ‘mixed crop growers’ ‘mixed crop and animal producers’,

‘gardeners, horticultural and nursery growers’, ‘animal producers not elsewhere

classified’ jobs in the ‘agriculture’ sector; to ‘craft and related workers not elsewhere

classified’ jobs in ‘manufacturing and utilities’ macro-sector; to ‘plumbers and pipe

fitters’, ‘craft and related workers not elsewhere classified’ and ‘carpenters and joiners’

in the ‘construction’ sector, and finally to ‘waiters’ and ‘cooks’ in the ‘commerce,

transport, accommodation and food service’ macro-sector.

On the other hand, the ‘information and communication, professional and

administrative services’ and the ‘education and human health’ macro-sectors

workplaces are more likely to be expecting further changes brought by digital

technologies in relation to all the jobs selected. The vast majority of workplaces expect

some degree of change induced by digital technologies in the way job tasks are

performed.

Table 4.4 – Occupations selected as the most important for day-to-day operations by

type of change in the job tasks due to the use of ICT in LAST and NEXT five years, by

sector (%)

Sector Rank Job title

Last 5 years

Next 5 years

No c

hange a

t all

Changes r

eport

ed

No c

hange a

t all

Changes r

eport

ed

A. Agriculture

1 Agricultural and forestry production managers 16.6 83.4 27.7 72.3

2 Forestry and related workers 44.8 55.2 51.8 48.2

3 Managing directors and chief executives 8.2 91.8 17.5 82.5

4 Mixed crop growers 45.0 55.0 66.7 33.3

5 Mixed crop and animal producers 43.1 56.9 53.4 46.6

6 Field crop and vegetable growers 52.7 47.3 60.7 39.3

7 Gardeners, horticultural and nursery growers 33.9 66.1 51.8 48.2

8 Livestock and dairy producers 31.9 68.1 41.3 58.7

9 General office clerks 11.1 88.9 11.1 88.9

10 Animal producers not elsewhere classified 55.9 44.1 54.5 45.5

CD. Manufacturing 1 Manufacturing managers 22.3 77.7 22.0 78.0

78

Sector Rank Job title

Last 5 years

Next 5 years

No c

hange a

t all

Changes r

eport

ed

No c

hange a

t all

Changes r

eport

ed

and utilities 2 Managing directors and chief executives 6.4 93.6 9.9 90.1

3 Clerical support workers not elsewhere classified

4.1 95.9 1.4 98.6

4 Sales and marketing managers 9.4 90.6 22.6 77.4

5 General office clerks 3.6 96.4 1.8 98.2

6 Building frame and related trades workers not elsewhere classified

22.2 77.8 24.4 75.6

7 Finance managers 4.9 95.1 11.9 88.1

8 Industrial and production engineers 16.7 83.3 11.6 88.4

9 Craft and related workers not elsewhere classified

65.1 34.9 69.0 31.0

10 Supply, distribution and related managers 21.2 78.8 16.1 83.9

F. Construction

1 Construction managers 26.6 73.4 29.7 70.3

2 House builders 39.3 60.7 50.0 50.0

3 Managing directors and chief executives 6.6 93.4 10.0 90.0

4 Building frame and related trades workers not elsewhere classified

40.8 59.2 44.1 55.9

5 General office clerks 11.1 88.9 9.6 90.4

6 Civil engineers 6.2 93.8 8.5 91.5

7 Plumbers and pipe fitters 50.0 50.0 53.5 46.5

8 Clerical support workers not elsewhere classified

9.8 90.2 4.9 95.1

9 Craft and related workers not elsewhere classified

83.8 16.2 88.9 11.1

10 Carpenters and joiners 68.7 31.3 59.4 40.6

GHI. Commerce, transport,

accommodation and food service

1 Managing directors and chief executives 4.6 95.4 16.2 83.8

2 Shop sales assistants 20.5 79.5 32.5 67.5

3 Retail and wholesale trade managers 9.8 90.2 13.5 86.5

4 Sales and marketing managers 16.9 83.1 26.4 73.6

5 Waiters 42.3 57.7 54.7 45.3

6 Cooks 66.7 33.3 77.0 23.0

7 General office clerks 9.5 90.5 8.1 91.9

8 Shopkeepers 6.6 93.4 6.6 93.4

9 Sales workers not elsewhere classified 44.3 55.7 46.6 53.4

10 Supply, distribution and related managers 6.8 93.2 15.5 84.5

JMN. Information and

communication; Professional, scientific and

technical activities;

Administrative services

1 Managing directors and chief executives 7.7 92.3 6.2 93.8

2 Clerical support workers not elsewhere classified

7.1 92.9 5.7 94.3

3 Sales and marketing managers 17.6 82.4 17.6 82.4

4 Accountants 2.0 98.0 14.9 85.1

5 Information and communications technology service managers

15.6 84.4 15.6 84.4

6 General office clerks 9.3 90.7 9.3 90.7

7 Finance managers 8.1 91.9 2.9 97.1

8 Engineering professionals not elsewhere classified

15.6 84.4 15.2 84.8

9 Software developers 15.6 84.4 9.7 90.3

10 Human resource managers 16.7 83.3 20.0 80.0

PQ. Education and human health

1 Education managers 6.2 93.8 6.3 93.7

2 Managing directors and chief executives 7.0 93.0 2.9 97.1

3 Specialist medical practitioners 22.0 78.0 29.3 70.7

4 Health services managers 7.5 92.5 15.4 84.6

5 General office clerks 10.9 89.1 13.3 86.7

79

Sector Rank Job title

Last 5 years

Next 5 years

No c

hange a

t all

Changes r

eport

ed

No c

hange a

t all

Changes r

eport

ed

6 Dentists 9.3 90.7 14.0 86.0

7 Personal care workers in health services not elsewhere classified

13.2 86.8 13.5 86.5

8 Generalist medical practitioners 5.6 94.4 2.8 97.2

9 Health professionals not elsewhere classified 10.0 90.0 17.2 82.8

10 Primary school teachers 6.5 93.5 3.2 96.8

Q20. Thinking about these jobs in your workplace, please indicate if and to what extent the use of ICT has changed the way job tasks are carried out. Please refer to the timespan of the last 5 years. Q21a. Thinking about these jobs in your workplace, please indicate if and to what extent you think the use of ICT will change the way job tasks are carried out. Please refer to the timespan of the next 5 years. N=6,264 Source: European Digital Skills Survey (unweighted values)

4.6 Summary

This section has provided an overview of the demand for and the importance

assigned to different types of digital skills by employers, establishing differences

across occupations in terms of skills required. The key findings are:

Digital skills can be broadly understood as the ability to locate, organise,

understand, evaluate, create and share information using digital technology, at

different levels of competence;

A set of 10 digital skills was considered in the survey and were clustered in 3 main

synthetic indicators, based on the level of competences required to the worker:

Basic digital skills, Advanced digital skills, Specialist digital skills;

The demand for digital skills is clearly related to the job role of the worker;

The evidence gathered through the Digital Skills Survey indicates that in some job

categories and in relation to specific types of digital skills already more than 90% of

jobs require digital skills;

Workplaces were surveyed about the level of importance (measured on a scale

from 1 to 5, where 1 is ‘not at all important’ and 5 is ‘essential’) they assign to

digital skills possessed by their workforce;

The demand for basic digital skills is generally higher across occupations, but with

different degrees of importance. Advanced and specialist digital skills are required

to a moderate to low level of importance;

Basic digital skills are required above all for managers, but also for professionals,

technicians and associate professionals, clerical support workers or skilled

agricultural workers (about 90% of workplaces surveyed report the need); basic

digital skills are less required in occupations such as building workers (almost half

of workplaces), plant machine operators (34%) and elementary occupation (27%);

The basic digital skills more frequently required for all the job categories are those

related to the search for, collection and processing of information via the internet

and communicating using email;

80

Advanced digital skills are required mostly by professionals (54% of workplaces)

and technicians (52%) and to a lesser extent by clerical workers (45%), managers

and building workers (31% each). This type of skill is much less important for other

occupations;

Specialist digital skills are required by a significant proportion of workplaces by

professionals and technicians (43% and 44% respectively), and to a lesser extent

to managers (33% of workplaces), skilled agricultural workers and clerical workers

(25% and 22% respectively);

The specialist digital skills most frequently required across all occupations are

around the ‘design and maintaining of ICT for the workplace’;

Different characteristics of workplaces (such as economic sector, size, market of

reference, characteristics of workforce, etc.) are correlated to the different levels of

importance assigned to the type of digital skills.

The section furthermore provided an overview of digital skills held by workers in

different occupations. The key findings are:

The type of digital skills required by employers mirrors the type of digital skills they

possess;

The majority of employees in each job category possess at least basic digital skills,

but the proportion varies greatly according to the occupation, with the highest

share among managers (90%), clerical workers and skilled agricultural workers

(more than half);

Advanced digital skills are possessed more frequently by workers employed as

managers (19%), skilled agricultural workers (21%) and to a lesser extent by

clerical workers (13%), technicians (10%), professionals (9%), building workers

(8%), sales workers (8%), elementary occupations (7%) and finally as plant

machine operators (7%);

Specialist digital skills are more common among workers employed as skilled

agricultural workers and managers (25% and 23% respectively) and to a lesser

extent among clerical workers, technicians and professionals (14%, 12%, 10%

respectively).

Finally, the chapter presented illustrative examples of the digital skills required

by specific jobs that are not office-based, providing a picture of how digitisation is

increasingly penetrating and transforming existing jobs and their quality, using

qualitative information. The key findings are:

Overall, basic digital skills are required for jobs in all the occupational groups; the

other types of digital skills do not appear as relevant across sectors and jobs;

All the most crucial jobs across sectors are considered to require all types of digital

skills to some level of importance, although basic digital skills are required to a

moderate to higher level of importance;

The use of digital technologies has changed the way job tasks are carried out in

most of the selected jobs for the vast majority of workplaces in most of the sectors;

The majority of workplaces consider that ICT will continue changing the way job

tasks are performed in the next five years, especially with regards to the top-

ranked jobs, even though to some extent future changes are expected to be less

intense for certain occupations.

81

CHAPTER 5. THE DIGITAL SKILLS CHALLENGE IN EUROPEAN

WORKPLACES

This chapter explores the issue of digital skills gaps within the workforce in the

European workplaces covered by this survey. Gaps occur when existing employees lack

proficiency in dealing with digital technologies to perform their job tasks. Such digital

skills gaps can inhibit an establishment’s ability to perform efficiently, therefore

impacting negatively on its productivity and profitability, as seen in the studies and

evidence presented in Chapter 1.

The aim of this chapter is thus to respond to the following research questions:

What are the most common digital skills gaps in the workforce according to

employers?

What are the differences across economic sectors and occupations in terms of

digital skill gaps?

What are the actions undertaken by employers to address existing digital skill

gaps?

What are the differences across economic sectors and occupations in terms of

actions undertaken by the employers to address the lack of digital skills?

The chapter presents the incidence of digital skills gaps in European workplaces,

including at sectoral and workplace-size levels, and the digital skills gap density (that is

the proportion of workforce with digital skills gaps) in relation to specific occupations

and types of digital skills. Furthermore, the chapter also presents the impacts of digital

skills gaps on the overall performance of the workplace, and the outcome of such

impacts from an employer’s perspective. The actions undertaken to address the digital

skills gaps are also presented and discussed, alongside the obstacles and bottlenecks to

increase the availability of digital skills in the workplace.

5.1 Defining digital skills gaps

As noted in Chapter 1, there is not a single definition of digital skills gaps, and existing

definitions of skills gaps have been adapted to the context of this study. In particular, a

skills gap can be defined as “a situation where the level of skills of the existing

workforce in a firm is less than required to perform a job adequately or to match the

requirements of a job” (Cedefop, 2015a, p.27). A skills gap corresponds therefore to

the underskilling of workers, that is “a situation in which an individual lacks the skills

and abilities to perform the current job adequately” (Cedefop, 2015, p. 26).

Adapting such definitions to the context of this survey, a digital skills gap has

therefore to be understood as a situation in which the level of digital skills of the

existing workforce in a workplace is less than required to perform a job

adequately or to match the requirements of a job. In other terms, digital skills

gaps exist in workplaces where the workforce lacks the right skills to deal with digital

technologies to perform their current job adequately.

82

From an operational viewpoint, the digital skills gap density (that is the percentage of

underskilled workforce) has been calculated as the number of employees not proficient

in carrying out job tasks involving the use of digital technologies as a proportion of the

total number of employees carrying out the same job tasks involving the use of digital

technologies. This is calculated for each occupation and in relation to each type of

digital skill:

Digital skills gap density = [Number of employees with a non-proficient level of

digital skill n / Number of employees with digital skill n] *100

The digital skills gaps density is expressed in percentage terms and corresponds to the

proportion of underskilled workforce in the area of use of digital technologies. The

higher the value calculated, the larger the digital skills gap.

5.2 Digital skill gaps in European workplaces

The vast majority of European workplaces (85%) report that all of their employees are

fully proficient at performing job tasks involving the use of digital technologies.

Nevertheless, around one in seven workplaces (15%) consider that some of their staff

are not fully proficient when carrying out tasks using digital technologies at work, and

therefore report digital skills gaps in their workforce32.

Larger employers are more likely than smaller employers to report digital skills gaps, in

part because with a larger workforce it is more probable to have underskilled staff, but

most probably because (as seen in Chapter 3) large workplaces are more likely to use

digital technologies. 57% of large workplaces report issues of digital skills gaps,

followed by the small and medium-sized workplaces (24% and 23% respectively), while

only about 12% of micro-sized workplaces (2 to 9 employees) report this issue.

At sectoral level, the macro-sector ‘manufacturing and utilities’ is the most affected by

digital skills gaps (22%), followed by the ‘construction’ sector (19.5% of workplaces

reporting digital skills gaps), the macro-sectors ‘commerce, transport, accommodation

and food service’ (18%) and ‘education and human health’ (17%). The rest of the

sectors display a proportion of workplaces with issues of digital skills gaps lower than

the average, in particular in the agricultural sector where only 0.6% state they have

digital skills gaps (Figure 5.1).

32 The figure regarding the proportion of workplaces with digital skills gaps was calculated comparing variables Q17 and Q23 of the questionnaire and recoding the ‘Don’t knows’ as missing values. In this way, only those who responded to the question were considered to calculate the proportion of workplaces with digital skills gaps.

83

Figure 5.1 – Workplaces reporting digital skill gaps by sector and size, EU28 (% of workplaces)

Q17/Q23. Please provide your best estimate of the approximate number or share of employees carrying out such tasks and indicate how many of them are fully proficient in carrying out the tasks. Please note that a proficient employee is someone who is able to do the job/carrying out the task to the required level. Number of valid responses: 4,569 N= 5,634,045 Source: European Digital Skills Survey (weighted values)

The analysis by sampled countries displays that in Portugal and Finland a relatively

lower proportion of employers than in the rest of the countries consider themselves to

have digital skills gaps (3% and 10% respectively). Nevertheless, larger companies in

Portugal seem particularly concerned with digital skills gaps, as 54% of them report this

issue. Digital skills gaps are reported by 22% of workplaces in Germany, 18% in the

UK, 27% in Sweden and 15% in Slovakia (Figures A5.1 and A5.2 in Annex 2).

5.3 Digital skills gaps density

The digital skills gaps density in European workplaces is now presented by occupation

and type of digital skills. Broadly speaking, at an occupational level, digital skills gaps

are more likely to be found in the high-skilled (managers, technicians) and in medium-

skilled (clerical workers, sales workers) occupations, and to a lesser extent in the low-

skilled occupations, with the exception of workers in elementary occupations. This can

be related to the fact that (as noted in Chapter 4) workers in the high-skilled and the

medium-skilled occupations are more likely to use digital technologies to perform their

job than those in the low-skilled and the unskilled occupations, although the proportion

of workers in elementary occupations for which digital skills are required is also

significant.

12.3

23.9 23.3

56.7

0.6

22.4 19.5 18.0

13.7 17.1

14.8

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84

Collectively, as set out in Chapter 2, the first five job categories (managers,

professionals, technicians, clerical workers and sales workers) account for almost 70%

of the total workforce employed by the workplaces covered by this survey.

Figure 5.2, shows that the digital skills gaps density is around 13% for managers, and

about 11% with regards to professionals in all the three categories of digital skills

(basic, advanced, specialist). Technicians display a relatively much higher digital skills

gap density in relation to basic digital skills (22%) than in relation to the other two

categories of digital skills: 17% with regards to advanced digital skills and 16% with

regards to specialist digital skills. Clerical workers are slightly more frequently

underskilled with reference to basic digital skills (17%) and less in relation to the other

two groups of digital skills: 16% for advanced digital skills and 15% for specialist digital

skills.

The digital skills gaps density for sales workers are 19% and 18% in relation to basic

and advanced digital skills, but reaches 23% in relation to specialist digital skills. That

result is influenced by the ‘programming and software development’ component that

accounts significantly for this bundle of digital skills (the detailed breakdown by specific

digital skills is reported in Table A5.1 in Annex 2). Both skilled agricultural workers and

building workers display a comparatively lower digital skills gaps density in two out of

the three categories of digital skills (basic and advanced digital skills). Digital skills gaps

density in the category of plant machine operators is about 15% for all the three

categories of digital skills.

Finally, workers in elementary occupations have a high digital skills gaps density in all

the categories of digital skills, in spite of being required to use digital technologies only

to a limited degree, as indicated in Chapter 4. It is likely that the penetration of digital

technologies into tasks normally performed by unskilled workers (as seen in the studies

reported in Chapter 1) has resulted in a growing need for digital skills in a category of

workers that, in the past, would generally not have needed them at all or at a very low

level.

85

Figure 5.2 – Digital skills gaps density by occupation and type of digital skills, EU28 (%)

Q17/Q23. Please provide your best estimate of the approximate number or share of employees carrying out such tasks and indicate how many of them are fully proficient in carrying out the tasks. Please note that a proficient employee is someone who is able to do the job/carrying out the task to the required level. Number of valid responses=4,608 (job category: Managers); 2,224 (job category: Professionals); 1,886 (job category: Technicians); 3,929 (job category: Clerical workers); 2,298 (job category: Sales workers); 858 (job category: Skilled agricultural workers); 1,824 (job category: Building workers); 1,145 (job category: Plant machine operators); 1,319 (job category: Elementary occupations) N=5,644,799 (job category: Managers); 3,463,858 (job category: Professionals); 2,045,270 (job category: Technicians); 4,172,004 (job category: Clerical workers); 3,815,976 (job category: Sales workers); 1,740,841 (job category: Skilled agricultural workers); 2,475,089 (job category: Building workers); 1,059,179 (job category: Plant machine operators); 1,164,035 (job category: Elementary occupations) Source: European Digital Skills Survey (weighted values)

5.4 Impacts of digital skills gaps

Digital skills gaps at a workplace, determined by the fact that some workers are not

fully proficient in carrying out tasks involving ICT use, may affect the overall workplace

performance as shown in the available evidence presented in Section 1.4 of Chapter 1.

In the majority of workplaces with digital skills gaps, respondents indicated that they

felt that there was no impact of digital skills gaps (62% of total workplaces with digital

skill gaps). However, about 36% of workplaces with digital skill gaps do report a

negative impact on the overall performance. Of those, about the same proportion

(18%) report either a minor impact or a major impact (Figure 5.3).

13.9

11.6

21.8

17.2

19.5

8.4

5.6

15.9

20.6

13.2

10.4

17.4

15.8

17.6

8.0

10.9

16.5

15.3

13.3

12.8

16.4

14.7

22.8

14.0

12.9

15.3

17.6

Managers

Professionals

Technicians

Clerical workers

Sales workers

Skilled agric workers

Building workers

Plant machine operators

Elementary occupations

Basic digital skills Advanced digital skills Specialist digital skills

86

Figure 5.3 - Workplaces by impact of digital skill gaps on overall performance, EU28 (%)

Q26. Thinking about your workplace as a whole, does the fact that some of your employees are not fully proficient in carrying out the indicated tasks involving ICT use have an impact on your workplace performance? (Yes, a major impact/Yes, a minor impact/No/Don’t know/Not applicable-100% proficient) Note: workplaces with fully proficient workers (e.g. without digital skills gaps) are excluded from the analysis Number of valid responses: 6,776 N=12,268,096

Source: European Digital Skills Survey (weighted values)

64% of micro-sized workplaces with digital skills gaps report that there is no impact of

digital skill gaps on their overall performance, followed by 61.5% of large workplaces.

On the other hand, small and medium-sized workplaces appear more concerned with

digital skill gaps; around 45% of both categories report an impact on their overall

performance. Small-sized workplaces most frequently report a major impact (24%, with

22% noting a minor impact), while medium-sized workplaces are less likely to report a

major impact (19%, with 26% noting a minor impact) (Table A5.4, Annex 2). The

analysis at sectoral level shows that only about 25-30% of workplaces with digital skills

gaps in the macro-sectors ‘Manufacturing and utilities’ and ‘Construction’ report an

impact on the overall performance of the workplace. In the other economic sectors,

there are greater concerns about the impact of digital skills gaps; almost half of

workplaces expect impacts on the overall workplace performance in the macro-sectors

‘Information and communication/Professional, scientific and technical

activities/Administrative and support service activities’ and ‘Education and human

health’ (Table A5.4, Annex 2).

17.6

18.5

61.7

2.1

Yes, a major impact

Yes, a minor impact

No

Don't know

87

In order to identify additional characteristics of the workplace correlated to the existing

digital skills gaps having a major impact on overall performance, a logistic regression

model was calculated (Figure 5.4, and the full model is reported in Table A5.5 in Annex

2). Workplaces reporting a major impact of digital skill gaps are more likely to be small

or medium-sized, as seen in the descriptive analysis above. Also, the likelihood of

reporting a major impact is highest for the ‘Information and

communication/Professional, scientific and technical activities/Administration support

activities’ macro-sector. Workplaces with a higher incidence of female employees (more

than 75%), of employees holding a university degree (more than 75%) or of younger

employees (between 51% and 75% of employees aged less than 30 years), and

workplaces active on the international market, are more likely to report a major impact

of digital skills gaps on their overall performance. Conversely, workplaces which are

part of a group as a subsidiary site, those belonging to other economic sectors, and

those with a high proportion of older workers are less likely to report a major impact of

digital skills gaps on the overall performance of the workplace.

88

Figure 5.4 – Logistic regression: probability that digital skills gaps have a major impact

on overall performance (Odds ratios).

* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; P,Q: Education and human health and social work activities. Q26. Thinking about your workplace as a whole, does the fact that some of your employees are not fully proficient in carrying out the indicated tasks involving ICT use have an impact on your workplace performance? (Yes, a major impact/Yes, a minor impact/No/Don’t know/Not applicable-100% proficient) Note: The regressors ‘Female rate’, ‘University rate’, ‘Young rate’. ‘Old rate’ have four categories, reflecting the proportion of female, university degree holders, younger than 30, older than 50 workers of the total workforce. The four categories are as follows: between 0% and 25%, 26% to 50%, 51% to 75%, more than 75%. Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Note: workplaces with fully proficient workers (e.g. without digital skills gaps) are excluded from the analysis Source: Elaboration on European Digital Skills Survey (weighted values)

89

The analysis at country level shows that the majority of workplaces with digital skills

gaps consider that digital skill gaps will not have an impact on the overall performance

of the workplace: Germany (86%), Finland (74%), Slovakia (74%), Sweden (63%) and

the UK (59%). In Portugal, on the other hand, the vast majority (83%) of workplaces

express concern about the impact of digital skill gaps, although half expect a minor

impact (Figure A5.3, Annex 2).

As mentioned in the introduction to this chapter, the impact of digital skill gaps on

workplaces’ overall performance can result in different types of negative outcomes as

pointed out in the literature (see Chapter 1, Section 1.4). Of the total number of

workplaces expecting digital skill gaps to impact on the overall performance of

workplace, a general loss of productivity is the expected impact for more than 45% of

them (corresponding to around 15% of total workplaces). Slightly lower proportions of

workplaces consider a decrease in the number of customers or other negative impacts

as the outcome of digital skill gaps in the workforce (42.5% and 41% respectively).

Finally, only 32.4% of workplaces reporting an impact of digital skill gaps on their

overall performance consider digital skills gap to lead to a decrease in the number of

contracts received (Figure 5.5).

Figure 5.5 - Workplaces reporting impacts of digital skill gaps by expected type of impact on overall performance, EU28 (%)

Q26a. What type of impact does this have on your workplace performance? Number of valid responses: 2,591 N=4,427,804 Source: European Digital Skills Survey (weighted values)

5.5 How do workplaces deal with digital skill gaps?

In spite of the potential for digital skills gaps to impact negatively on workplace

performance, the vast majority of workplaces in the European Union have not taken any

45.6 42.5

41.0

32.4

6.8

Loss of productivity Decrease in thenumber ofcustomers

Other negativeimpact

Decrease in thenumber of contracts

Don't know

90

steps to improve the digital proficiency of employees (77%), while 11% reported not

having taken any steps, although they have plans to. Only 12% of total workplaces

have taken action to tackle with digital skill gaps (Figure 5.6).

Figure 5.6 - Workplaces reporting action to tackle digital skill gaps, EU28 (%)

Q27. Has your workplace taken any steps to improve the proficiency of employees to enable them to carry out the tasks involving ICT use? Number of valid responses: 6,776 N=12,268,023 Source: European Digital Skills Survey (weighted values)

Micro-sized workplaces are most likely to say they have not taken any steps to reduce

digital skills gaps (81%), with a further 10% having plans to take steps to tackle digital

skill gaps. On the other hand, medium and large workplaces are those more likely to

have taken action (36% and 29% respectively). At sector level, the sector with the

highest proportion ofworkplaces having taken action is the ‘Education and human

health’ macro-sector (21%), which also has the highest incidence of workplaces

planning to take steps to tackle gaps (15%). ‘Agriculture’ and ‘Construction’ are the

sectors with the highest proportion of workplaces that have not undertaken any

initiative to reduce digital skill gaps (85% and 81% respectively) (Table A5.6, Annex 2).

A logistic regression model on the likelihood that workplaces have undertaken steps to

tackle digital skills gaps as been calculated (Figure 5.7 – full model in Table A5.7, Annex

2). The model confirms a strong positive correlation between the size of the workplace

and the probability of undertaking steps to tackle digital skills gaps. In addition,

workplaces with a high rate of female workers and those active on the international

markets are more likely to undertake actions to deal with digital skills gaps. Conversely,

workplaces with a high incidence of younger workers, those with a proportion of older

workers between 26% and 50%, and those active on national markets are less likely to

undertake any actions to tackle digital skills gaps.

12.1

11.2

76.7

Yes

No, but have plans to

No

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Figure 5.7 – Logistic regression: probability that workplace has undertaken steps to

tackle digital skills gaps (Odds ratios)

* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; P,Q: Education and human health and social work activities. Q27. Has your workplace taken any steps to improve the proficiency of employees to enable them to carry out the tasks involving ICT use? Note: The regressors ‘Female rate’, ‘University rate’, ‘Young rate’. ‘Old rate’ have four categories, reflecting the proportion of female, university degree holders, younger than 30, older than 50 workers of the total workforce. The four categories are as follows: between 0% and 25%, 26% to 50%, 51% to 75%, more than 75%. Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Note: workplaces with fully proficient workers (e.g. without digital skills gaps) are excluded from the

analysis Source: Elaboration on European Digital Skills Survey (weighted values)

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The 12% of workplaces which had taken actions to tackle digital skills gaps reported

that training is the most common means selected to deal with digital skill gaps33. In

particular, on-the-job training was used by 84% of workplaces which had undertaken

action to address digital skills gaps. External training and development programmes are

mentioned by 58% of workplaces which had taken action to deal with digital skills gaps.

The other types of actions undertaken are related to changes in work organisation,

including changes in working practices (reported by 49% of workplaces which had taken

action), and reallocating tasks (indicated by 47% of workplaces which had taken

action). Recruiting new staff with the necessary skills, or hiring temporary staff, was an

option taken up by 39% and 25% of workplaces respectively, while outsourcing tasks

involving ICT was used by almost 30% of workplaces which took some action to tackle

digital skill gaps. The last two options - secondment of employees from other

workplaces within the same organisation (for workplaces part of multi-establishment

organisations) and other (non-specified) actions - were the least popular options,

reported by less than 14% and about 11% of workplaces which had taken action to

reduce digital skills gaps (Figure 5.8).

Figure 5.8 - Workplaces reporting having taken action to tackle digital skill gaps by type of action undertaken, EU28 (% of workplaces with digital skill gaps which

undertook actions)

Q28. Which of the following steps is your workplace taking to overcome the fact that some of its employees

are not fully proficient in carrying out tasks involving ICT use? (Please select all that apply) Number of valid responses: 1,486 N=1,476,489 Source: European Digital Skills Survey (weighted values)

33 Eurofound’s Sixth European Working Conditions Survey (2015) carried out on employed individuals indicates that only 37% of workers in the European Union received external training in the last 12 months, even though the majority for less than 5 days, and 33% of employees received on the job training. Differences can be observed among different countries. It is worth to point out that those who are employed in low-skilled occupations, who need the most training, receive actually less; similarly people who have fixed term contracts and people who work part-time receive less training than people in more stable positions: considering that these types of contracts are increasingly more common, this trend might be problematic (Eurofound, Experts’ workshop, Brussels 7th of October 2016).

11.2

13.8

29.3

24.6

39.0

47.2

48.8

58.3

84.4

Other

Secondment of employees from other…

Outsourcing of tasks involving ICT use

Hiring temporary staff with needed skills…

Recruiting new staff with needed skills

Reallocating tasks

Changing working practices (e.g.task sharing)

External training and development programmes

On the job training and development…

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Larger workplaces are more likely to provide training (both on-the-job and external) to

their employees to reduce digital skills gaps than smaller workplaces. While almost 80%

of micro-sized workplaces chose on-the-job training to reduce digital skill gaps, 90% of

small-sized, 95% of medium-sized and almost the totality of large workplaces chose

this option. Similarly, only around half (52%) of micro-sized workplaces opted for

external training to reduce digital skill gaps of their employees, while the proportion of

the rest of workplaces which chose this type of action is much higher (69% of small-

sized workplaces, 71% of medium-sized workplaces and 64% of large workplaces).

The interventions on work organisation to reduce digital skills gaps in the workforce,

such as changing working practices or reallocating tasks among employees, are more

common among larger workplaces than smaller organisations (about 40% of micro-

sized workplaces versus about 60% of larger-sized workplaces).

Also, actions such as hiring new staff (both on a permanent or temporary basis),

outsourcing tasks or seconding staff, are more frequently undertaken by larger-sized

workplaces than by micro-sized workplaces. For example, 88% of large-sized versus

32% of micro-sized workplaces recruited new staff. 29% of medium-sized versus 22%

of micro-sized workplaces hired temporary staff. 35% of large-sized versus 28% of

micro-sized workplaces outsourced tasks (Table A5.8 in Annex 2). At sectoral level,

workplaces in the ‘Agriculture’, ‘Manufacturing and utilities’, ‘Information and

communication/Professional, scientific and technical activities/ Administrative and

support service activities’ and the ‘Education and human health’ macro-sectors, are

those which have more frequently undertaken actions to tackle digital skills gaps.

5.6 Barriers to initiatives tackling digital skills gaps

Actions to improve the availability of digital skills do not come without difficulties or

costs. As shown in Figure 5.9 below, 31% of workplaces in the EU which reported

having taken action to deal with digital skill gaps found the costs of training and

development programmes to be excessive, while 26% could not modify work

organisation due to the limited number of employees in the workplace (they therefore

did not have sufficient capacity for staff to ‘absorb’ training). Hiring temporary staff with

the required digital skills, and outsourcing tasks involving ICT use, have been found to

be an overly costly option by about 21% and 19% of the workplaces which undertook

actions to reduce skill gaps. Finally, only around 13% of workplaces reported that

vacancies for jobs involving ICT could not be filled either due to a lack of required skills

or that they stayed open for a long time (Figure 5.9).

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Figure 5.9 - Workplaces reporting difficulties when taking action to tackle digital skill

gaps by type of difficulty encountered, EU28 (%)

Q29. Which – if any – of the following difficulties has your workplace encountered when taking steps to overcome the fact that some employees are not fully proficient in carrying out tasks

involving ICT use? (Please select all that apply)

Number of valid responses: 1,485 N=1,469,631 Source: European Digital Skills Survey (weighted values)

The size of workplaces appear to be very much correlated to a number of these

barriers, such as the excessive cost of training, of hiring temporary staff or of

outsourcing tasks, or the difficulty to find employees with required skills. That is in

addition to the difficulty of modifying work organisation due to the limited size of the

workplace (as displayed in Table A5.9 in Annex 2). Only small proportions of large

workplaces encounter difficulties when taking action to tackle digital skill gaps, with the

exception of digital skill shortages on the labour market, which is reported by 37% of

large-sized workplaces. At sectoral level, the excessive cost (of training, hiring

temporary staff or outsourcing tasks) is an issue reported mostly by workplaces in the

‘Agriculture’ and in the ‘Construction’ sectors, while digital skills shortages are reported

mostly by workplaces in the construction sector.

5.7 Summary

This section has presented the existing digital skill gaps in EU workplaces and has

quantified them (‘digital skills gaps density’) in relation to specific occupations and

types of digital skills gaps. Bearing in mind the definition of the digital skills gap (“a

31.0

26.2

20.7

18.7

13.2

13.1

Excessive cost of training and developmentprogrammes

Modifications to work organisation are not possibledue to limited number of employees in workplace

Excessive cost of hiring temporary staff with therequired skills

Excessive cost of outsourcing of tasks involving ICTuse

Vacancies for jobs involving ICT are not filled due tolack of required skills

Vacancies for jobs involving ICT stay open for a longtime

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level of digital skills of the existing workforce in a workplace which is less than required

to perform a job adequately”), this section has also explored the perceived impacts of

digital skill gaps on business performance, the actions undertaken to tackle digital skills

gaps in the workforce and the main difficulties encountered when taking action. Overall,

the main findings include the following:

15% of total workplaces in the European Union report the existence of digital skills

gaps in their workforce;

Larger employers are more likely than smaller employers to report digital skills

gaps. 57% of large workplaces report issues of digital skills gaps, followed by small

and medium-sized workplaces (24% and 23% respectively), while micro-sized

workplaces appear to be less likely to have digital skills gaps (12%);

At sectoral level, digital skills gaps are more frequently reported in the

‘Manufacturing’ (22%), ‘Construction’ (19.5%), ‘Commerce, transport,

accommodation and food’ (18%) and ‘Education and health’ (17%) sectors. The

other sectors have a lower than average proportion of workplaces reporting digital

skills gaps (14.8%), in particular in the agricultural sector where only 0.6%

consider that they have digital skills gaps. At the country level, workplaces in

Portugal and Finland report the lowest digital skills gap (3% and 10% respectively).

Sweden and Germany have the highest proportion of workplaces stating that they

experience digital skills gaps issues (27% and 22%); At the occupational level,

digital skills gaps exist across all occupations and types of digital skills, meaning

that a proportion of workforce in all occupations is not fully proficient in carrying

out tasks involving the use of digital technologies;

Digital skills gaps are, however, more likely to be reported to be found in the high-

skilled (managers, technicians) and in medium-skilled (clerical workers, sales

workers) occupations, and to a lesser extent in the low-skilled occupations, with the

exception of workers in elementary occupations;

While, for certain occupations, the level of the digital skills gap across types of skills

is relatively similar, for some occupations the digital skills gaps density is higher for

certain type of skills. For example, technicians more frequently lack basic digital

skills, while agricultural skilled workers more frequently lack specialist digital skills;

Skills gaps related to basic digital skills are more concentrated among technicians

(21.8%), elementary occupations (20.6%), sales workers (19.5%) and clerical

workers (17.2%). Skill gaps related to advanced digital skills are more

concentrated among sales workers (17.6%), technicians (17.4%), plant machine

operators (16.5%), clerical workers (15.8%) and elementary occupations (15.3%).

Skills gaps related to specialist digital skills are more concentrated among sales

workers (22.8%), followed by elementary occupations (17.6%) and technicians

(16.4%).

For the majority of workplaces reporting digital skills gaps within their workforce,

the existence of such gaps is not accompanied by concern regarding their impact on

workplace performance. Almost 62% of workplaces reporting digital skills gaps

think that these skill gaps will not have any impact on overall workplace

performance;

More than one third of workplaces with digital skills gaps express concern about the

impact these could have on the workplace performance (36%); about half of them

expect a major impact (17.6%) and the other half a minor impact (18.5%);

Micro-sized workplaces are less likely to report an expected negative impact (64%

reports no impact), followed by large workplaces (61.5%);

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More than two thirds of workplaces in the ‘Manufacturing’, ‘Electricity and gas

supply’ and ‘Construction’ sectors do not expect any impact from digital skill gaps,

versus about half of workplaces in the grouping ‘Information and

communication/Professional scientific and technical activities/Administrative

activities’ and the grouping ‘Education and human health and social work activities’;

According to the logistic regression model, workplaces reporting a major impact of

digital skill gaps are more likely to be small or medium sized (compared to the

micro-sized), belong to the ‘Information and communication’ sector (compared to

the Agricultural sector), have a higher incidence of women employees, of

employees holding a university degree or of younger employees, and are active on

the international market;

At the country level, in Portugal the vast majority of workplaces with digital skills

gaps express concern about the possible impacts on workplace performance,

showing a trend opposite to that in other countries, where the majority consider

there will be no impact;

The type of impact most frequently reported is a loss of productivity (45.6%)

followed by an expected decrease in the number of customers (42.5%), another

(unspecified) negative impact (41%) and a decrease in the number of contracts

(32.4%);

Only 12% of workplaces with digital skill gaps have undertaken actions to tackle

the problem, and 11% have plans to do so;

81% of micro-sized workplaces have not taken any steps to deal with digital skill

gaps, versus around half of all the other workplaces;

Only 9% of micro-sized workplaces have undertaken actions to deal with digital skill

gaps, versus almost one third of medium-sized and large workplaces;

Workplaces in the ‘Education and health’ macro-sector are those most frequently

taking action to deal with digital skills gaps or plan to do so;

Training (both in the form of on-the-job training and development programmes and

external training) is the most common action undertaken to tackle digital skill gaps;

The other types of actions undertaken are related to work organisation changes:

changing working practices was reported by 49% of workplaces which took action,

and reallocating tasks, indicated by 47%. Around one third of workplaces hire

permanent or permanent staff or outsource tasks to reduce digital skill gaps in their

workforce;

The most commonly reported barrier when tackling digital skill gaps is the

excessive cost of most of the available options to enhance the skills of workers. At

sectoral level, the excessive cost (of training, hiring temporary staff or outsourcing

tasks) is an issue reported mostly by workplaces in the ‘Agriculture’ and

‘Construction’ sectors. Micro-sized workplaces are most likely to report the

excessive cost of most of the available options.

Only limited proportions of large workplaces encounter difficulties when taking

action to tackle with digital skill gaps, with the exception of digital skill shortages in

the overall labour market, which is reported by 37% of large-sized workplaces.

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CHAPTER 6. CONCLUSIONS AND RECOMMENDATIONS

The findings of this study ‘ICT for work: Digital skills in the workplace’ appear to be in

line with the research literature relating to the impact of digitisation on the world of

work. At the same time, they provide robust evidence to respond to the research

questions formulated at the conceptualisation stage of the study, and to fill the research

gaps identified. Such findings were presented, discussed and validated in an experts’

workshop (main points of discussion and characteristics of attendees are reported in

Section 6.2), which also allowed identification of priorities and areas of concern, and to

formulate recommendations. These are presented at the end of this Chapter 6.

6.1 Main results and related learning points

The results of the survey are based on a sample of 7,800 workplaces representative of

13,803,113 workplaces in 12 economic sectors across the European Union. They have

been deliberately chosen from sectors not traditionally related to digital technologies:

agriculture, forestry and fishing; manufacturing; electricity, gas, steam and air

conditioning supply; construction; wholesale and retail trade and repair of motor

vehicles and motorcycles; transportation and storage; accommodation and food service

activities; information and communication; professional, scientific and technical

activities; administrative and support service activities; education; human health and

social work activities.

More than 80% of these workplaces (81.6%) can be classified as micro-sized (2 to 9

employees), 15% are small-sized, 3% are medium-sized, and the remaining 0.5% are

large workplaces. The vast majority of such workplaces belong to the private sector

(90%), and are not part of a corporate group of companies (86%). Those belonging to

a group, on the other hand, represent the headquarters in seven out of ten cases. In

addition, the main geographical reference market for most of these workplaces is the

local or regional market (65%), while a limited proportion of workplaces operates or

trades at national level and only 13% at international level.

The workplaces surveyed are representative of workplaces which employ a total of

150,563,540 employees. 39% of employees are female, 20% are older than 50 years

of age, and 19% are younger than 30 years of age. Almost 27% of the employees hold

a university degree. Only 5% are employed as managers, with largest proportions of

employees surveyed working as professionals (19%), technicians (17%) and sales

workers (17%). Smaller proportions of employees work as clerical workers or building

workers (11% in both cases), or in the category of elementary occupations (10%).

Workers employed as plant machine operators represent 8% of total employees and

skilled agricultural workers are 1% of the total. Almost 41% of workplaces employ

managers.

In this context, the survey results show that digital technologies are widely used by

workplaces in the European Union. Personal computers (both desktop and, to a lesser

extent, portable computers), and broadband technology to access the internet, are

used by 90% of workplaces in the EU. Much less common is the use of portable digital

devices and intranet platforms (most probably due to the reduced size of workplaces),

while the use of other types of digital technologies such as CNC machines and tools or

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robots is very limited, and very much sector specific, being more common in the

agriculture and manufacturing sectors.

The current level of use of digital devices seems to have been supported by specific

investment strategies over the last five years, aimed at introducing ICTs mostly to

improve the overall efficiency or the business volume of workplaces. However,

investments in ICTs appear to be less common among micro-sized workplaces, which

are more likely than other workplaces to report a total lack of investment in ICT in the

recent past. At sectoral level, recent investments in ICTs seem to be more frequent

among workplaces in sectors with traditionally low levels of digital intensity (e.g.

agriculture, manufacturing or construction), most probably in light of recent changes in

the production strategy pursuing higher efficiency, but also in sectors with higher levels

of digital intensity (e.g. information and communication sector).

In the European Union the proportion of workplaces requiring their employees to

possess digital skills varies greatly according to the type of job and the type of digital

skills. Basic digital skills are the most commonly required in all the occupations, and

particularly for high and medium-skilled jobs. Almost all workplaces require their

managers to possess basic digital skills and around 90% of employers state that

professionals, technicians, clerical workers or skilled agricultural workers are required to

possess at least basic digital skills. Eight out of ten workplaces require basic digital

skills for sales workers. Although in much smaller proportions, workplaces also often

require basic digital skills for building workers (almost half of workplaces), plant

machine operators (34% of workplaces) and even employees in elementary occupations

(27% of workplaces).

Advanced digital skills are much less required by employers. It is mostly professionals

(54% of workplaces), technicians (52%) and to a lesser extent clerical workers (45%),

managers and building workers (31% of workplaces in both cases) who are required to

have these type of digital skills, while they are considered much less important for all

other occupations. Specialist digital skills are required mostly for workers employed as

professionals and technicians (43% and 44% respectively), and to a lesser extent for

managers (33% of workplaces). Advanced and specialist digital skills are very much

related to specific sectors (in particular manufacturing and information and

communication) and are more likely to be required in larger workplaces.

There is a high proportion of workplaces which do not consider digital skills to be

important for several low or non-skilled occupations. The proportion of employees

equipped with the required digital skills broadly reflects the level of importance attached

by the employers to the specific types of digital skills in the different job categories.

Nevertheless, 15% of workplaces report the existence of digital skill gaps in their

workforce, indicating that a proportion of their employees are not fully proficient in

carrying out tasks involving the use of digital technologies. Large workplaces, and

workplaces in the manufacturing or construction sectors, are more likely to report

digital skill gaps. Overall, the density of the digital skills gap greatly varies according to

the type of digital skills in relation to the different occupations. Digital skills gaps are

more likely to be found in the high-skilled (managers, technicians) and in medium-

skilled (clerical workers, sales workers) occupations, and to a lesser extent in the low-

skilled occupations, with the exception of workers in elementary occupations.

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Most workplaces (62%) that report an issue of digitally underskilled workers, do not

however consider that existing digital skills gaps have an impact on workplace

performance, while similar proportions of the remainder state that digital skill gaps

have either a major or a minor impact on it. Micro-sized and, to a lesser extent, large

workplaces, and workplaces in the manufacturing and construction sectors are more

likely to say that digital skills gaps are not impacting on performance. Those who do say

there is an impact are most likely to say this will result in a loss of productivity or a

decrease in the number of customers.

Awareness of the existence of digital skills gaps is frequently not accompanied by

initiatives undertaken to address the issue: 77% of workplaces reporting digital skills

gaps have not undertaken any actions, while only 12% have done so, and 11% plan to.

Micro-sized workplaces have been least active in this respect, with only 9% having

taken action to tackle digital skill gaps and 81% having not undertaken any actions at

all. Overall, training appears to be the most common action undertaken to tackle the

digital skills gaps, while changes to work organisation and the hiring of new staff appear

to be much less common. Excessive cost seems to be the main barrier encountered

when undertaking actions to deal with digital skills gaps.

Overall, the evidence gathered corroborates existing research demonstrating that digital

technologies are becoming increasingly widespread across a wide range of workplaces,

also in economic sectors not traditionally related to digitisation. The study also confirms

(as observed in existing research) that digitisation is resulting in an increasing demand

for digital skills across different types of occupations and jobs in a range of industries,

and that employers have a shortfall in the availability of appropriate digital skills.

However, the evidence gathered also provides answers to existing gaps in the research

on the topic, providing detailed information on the impact of digital technologies on the

existing workforce and the evolving need for digital skills. Firstly, it provides a more

granular picture of the type of digital technologies currently used by firms in the

European Union and their underpinning investment strategies. Secondly, the study

identifies the digital skills that are most frequently required by employers, including the

type and level of importance in relation to all the occupations and specific jobs covered

by the survey. Thirdly, the findings set out the digital skills which currently exist in the

workplace by type and in relation to specific occupations. Fourthly, the evidence

provides a picture of the digital skills challenge for European workplaces, relating to the

lack of employees who are fully proficient in performing tasks requiring digital

technologies. Finally, the study provides empirical evidence on the perceived impact of

the lack of digital skills on business performance, and identifies the actions undertaken

by employers, and the barriers encountered.

Overall, the vast body of evidence collected provides valuable learning in relation to the

main policy areas for action, which forms the basis for the series of recommendations

set out at the end of this chapter. The main learning points can be summarised as

follows:

First, the evidence shows that digital technologies are increasingly and extensively

used across the economy. However, digital skills appear to be currently required

mostly for the high-skilled and, to a lesser extent, for the medium-skilled

employees to perform their job tasks, while are less likely to be required for the

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low-skilled or the unskilled (or frequently not required at all, even at basic level).

The polarising trends, confirmed by other available evidence34, draws attention to

the fact that a high share of workers are in occupations which do not require (or

require to a very limited extent) digital skills. This dichotomy risks widening the

digital divide, leaving a proportion of workers lagging behind and at risk of digital

exclusion, who would hence benefit from specific attention.

Second, the availability of digital skills is not always sufficient to meet employers’

needs, as demonstrated by the reported existence of digital skills gaps in the

workforce, even as regards basic digital skills. Different factors contribute to this

situation. Digital skills are available to different degrees across Member States, but

the provision often does not meet employer’s needs. The speed at which workers

are being provided with the right digital skills in the right locations is frequently

slower than the speed at which digital technologies are evolving. As a result, digital

skills are often also more subject to obsolescence. An age-related issue can also be

identified, as older workers are less likely to be equipped with digital skills than

younger workers (and hence likely to be less proficient in carrying out tasks

involving digital technologies).

Third, results show that even if workplaces report that a proportion of their

workforce is not fully proficient in carrying out tasks involving the use of digital

technologies, they often do not feel that these skills gaps impact on workplace

performance and hence often do not take action to deal with the issue.

Fourth, size matters. For micro and small-sized workplaces, it may not be viable to

make investment to increase ICT use. Also, for those micro and small-sized

employers who have a high demand for digital skills, simply allocating staff time to

acquire them is both difficult (loss of productive time), and expensive (training and

development programmes need to be brought in). This is less an issue for bigger

employers with more available resources who can manage capacity, develop

training programmes or buy them in. But it is also important to remember that

some micro or small-sized companies do not need ICT at all, and therefore do not

demand digital skills. In this context, it is important to underline that micro-sized

workplaces represent more than 80% of the workplaces in the European Union.

Fifth, the skills challenges appear highly dispersed, as different sectors have

different demands, and the balance of supply and demand is different across

Member States. The sectoral analysis indicates that the use of digital technologies

is uneven across economic sectors, particularly concerning the types of digital

technologies, their speed of penetration and also the related demand for digital

skills, with some sectors clearly leading the ‘digital revolution’ and some others

following at a slower pace.

34 As pointed out by Eurofound expert at the Experts’ Workshop (Brussels 7th of October 2016, see Section 6.2 for details), an increase in the use of ICT in the workplace was registered compared to 2010 (26% to 37 %). However, even though low ICT intensity is dropping, 44% of workers still reported that they hardly use or do not use digital technologies at work. As the highest increases in the use of ICT between 2010 and 2015 are registered among those workers who were already using ICT intensely in 2010, the expert further explained that it seems possible to argue that the digital divide in workplaces is growing. In other words, the future trend seems to go towards a high share of workers who do not use ICT at all and a share of workers who use it very intensely, without these two groups showing any convergence.

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6.2 Learning points from the validation workshop

As part of the methodological approach of the study, a workshop took place at the

European Commission’s premises on the 7th of October 2016 with the aim of validating

and discussing the findings of the research, involving stakeholders at European and

national level and international experts from Eurofound, UNESCO and Cedefop.

Participants represented a mix of stakeholders, mostly employer and employee

organisations and associations of training providers at European level, in addition to

representatives from the research team (Ecorys and the Danish Technological Institute)

and the European Commission.

The workshop consisted of three main sessions: the presentation and discussion of the

main preliminary findings of the research; experts’ contributions and panel discussion

on the research, and a plenary discussion aimed at addressing some of the questions

underpinning the study. Overall the discussions provided the research team with

meaningful insights on how to better present and interpret the data collected and to

guide the formulation of the conclusions and, most importantly, the recommendations.

With regard to the scope of the study and its methodology, the main points made

during the workshop were the following:

First, the importance of this study in addressing a key research gap.

Second, the importance of a clear conceptual framework was highlighted,

including a clear definition and classification of digital skills and digital skills

gaps, which ensures consistency across the study and clarifies the way gaps are

calculated, in addition to possible bias and limitations of the study. It was

mentioned in particular that data on the density of digital skills gaps is useful to

provide a more precise and nuanced picture of the situation in European

workplaces, as many employees may be under-skilled but only to a limited

extent.

Third, the difficulty in delivering more detailed data on skills and skills gaps

based on survey methods, given cost and methodological constraints. In

addition, the relative lack of utility of delivering a very detailed picture in terms

of policy repercussions taking into consideration the speed with which the

subject of study changes. In this regard, the point was made was that the best

results in terms of improved employability and in closing skills gaps often stem

from more generic measures aimed at improving people’s capacity to acquire

new skills and learn and general skills levels (e.g. basic skills including numerical

skills).

With regards to the results presented by the research team, experts generally noticed a

convergence between the findings stemming from the work of their organisations –

especially Cedefop and Eurofound - and those of the study. This convergence relates

mostly to the following key issues:

First the increasing proliferation of ICT and digitisation in European jobs.

Second the increased segregation and digital divide within the labour force, as

some groups are more reliant on ICT than others, with a high share of workers

in certain occupations who do not need digital skills at all.

Finally, the role of digitisation in further blurring the distinction between different

types of occupations and in driving change within the labour market.

102

In addition to these overall trends, the discussion on findings was fundamental to enrich

the conclusions of the study with stakeholders’ perspectives on the reasons behind the

results observed in particular regarding digitisation at workplace level.

Employers’ attitudes and heterogeneity (which results in different needs) were

particularly discussed. Stakeholders had different views in terms of whether or not

employers tend to upskill their workforce and in terms of viability for the employer to

invest in training. Stakeholders pointed out that companies in certain sectors and/or

countries may be less aware of their employees’ need to upskill their digital skills, as

there is a general lack of focus on the specific issue. Similarly, it was also pointed out

that SMEs might have more difficulties in digitally upskilling their workforce and may

need specific support to do so. Overall, the conclusion was that diversity in company

sizes, economic sectors and countries should be kept in mind when making policy

recommendations.

This discussion led to useful points on possible action to be taken to increase digital

skills in the workforce. With regard to national policies, it was underlined that countries

seem to be quite responsive in developing digital strategies, in trying to address

digitisation within the training systems, update training programmes, and introduce ICT

as part of key competences. On the other hand, areas in which countries have not been

as efficient relate to investment in teachers’ skills development and training with regard

to their digital skills and the use of ICT in teaching, and providing incentives to at-risk

groups by, for example, supporting them in gaining access to ICT or covering the cost

for them of developing these skills. These areas were thus identified as possible areas

of future action.

The role of social partners was also emphasised. In light of the continuous change in

the labour market in relation to required skills, it is important that investment is made

to fund not only training providers and employers, but also to make funds available to

social partners regionally and nationally (both employee and employer organisations);

these actors should have better access to funding and procedures should be simplified.

In addition to better access to funding, stakeholders also felt that policy-makers should

see digital skills as part of a broader and comprehensive skills strategy, resulting from

effective social dialogue with employers and trade unions. This is necessary as, in

several countries, social dialogue in this respect is still lacking, contributing to lack of

alignment of skills strategies with labour market needs.

All the above points, as well as more general outcomes of the workshop, were taken

into account in the analysis and presentation of the research findings – especially in

terms of formulation of the recommendations – in this final report.

6.3 Recommendations

Based on the above learning points, in particular those emerged from the stakeholder

consultation in the validation workshop, the following recommendations can be

formulated:

Recommendation 1: Raise awareness on digital technologies and the need

for digital skills

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Awareness-raising campaigns, based on up-to-date research evidence, should be

implemented to raise awareness both on the importance of the use of digital

technologies to support and improve business performance, productivity and

internal organisation, and on the need for digital skills in relation to new digital

technologies. Employers should also be made aware of what digital skills gaps are,

how to recognise them, their possible underlying causes, the consequences of not

properly addressing them, and how to tackle them. Among others, it will be

important to raise awareness on the need for the development of improved digital

skills for workers in medium and low-skilled occupations.

Recommendation 2: Promote access to digital technologies

Mechanisms (loans, grants etc.) should be used to enhance and support access to

digital technologies, particularly for micro and small-sized companies, many of

which are not fully aware of the importance of investing in digital technologies, and

often do not have the financial capacity to do so. Access to digital technologies

would allow the proliferation of digitisation across the economy contributing to the

development of the demand of digital skills, also in the medium and low-skilled

occupations.

Recommendation 3: Expand the availability of digital skills through the

education and training system

In order to expand the current and future availability of digital skills in the economy,

actions should be undertaken within educational systems (including vocational

education and training). The education and training sector should be supported to

develop and adapt its offer to meet the changing needs of the digital economy,

programmes in all levels and sectors of education should be updated and digital

skills should be part of the core competences required at every level. There should

be greater investment in teachers’ and trainers’ skills for using ICT in teaching.

Teaching methodologies to use digital technologies should be increased significantly,

which can act as multipliers and provide a significant contribution to increasing the

digital skills availability.

Recommendation 4: Promote access to training

Access to training to address digital skills gaps in the existing workforce should be

supported through a variety of means. Information about existing training initiatives

and procedures to access them should be made available to employers through their

professional or sectoral organisations and associations, or through governmental

channels.

Recommendation 5: Build multi-stakeholder partnerships based also on

effective social dialogue to increase the availability of digital skills

Policymakers should support digital skills development within multi-stakeholder

partnerships (e.g. involving the European Commission, Member State ministries and

agencies, training providers, employers and social partners). Partnerships are

proven to generate a more inclusive and targeted approach to skills development

and training provision that is more responsive to labour market needs, in line with

vocational programmes and qualifications. A digital skills strategy should therefore

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be the result of a discussion based on effective social dialogue. In that context,

stakeholders including the European Commission need to work together on a holistic

approach to digital skills development. The recent launch of the Digital Skills and

Jobs Coalition35 is an important step in that direction, but special attention should be

paid to promoting the participation of micro and small-sized companies in order to

better understand their needs.

Recommendation 6: Provide access to funding for digital technologies and

digital skills development

Funding is critical to enhance the availability of digital skills in the current workforce.

Employers could benefit from access to funds (including EU funds) to support more

investment in digital technologies and the development of digital skills, especially for

initiatives that are cross-border and which share experiences in the generation and

use of digital skills. Better access to funding should be provided as well to social

partners regionally and nationally (both employee and employer organisations)

considering co-funding mechanisms to increase their role in the provision of training.

Within the context of the Better Regulation initiative, access to funding should be

simplified to support the dynamic development of the digital skills landscape.

Recommendation 7: Include digital skills in a wider skills strategy

Although it remains crucial to develop a range of specific digital skills which respond

to the needs of the digital economy, wider digital skills for the whole population

should be embedded in a broader and comprehensive skills strategy in which other

transversal skills relevant to employers such as soft skills and communication skills

are also included. Evidence shows that the most effective means of improving

employability and closing skills gaps are more generic measures aimed at improving

the capacity of workers to acquire new skills and learn in an evolving economy.

Opportunities for ensuring that digital competence is further embedded in an

integrated approach to skill development could be provided for example via the

European Commission’s proposal for the “Upskilling Pathways” or its current review

of the 2006 recommendation on key competences for lifelong learning

(2006/962/EC), which form part of the New Skills Agenda for Europe36.

Recommendation 8: Consider diversity and avoid the ‘one-size fits all’

approach

Employers require different types and levels of digital skills according to the sector

in which they operate, their size, their market, and the country in which they are

based. In designing a digital skills strategy or any other type of initiative to help

employers to access the required digital skills, diversity needs to be clearly

addressed through a tailored approach, for example through a sectoral/industry

approach, an occupational approach (e.g. focusing on specific categories of jobs) or

a territorial approach.

Recommendation 9: Reduce the digital divide

35 https://ec.europa.eu/digital-single-market/en/digital-skills-jobs-coalition#Article 36 http://ec.europa.eu/social/main.jsp?catId=1223

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Policymakers should take action to reduce the existing digital divide, focusing in

particular on the categories of individuals (e.g. older people, the lower-educated,

those employed in low-skilled jobs) who do not possess digital skills and are

consequently at risk of marginalisation not only in the labour market, but also in

day-to-day life, which can contribute to social and economic exclusion. The access

to digital literacy should be ensured for everyone, with a special focus on the most

deprived groups.

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ANNEX 1. SURVEY METHODOLOGY

This annex presents the detailed methodological approach adopted to carry out the

survey which findings are presented in this report.

The Digital Skills Survey has been carried out by Ecorys Survey Division with the

support of GN Research within the study “ICT for Work: Digital Skills in the Workplace”,

conducted by Ecorys UK and Danish Technological Institute on behalf of the European

Commission, DG CONNECT (project SMART 2014/0048).

The survey covered six EU member states and was carried out on a representative

sample of 7,800 workplaces in 12 economic sectors across the selected member states.

The objective of the survey was to collect evidence on:

the number and type of jobs, in the EU and selected member states, that require

digital skills by economic sector;

the level and type of digital skills required by such jobs;

the digital skills gaps related to such jobs and sectors;

the actions undertaken by employers to deal with digital skills gaps (e.g. providing

training, out-sourcing);

the main bottlenecks/barriers to improved availability of digital skills.

As a result, the methodology described in this Annex was designed and implemented to

produce the findings presented in this report. In particular the Annex presents the

methodology employed and activities carried out in order to:

identify the target population;

design the sampling strategy;

withdraw the sample and carry out data collection activities;

design and carry out the estimation procedure to infer sample results to the

population of workplaces in the selected countries and at EU28 level;

design the survey questionnaire.

A1.1 The target population

In the context of this survey the target population - identified as the set of units to be

surveyed - are the employers whose business falls within economic sectors requiring

digital skills, in particular in relation to specific types of occupation.

The economic sectors of interest and relevance for the purposes this study (identified

through the corresponding code within the NACE classification37) was identified using

the information collected by the Eurostat Survey on ‘ICT use in Enterprises’ data38.

Such survey, carried out yearly, covers a wide range of ICT-related topics39, and allows

classifying the economic sectors according to the intensity of computer use.

37 Detailed description and rationale of the NACE classification system, is available on Eurostat website http://ec.europa.eu/eurostat/statistics-explained/index.php/NACE_background 38 http://ec.europa.eu/eurostat/web/information-society/overview 39 The survey covers the following list of topics: ICT systems and their usage in enterprises; use of the Internet and other electronic networks by enterprises; e-commerce; e-business processes and organisational

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In particular, for the purposes of this survey, the NACE economic sectors were classified

according to three levels of intensity of computer use: low, medium, and high. Such

levels of intensity were calculated considering the percentage of employees using

computers40 within specific sectors, and subsequently classifying the sectors as follows:

Low intensity: sectors employing up to 33% of employees using computers with or

without access to the Internet;

Medium intensity: sectors employing from 34% to 66% of employees using

computers with or without access to the Internet; and

High intensity: sectors employing 67% and more of employees using computers

with or without access to the Internet.

The results are displayed in the following table at EU-28 level and by economic macro-

sectors41.

Table A1.1: Economic sectors covered by the ICT usage in enterprises survey, by share

of workers using computer and related digital intensity, EU-28.

NACE Rev.2 code

Economic sector Share of workers using computer

Digital intensity of sector

C Manufacturing 0.447847 Medium

D, E Electricity, gas, steam, air conditioning and water supply

0.550406 Medium

F Construction 0.373525 Medium

H Transportation and storage 0.492989 Medium

J Information and communication 0.944047 High

L Real estate activities 0.737925 High

M Professional, scientific and technical activities 0.893166 High

N Administrative and support service activities 0.409335 Medium

Source: elaboration Ecorys/DTI on Eurostat data http://ec.europa.eu/eurostat/web/information-

society/data/comprehensive-database

These results were considered as an initial indication, since the Eurostat Survey on ‘ICT

use in Enterprises’ does not cover all the NACE economic sectors42, and the final

selection of sectors to be surveyed benefitted from the indications of the Steering

Committee’s experts.

aspects; use of ICT by enterprises to exchange information and services with governments and public administrations (e-government); ICT competence in the enterprise and the need for ICT skills; barriers to the use of ICT, the Internet and other electronic networks, e-commerce and e-business processes; ICT expenditure and investment; ICT security and trust; use of ICT and its impact on the environment (Green ICT); access to and use of the Internet and other network technologies for connecting objects and devices (Internet of Things); access to and use of technologies providing the ability to connect to the Internet or other networks from anywhere at any time (ubiquitous connectivity). 40 As surveyed by question A2 of the 2014 ‘ICT use in Enterprises survey’ questionnaire available at the link https://circabc.europa.eu/sd/a/cd0f3e86-d720-407e-88c9-b2f10a64410b/Questionnaire%20ENT2014.pdf 41 Data available on Eurostat online database does not allow for a more detailed extraction, as only data at economic macro-sector are made available online. 42 The sectors covered by the Eurostat Survey on ICT use in Enterprises are: (Section C) “Manufacturing”; (Section D,E) “Electricity, gas and steam, water supply, sewerage and waste management”; (Section F) “Construction”; (Section G) “Wholesale and retail trade; repair of motor vehicles and motorcycles”; (Section H) “Transportation and storage”; (Section I) “Accommodation and food service activities”; (Section J) “Information and communication”; (Section L) “Real estate activities”; (Division 69 -74)“Professional, scientific and technical activities”; (Section N)"Administrative and support activities"; (Group 95.1) “Repair of computers”.

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The final list of sectors selected to be covered by the survey, which includes a total

number of 12 economic sectors with different levels of digital intensity, is reported in

table A1.2 here below.

Table A1.2: Economic sectors selected for the purposes of this study

NACE

Rev.2 code

Economic sector Inclusion?

A Agriculture, forestry and fishing yes

B Mining and quarrying no

C Manufacturing yes

D Electricity, gas, steam and air conditioning supply yes

E Water supply; sewerage, waste management and remediation

activities no

F Construction yes

G Wholesale and retail trade; repair of motor vehicles and motorcycles

yes

H Transportation and storage yes

I Accommodation and food service activities yes

J Information and communication yes

K Financial and insurance activities no

L Real estate activities no

M Professional, scientific and technical activities yes

N Administrative and support service activities yes

O Public administration and defence; compulsory social security no

P Education yes

Q Human health and social work activities yes

R Arts, entertainment and recreation no

S Other service activities no

T Activities of households as employers; undifferentiated goods- and

services-producing activities of households for own use no

U Activities of extraterritorial organisations and bodies no

Source: elaboration Ecorys/DTI on Eurostat data

A1.2 Sampling strategy

The sampling strategy and related quality assurance mechanisms that were employed

for this survey are detailed here below.

The sample size was fixed at 6 countries and 7,800 employers. The sample size was

defined in order to respect budget constraints as far as the minimum size required by

the selection procedure is guaranteed.

With regards to the sampling design, in the context of this survey, a two-stage,

stratified sampling design was adopted, in which countries are the primary sampling

units and workplaces are the secondary sampling units.

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In a two-stage sampling design, a sample of primary units is selected at the first stage

and a sample of secondary units within each primary unit is subsequently selected at

the second stage.

First-stage sampling

Based on indications of the DG CONNECT Steering Committee, the first-stage sample is

a non-probabilistic sample43. Six countries were selected within the EU-28 Member

States taking into account a number of aspects such as the relative population size of

the country and related enterprises population size, the level of digitisation in the

country, the need of ensuring a satisfactory geographical coverage and the overall and

specific objectives of this study.

In choosing the sample countries, the ‘Digital skills indicator’44 was used to take into

account the level of digitisation of each EU Member State. The indicator is calculated

according to the new methodology used for the measurement of digital skills in the

Digital Agenda Scoreboard 2014 within the pilot work carried out by DG CONNECT F4 in

relation to Action 62 of the Digital Agenda to propose "EU-wide indicators of digital

competence".

The sample of countries selected (table A1.3) included Sweden, Finland and Slovakia

(amongst the countries with a population of less than 10 million inhabitants) and

Germany, Portugal and the United Kingdom (amongst the countries with a

population of more than 10 million).

Table A1.3 - First stage sampling procedure and proposed non-probabilistic sample of countries

Countries Total Population

(2013)

Digital skills indicator

(2012)45 Sample

Austria 8'451'860 63.3

Denmark 5'602'628 78.1

Finland 5'426'674 73.6

Ireland 4'591'087 54.2

Luxembourg 537'039 78.2

Malta 421'364 51.8

Bulgaria 7'284'552 19.4

Croatia 4'262'140 42.5

Cyprus 865'878 37.8

Estonia 1'320'174 58.2

Latvia 2'023'825 51.7

Lithuania 2'971'905 47.7

43 Non-probability sampling represents a group of sampling techniques that help researchers to select units from a population that they are interested in studying. A core characteristic of non-probability sampling techniques is that samples are selected based on the subjective judgement of the researcher, rather than random selection (i.e., probabilistic methods), which is the cornerstone of probability sampling techniques. See also: http://www.statcan.gc.ca/edu/power-pouvoir/ch13/nonprob/5214898-eng.htm 44 The ‘Digital skills indicator’ calculated by country is published in the paper “Measuring Digital Skills across the EU: EU wide indicators of Digital Competence” on pag. 14, http://ec.europa.eu/digital-agenda/en/news/measuring-digital-skills-across-eu-eu-wide-indicators-digital-competence. The indicator can be downloaded from the following link: http://digital-agenda-data.eu. 45 Definition: Persons that have been using internet during last 3 months are attributed a score on four digital competence domains: information, communication, content-creation and problem-solving, depending the activities they have been able to do. The scores are basic, above basic and below basic. Individuals not using internet are classified without digital skills. Source dataset: European Commission, Digital Agenda Scoreboard. We have considered the share of people who have basic and above basic digital skills.

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Countries Total Population

(2013)

Digital skills indicator

(2012)45 Sample

Slovakia 5'410'836 57.3

Slovenia 2'058'821 49.8

Belgium 11'161'642 56.7

France 65'578'819 62.8

Germany 82'020'578 60.5

Italy 59'685'227 39.8

Netherlands 16'779'575 76.7

Spain 46'727'890 54.2

Sweden 9'555'893 74.5

United Kingdom 63'896'071 57.9

Czech Republic 10'516'125 48.5

Greece 11'062'508 35.1

Hungary 9'908'798 52.8

Poland 38'533'299 41.9

Portugal 10'487'289 44.9

Romania 20'020'074 15.3

Source: Elaboration Ecorys/DTI on Eurostat and DG CONNECT data

Second-stage sampling

The second-stage sample was a random stratified sample. In order to draw this

sample, a number of preliminary activities were undertaken.

a) Definition of stratification variables

The strata were identified by the ‘economic macro-sector’ and ‘enterprise size’

variables. In particular the enterprises falling within the selected NACE economic

sectors were grouped in six macro-categories. Such aggregation was carried out

considering the distribution of employers in the sectors at EU level.

Table A1.4 - Grouping of economic sectors for second stage sampling procedure

Nace code

Economic sectors Economic macro-sectors

A Agriculture (farms) A: Agriculture (farms)

C Manufacturing

C,D: Industry D

Electricity, gas, steam and air conditioning supply

F Construction F: Costruction

G Wholesale and retail trade; repair of motor vehicles and motorcycles

G,H,I: Other services H Transportation and storage

I Accommodation and food service activities

J Information and communication

J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support service activities

M Professional, scientific and technical activities

N Administrative and support service activities

P Education P,Q: Education And Human Health And Social Work Activities

Q Human Health And Social Work Activities

Source: Ecorys/DTI

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Within each of these six macro-categories, the enterprises were clustered according to

their size in four groups, following the definition by Eurostat46:

micro enterprises (less than 10 employees);

small enterprises (10 to 49 employees);

medium-sized enterprises (50 to 249 employees);

large enterprises (250 or more employees).

In summary, in the second-stage sampling 18-strata47 for each country was produced.

b) Definition of the allocation method

Regarding the sample size in each stratum, the sampling design allowed for the sample

allocation on planned domains of estimates. The domains of study are identified by a

partition of the population under investigation. The domains are usually an aggregation

of elementary strata and the planned domains are specific subpopulations with a pre-

defined level of reliability of estimations.

In the second-stage of the sampling a multi-domain allocation was used, with the

following planned domains

Country

Economic macro-sector

Enterprise size

Concatenation of sampled countries and economic macro-sector;

Concatenation of sampled countries and enterprise size.

The sample size in each stratum was allocated so to ensure the desired level of

reliability of estimates in each domain, which were chosen in advance fixing a

coefficient of variation (CV) value48.

When the coefficient of variation (CV) is set to 0.2, the allocation made planning the

domains of analysis allows estimates to be calculated at the national level by sector or

by enterprise size with a relative sampling error up to 0.2. Also, the sample allocation

on planned domains of estimates allows the various segments of the population with a

limited number of units to be represented appropriately. The planned multi-domain

allocation is useful to make the allocation of the units in the strata more effective. In

addition, it is particularly powerful when compared to the proportional allocation, once

considered the design effect (deff.49).

Within the specific context of this survey methodology, we therefore first set the level of

reliability of the estimates for each domain of analysis, and then we allocated the total

46 http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Enterprise_size 47 For the Agriculture sector and for the Education and Human Health sectors the enterprise size variable is not considered. The official data, downloaded by the Eurostat web site, on the structure of enterprises for these sectors does not have the disaggregation by size. 48 The coefficient of variation (CV) is the ratio of the standard deviation to the mean. The higher the coefficient of variation, the greater the level of dispersion around the mean. It is generally expressed as a percentage. Without units, it allows for comparison between distributions of values whose scales of measurement are not comparable. When we are presented with estimated values, the CV relates the standard deviation of the estimate to the value of this estimate. The lower the value of the coefficient of variation, the more precise the estimate. (http://www.insee.fr/en/methodes/default.asp?page=definitions/coefficient-de-variation.htm) 49 The design effect measures the effect of this sampling design compared to the simple random sampling: to this extent the respective variances of estimators will be compared.

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sample of employers to the strata, so the sample size for each domain allows to respect

the level of reliability set previously. In other terms, the sample allocation was

conducted taking into account simultaneously each country and total EU population of

employers, considering the enterprise size and sector.

In table A1.5 below the multi-domain allocation of the sample of 7,800 units for this

study is displayed. The level of reliability of estimates in each domain is fixed with a

coefficient of variation (CV) not higher than 0.2 for an estimate of a relative frequency

equal to 14%.

The starting point was the quantification of the target population (total number of

employers in the selected sectors in the country). Data to calculate the population were

downloaded from the Eurostat data warehouse using different sources. The main data

source was the Structural Business Statistics50; for P,Q sectors the data source was

the Business demography (Population of active enterprises in t)51; for the A

sector the source was the Farm structure in the Agriculture52. Sample allocation

phase took place at the beginning of 2015; Eurostat data used to allocate the sample

available at the beginning of 2015 referred to 2011.

Table A1.5 - Population size and sample size and the CV by country

Country n. of employers (population) n. of employers (sample) CV (p=0.14)

Germany 2,539,642 1,240 0.09

Portugal 1,259,922 1,333 0.09

Slovakia 446,843 1,325 0.10

Finland 299,880 1,281 0.09

Sweden 707,899 1,316 0.10

United Kingdom 1,926,857 1,305 0.10

Total 7,181,043 7,800 0.05

Source: Ecorys/DTI and Eurostat (2011)

In table A1.6 the detail of the sample allocation in the strata is displayed.

Table A1.6 - Population size and sample size by country, economic macro-sector and

size, year 2011

County Economic

macro-sector*

Size n. of employers

(population) n. of employers

(sample)

Germany

A n.a. 299,130 154

C,D

Less than 10 employees 130,223 82

10-49 employees 62,330 47

50-249 employees 17,561 51

250 or more employees 4,408 66

F

Less than 10 employees 199,840 124

10-49 employees 35,818 27

50-249 employees 3,055 19

250 or more employees 211 13

J,M,N Less than 10 employees 525,410 126

50 http://ec.europa.eu/eurostat/web/structural-business-statistics 51 http://ec.europa.eu/eurostat/statistics-explained/index.php/Business_demography_statistics 52 http://ec.europa.eu/eurostat/web/agriculture/farm-structure

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County Economic

macro-sector*

Size n. of employers

(population) n. of employers

(sample)

10-49 employees 57,802 27

50-249 employees 12,099 35

250 or more employees 2,851 43

G,H,I

Less than 10 employees 704,039 107

10-49 employees 150,848 65

50-249 employees 20,791 59

250 or more employees 2,727 41

P,Q n.a. 310,499 154

Total 2,539,642 1,240

Portugal

A n.a. 305,270 154

C,D

Less than 10 employees 61,109 112

10-49 employees 10,608 48

50-249 employees 2,237 64

250 or more employees 282 45

F

Less than 10 employees 98,820 135

10-49 employees 7,123 31

50-249 employees 689 43

250 or more employees 78 42

J,M,N

Less than 10 employees 261,138 149

10-49 employees 3,895 16

50-249 employees 726 21

250 or more employees 217 35

G,H,I

Less than 10 employees 348,158 141

10-49 employees 14,602 60

50-249 employees 1,610 46

250 or more employees 236 38

P,Q n.a. 143,124 154

Total 1,259,922 1,333

Slovakia

A n.a. 24,460 153

C,D

Less than 10 employees 66,695 136

10-49 employees 3,340 38

50-249 employees 1,050 68

250 or more employees 286 68

F

Less than 10 employees 89,436 148

10-49 employees 1,737 20

50-249 employees 239 34

250 or more employees 20 16

J,M,N

Less than 10 employees 80,503 147

10-49 employees 1,703 19

50-249 employees 322 21

250 or more employees 76 18

G,H,I

Less than 10 employees 149,460 140

10-49 employees 6,952 77

50-249 employees 629 41

250 or more employees 118 28

P,Q n.a. 19,817 153

Total 446,843 1,325

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County Economic

macro-sector*

Size n. of employers

(population) n. of employers

(sample)

Finland

A n.a. 63,870 154

C,D

Less than 10 employees 19,993 112

10-49 employees 3,292 37

50-249 employees 900 54

250 or more employees 229 47

F

Less than 10 employees 39,655 137

10-49 employees 2,562 27

50-249 employees 231 30

250 or more employees 37 26

J,M,N

Less than 10 employees 52,980 135

10-49 employees 3,263 33

50-249 employees 635 38

250 or more employees 139 29

G,H,I

Less than 10 employees 75,102 132

10-49 employees 5,688 57

50-249 employees 665 40

250 or more employees 195 40

P,Q n.a. 30,444 153

Total 299,880 1,281

Sweden

A n.a. 71,090 154

C,D

Less than 10 employees 50,932 123

10-49 employees 5,381 32

50-249 employees 1,525 48

250 or more employees 370 51

F

Less than 10 employees 82,185 139

10-49 employees 4,489 25

50-249 employees 405 28

250 or more employees 40 19

J,M,N

Less than 10 employees 244,453 145

10-49 employees 6,438 35

50-249 employees 1,186 37

250 or more employees 250 35

G,H,I

Less than 10 employees 172,649 133

10-49 employees 11,821 63

50-249 employees 1,682 53

250 or more employees 303 42

P,Q n.a. 52,700 154

Total 707,899 1,316

United Kingdom

A n.a. 186,800 154

C,D

Less than 10 employees 99,203 106

10-49 employees 23,215 34

50-249 employees 6,429 40

250 or more employees 1,438 39

F

Less than 10 employees 246,914 138

10-49 employees 16,012 19

50-249 employees 2,088 28

250 or more employees 322 29

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County Economic

macro-sector*

Size n. of employers

(population) n. of employers

(sample)

J,M,N

Less than 10 employees 610,103 134

10-49 employees 40,093 41

50-249 employees 7,969 49

250 or more employees 1,952 53

G,H,I

Less than 10 employees 481,018 120

10-49 employees 62,693 64

50-249 employees 8,467 52

250 or more employees 1,876 51

P,Q n.a. 130,265 154

Total 1,926,857 1,305

* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Other services: Wholesale and retail

trade, repair of motor vehicles and motorcycles; Transportation and storage; Accommodation and food service

activities; J,M,N: Information and communication; Professional, scientific and technical activities; Administrative

and support service activities; P,Q: Education; Human health and Social work activities

Source: Ecorys/DTI and Eurostat (2011)

c) Definition of the inclusion probability and sample withdrawal

In the second-stage sampling a same-inclusion probability of enterprises within the

strata was used. Therefore, the withdrawal of the sample units (enterprises) from the

population was systematic with a predetermined extraction step. Furthermore, the

representativeness of the second-stage sample was guaranteed by the random

selection procedure, which eliminates the introduction of any bias.

A1.3 The sampling frames

A sampling frame can be defined as “the listing of all units in the population from which

a sample is selected” (Bryman, 200853). For this survey, the sampling frames that have

been used to select the sample of enterprises according to the methodology described

so far by country, NACE economic sector and size, have been purchased from Bureau

van Dyke54 and reflect those used by Eurofound in 2013 to carry out the Third European

Company Survey55.

A1.4 Data collection

Data collection has been carried out by Ecorys Survey Division with the support of GN

Research. The data collection strategy chosen for this survey was a mixed CATI

(Computer Assisted Telephone Interviewing) and CAWI (Computer Assisted Web

Interviewing) approach. This strategy was planned with the aim of increasing any low

response rate. The response rate to the CAWI survey was eventually relatively low

53 Bryman, A. (2008) Social Research Methods, Oxford University Press. 54 http://www.bvdinfo.com/en-gb/home 55 More information about the Third European Company Survey is available here http://www.eurofound.europa.eu/surveys/ecs/2013/index.htm, whereas the information regarding the sampling frames existing in the EU28 and EEA Member States is available here http://www.eurofound.europa.eu/surveys/ecs/2013/documents/ecs2013docs/3rdECS%202013Sampling_2.pdf

116

(around 2% on average), in spite of the communication strategy accompanying the

administering of the survey and the reminders sent, and therefore CATI was extensively

employed in order to achieve the targeted number of interviews. This suggests that the

CAWI technique is not recommendable for employers’ surveys, although CAWI

interviews have the great advantage of being significantly less expensive than CATI

interviews. The table below presents – by country - the number of completed interviews

by type (CAWI/CATI), the number of initial contacts made and the resulting response

rates. On average the duration of phone interviews was 20.35 minutes.

A sampling frame of 39,000 contacts was purchased, and contact data was cleansed to

ensure they were complete in terms of name, telephone number and email address for

the person responsible for HR, which was the person who was asked to respond the

survey questionnaire. Where such data was not available, or was not correctly detailed

in the database, telephone calls were made to the organisations to introduce the survey

and collect the missing information.

Table A1.7 – Completed interviews, contacts made and response rates by type of

interview (CAWI/CATI) and by country (N and %)

Country

Total DE FI UK PT SE SK

CAWI interviews 74 128 76 169 81 349 877

CATI interviews 1,095 1,145 1,177 1,189 1,250 1,067 6,923

Total completed interviews 1,169 1,273 1,253 1,358 1,331 1,416 7,800

Total contacts 6,200 6,405 6,525 6,665 6,580 6,625 39,000

Response rates (A/B):

CAWI response rate (%) 1.2 2.0 1.2 2.5 1.2 5.3 2.2

CATI response rate (%) 17.7 17.9 18.0 17.8 19.0 16.1 17.8

Overall response rate (%) 18.9 19.9 19.2 20.4 20.2 21.4 20.0

Source: Ecorys/DTI

A1.5 Identification of parameters of interest and estimation phase

In this section the areas of interest of this study and the related parameters of the

population and subpopulations to be estimated are illustrated, together with the

procedure to identify the estimators. An estimator is a combination of multiplicative

coefficients, each of them applied to each sample unit to calculate how many units of

target population this represent. The calculation of the estimator is crucial in the overall

process of statistical inference and allow comparison of the survey sample to the target

population, thereby allowing bias to be eliminatedand optimisation of the estimation

efficiency.

The areas of interest are to:

Quantify the jobs in the EU and in the sampled Member States that require digital

skills, by economic sector;

117

Provide evidence on the type and level of digital skills required by different jobs and

in different economic sectors;

Identify the main digital skills gaps in different occupational categories and economic

sectors;

Investigate how employers deal with ICT/digital skills gaps (e.g. providing training,

out-sourcing);

Examine the main bottlenecks/barriers to improved availability of digital skills.

The related parameters of interest are therefore:

Types of digital skills available by occupational category;

Types of digital skills needed by occupational category;

Levels of digital skills available by occupational category;

Levels of digital skills needed by occupational category;

Actions undertaken by employers to deal with digital skills gaps;

Types of bottlenecks/barriers to improved availability of digital skills by occupational

categories.

The parameters of interest have been analysed both at the level of the countries

included in the sample and at EU level. This has been possible through the use of

calibration weights as illustrated below.

The estimation phase is of particular relevance in the inferential procedure. At this

stage it is in fact possible to correct the sample selection bias and the total non-

response bias56. The use of a sample rather than a census means that some procedure

is needed in order to estimate the characteristics of the target population from

responding units.

The techniques used for the construction of the survey estimator are based on the

predictive approach to regression estimator, and involve the construction of an

estimator based on the general category of model-based estimators. Model-based

approaches assume that responses are generated according to a statistical model.

These models typically attempt to use important auxiliary variables (information coming

from external sources and correlated with the parameter of interest) to improve fit and

usability. The main concern with model-based inferences is that population estimates

are totally dependent on model assumptions. Once the model is formulated, standard

statistical estimation procedures such as likelihood-based estimation are then used to

make inferences about the parameters being estimated.

Model-assisted means that a superpopulation model (Dorfman et al. 2002)57 is adopted

which allows the development of calibration estimators (Deville and Särndal 1992)58. In

addition to using auxiliary variables, reducing the sample variance, such a class of

estimators has among its properties the fact that the calibration incorporates estimates

of auxiliary variables (used as regressors) which correspond to known population totals

(Deville and Särndal 1992). In this way it is possible to calibrate the estimated

population with known population totals, broken down according to specific

characteristics.

56 http://www.nss.gov.au/nss/home.nsf/NSS/4354A8928428F834CA2571AB002479CE?opendocument 57 Dorfman A.H., Royall R.M., Valliant R., Finite Population Sampling and Inference: a Prediction Approach, New York, John Wiley & Sons, 2002. 58 Deville J. C., Särndal C. E., (1992), Calibration Estimators, “Survey Sampling”, Journal of the American

Statistical Association, vol. 87, pp. 367-382. http://www.stat.unipg.it/~giovanna/didattica/teo/Deville_sarndal.pdf

118

Calibration estimators are a category of adjustments that have been adapted from

probability sampling where the methods have been studied extensively and shown to

reduce both bias and variance in survey estimates (Deville and Särndal 1992). They

may be useful tools for non-probability samples (and in some circumstances the only

tool). However, they rarely are able to compensate fully for biases due to the

composition of the sample (Dever, Rafferty, and Valliant 2008; Tourangeau, Conrad,

and Couper 201359).The calibration estimator, applied as a multiplying coefficient of the

sample units, will produce estimates of the target population.

In particular, the estimate of total of variable Y is given by the following expression:

�̃� =∑ 𝑦𝑖𝑤𝑖

𝑖∈𝑠

where, for sample s, yi indicates the value of variable Y observed in sample unit ith and

wi the sampling weight related to sample unit ith. The procedure to calculate wi

sampling weights according to the calibration technique is articulated as follows: firstly

design weights di - defined as the inverse of the inclusion probability of sample unit ith

di =1/𝜋𝑖 are calculated; subsequently a correction factor i obtained linking direct

estimates to population totals. Sampling weight wi is therefore calculated multiplying

the design weight by the correction factor: wi = di i.

�̃� =∑ 𝑦𝑖𝑤𝑖

𝑖∈𝑠

=∑ 𝑦𝑖𝑑𝑖𝜔𝑖

𝑙∈𝑠

Correction factors i are calculated solving the following minimum optimisation problem,

in which a distance function between design weights di and sampling weights wi is

minimised; constraints are defined by the condition of equality between sample

estimates of auxiliary variables and known population totals:

{

𝑚𝑖𝑛 {∑ 𝑑𝑖𝑠𝑡 (𝑑𝑖, 𝑤𝑖)

𝑖∈𝑠

}

∑ 𝐱𝐢𝑤𝑖 = 𝐭

𝑖∈𝑠

where t denotes the vector of population totals and xi denotes the vector of auxiliary

variables observed in the ith sample unit. The solution is provided by the following

expression, which defines correction factor i for each sample unit (Deville e

Särndal,1992).

𝜔𝑖 = 1 + (𝐭 −∑𝐱𝐢𝑑𝑖𝑖∈𝑠

)

′

(∑𝐱𝐢𝐱𝐢′𝑑𝑖

𝑖∈𝑠

)

−1

𝐱𝐢

A comprehensive description of calibration weighting methods can be found in Särndal

(2007)60.

59 Dever J. A., Rafferty A.,Valliant R. Internet Surveys: Can Statistical Adjustments Eliminate Coverage Bias. Survey Research Methods 2008;2:47-62. Tourangeau R., Conrad F. G., Couper M. P.The Science of Web Surveys. New York: Oxford University Press; 2013 60 Särndal, C.E. (2007). The Calibration Approach in Survey Theory and Practice. Survey Methodology

33(2):99-119.

119

The known population totals that were used in the calibration procedure are, both at EU

level and at sampled country level:

Total number of enterprises (defined according to the selection criteria of the target

population);

Distribution of enterprises by sector;

Distribution of enterprises by size;

Number of workers by different occupational groups;

Number of workers by economic sector.

The input data for the calibration procedure, used as known population totals, came

from the following external sources:

Eurostat Structural Business Statistics;

Eurostat Business Demography Statistics;

Eurostat Farm Structure Statistics;

Eurostat Labour Force Survey.

Data used for the calibration procedure is the most recent Eurostat data available at the

time of the calculation of the weights (June 2016): statistics related to businesses refer

to 2013, while Labour Force Survey data refers to 2015.

With the aim of producing estimates both at EU and sampled country levels, two

different estimators (weights) have been calculated. The first allows the estimation of

the amount and the characteristics of enterprises falling in the selected NACE economic

sectors at EU level. The second allows the estimation of the enterprises in each country

included in the sample. These two estimators are needed as the survey has to reach a

two-fold objective: on the one hand the phenomenon of interest needs to be analysed

for each country included in the sample, and on the other hand a EU-level perspective,

where the countries included in the sample represent all the EU Member States. As a

consequence, in the first case, each country included in the sample will represent itself

and the related estimator will be employed to represent only the population within the

country whereas, while in the other case, each country included in the sample will help

to represent the EU countries as a whole and the related estimator will be used to

represent the overall population of European employers, with no breakdown by country.

A1.6 Sample profile

Table A1.8 below displays the discrepancies between the planned sample allocation as

described in previous section A1.2 and illustrated in Table A1.6 and the actual sample

allocation. Such difference, which happens very frequently when carrying out a survey,

has been taken into account and was corrected during the estimation phase in order to

produce reliable estimates.

120

Table A1.8 – Planned and actual sample allocation by country, economic macro-sector and size (N)

Co

un

try

Macro

secto

r

Planned sample Actual sample Discrepancy (N)

Size Size Size

2 t

o 9

10

to

49

50

- 2

49

25

0 +

To

tal

2 t

o 9

10

to

49

50

- 2

49

25

0 +

To

tal

2 t

o 9

10

to

49

50

- 2

49

25

0 +

To

tal

DE

A -- -- -- -- 154 98 51 46 3 198 -- -- -- -- 44

CD 82 47 51 66 246 77 43 63 79 262 -5 -4 12 13 16

F 124 27 19 13 183 99 28 13 11 151 -25 1 -6 -2 -32

GHI 107 65 59 41 272 74 68 45 26 213 -33 3 -14 -15 -59

JMN 126 27 35 43 231 103 24 35 35 197 -23 -3 0 -8 -34

PQ -- -- -- -- 154 74 31 24 19 148 -- -- -- -- -6

Total -- -- -- -- 1240 525 245 226 173 1169 -- -- -- -- -71

FI

A -- -- -- -- 154 160 36 5 0 201 -- -- -- -- 47

CD 112 37 54 47 250 151 45 44 15 255 39 8 -10 -32 5

F 137 27 30 26 220 196 41 7 3 247 59 14 -23 -23 27

GHI 132 57 40 40 269 148 42 23 6 219 16 -15 -17 -34 -50

JMN 135 33 38 29 235 138 28 30 14 210 3 -5 -8 -15 -25

PQ -- -- -- -- 153 92 18 25 6 141 -- -- -- -- -12

Total -- -- -- -- 1281 885 210 134 44 1273 -- -- -- -- -8

UK

A -- -- -- -- 154 80 16 26 8 130 -- -- -- -- -24

CD 106 34 40 39 219 127 38 42 23 230 21 4 2 -16 11

F 138 19 28 29 214 125 19 26 26 196 -13 0 -2 -3 -18

GHI 120 64 52 51 287 92 60 61 42 255 -28 -4 9 -9 -32

JMN 134 41 49 53 277 124 41 62 35 262 -10 0 13 -18 -15

PQ -- -- -- -- 154 67 33 51 29 180 -- -- -- -- 26

Total -- -- -- -- 1305 615 207 268 163 1253 -- -- -- -- -52

PT

A -- -- -- -- 154 150 54 26 4 234 -- -- -- -- 80

CD 112 48 64 45 269 84 49 70 53 256 -28 1 6 8 -13

F 135 31 43 42 251 93 38 46 7 184 -42 7 3 -35 -67

GHI 141 60 46 38 285 155 50 52 38 295 14 -10 6 0 10

JMN 149 16 21 35 221 158 25 15 30 228 9 9 -6 -5 7

PQ -- -- -- -- 154 95 28 25 13 161 -- -- -- -- 7

Total -- -- -- -- 1334 735 244 234 145 1358 -- -- -- -- 24

SE

A -- -- -- -- 154 107 38 7 1 153 -- -- -- -- -1

CD 123 32 48 51 254 154 20 33 56 263 31 -12 -15 5 9

F 139 25 28 19 211 151 40 24 9 224 12 15 -4 -10 13

GHI 133 63 53 42 291 125 84 52 20 281 -8 21 -1 -22 -10

JMN 145 35 37 35 252 139 39 31 24 233 -6 4 -6 -11 -19

PQ -- -- -- -- 154 77 20 30 50 177 -- -- -- -- 23

Total -- -- -- -- 1316 753 241 177 160 1331 -- -- -- -- 15

SK

A -- -- -- -- 153 82 22 33 0 137 -- -- -- -- -16

CD 136 38 68 68 310 125 51 91 77 344 -11 13 23 9 34

F 148 20 34 16 218 125 24 38 5 192 -23 4 4 -11 -26

GHI 140 77 41 28 286 126 122 53 30 331 -14 45 12 2 45

JMN 147 19 21 18 205 146 23 30 20 219 -1 4 9 2 14

PQ -- -- -- -- 153 73 49 47 24 193 -- -- -- -- 40

Total -- -- -- -- 1325 677 291 292 156 1416 -- -- -- -- 91

Total

A -- -- -- -- 923 677 217 143 16 1053 -- -- -- -- 130

CD 671 236 325 316 1548 718 246 343 303 1610 47 10 18 -13 62

F 821 149 182 145 1297 789 190 154 61 1194 -32 41 -28 -84 -103

121

Co

un

try

Macro

secto

r

Planned sample Actual sample Discrepancy (N)

Size Size Size

2 t

o 9

10

to

49

50

- 2

49

25

0 +

To

tal

2 t

o 9

10

to

49

50

- 2

49

25

0 +

To

tal

2 t

o 9

10

to

49

50

- 2

49

25

0 +

To

tal

GHI 773 386 291 240 1690 720 426 286 162 1594 -53 40 -5 -78 -96

JMN 836 171 201 213 1421 808 180 203 158 1349 -28 9 2 -55 -72

PQ -- -- -- -- 922 478 179 202 141 1000 -- -- -- -- 78

Total -- -- -- -- 7801 4190 1438 1331 841 7800 -- -- -- -- -1

* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Other services: Wholesale and retail

trade, repair of motor vehicles and motorcycles; Transportation and storage; Accommodation and food service

activities; J,M,N: Information and communication; Professional, scientific and technical activities;

Administrative and support service activities; P,Q: Education; Human health and Social work activities

Source: Ecorys/DTI

The figures below (Figure A1.1 and Figure A1.2) show the final sample allocation by

country and macro-sector and by country and company size, respectively.

Figure A1.1 – Actual sample allocation by country and economic macro-sector (%)

* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Other services: Wholesale and retail trade, repair of motor vehicles and motorcycles; Transportation and storage; Accommodation and food service activities; J,M,N: Information and communication; Professional, scientific and technical activities; Administrative and support service activities; P,Q: Education; Human health and Social work activities Source: European Digital skills survey

16.9

22.4

12.9

18.2

16.9

12.7

17.2

18.9

13.5

21.7

16.8

11.9

9.7

24.3

13.6

23.4

15.5

13.6

15.8

20.0 19.4

17.2 16.5

11.1 11.5

19.8

16.8

21.1

17.5

13.3

10.4

18.4

15.6

20.4 20.9

14.4

A

CD F

GH

I

JMN

PQ A

CD F

GH

I

JMN

PQ A

CD F

GH

I

JMN

PQ A

CD F

GH

I

JMN

PQ A

CD F

GH

I

JMN

PQ A

CD F

GH

I

JMN

PQ

DE PT SK FI SE UK

122

Figure A1.2 – Actual sample allocation by country and size (%)

Source: European Digital skills survey

A1.7 Data preparation and analysis

In order to prepare collected data for the subsequent calibration and analysis steps,

extensive quality and consistency checks using visual and logic checks and running

simple frequencies were carried out to ensure accuracy and completeness of data. Data

was subsequently cleansed, recoded and labelled as appropriate. A non-invasive

approach was chosen to deal with non-responses (people who responded “Don’t know”

to specific questions in the questionnaire). In other terms, it was chosen to not impute

non-responses (e.g. each missing value is replaced with an observed response from a

“similar” unit) to minimise bias, and as a consequence, non-responses were displayed

as blanks in the dataset and they were taken into account to compute denominators

when calculating incidences and proportions in the data analysis phase.

After data was prepared, the calibration procedure as described in section A1.5 was

carried out and two sample weights were calculated (as detailed in the last paragraph

of section A1.5), one to provide estimates at country level and the other one to provide

estimates at EU-28 level. These two weights were applied in the data analysis phase.

During such phase, the usual preliminary statistical analyses – frequencies, analysis of

variability, analysis of concentration, analysis of dispersion – were carried out on

weighted data. Descriptive analyses, including the calculation of ratios and incidences,

were then carried out in order to identify how the variables of interest vary according to

the occupations, economic sectors, enterprise size and country, and results were

presented in tables and graphs. Multivariate analyses, namely linear regression analysis

and logistic regression analysis, have been carried out to analyse the relationship

between specific variables of interest and the characteristics of the workplaces in terms

of sector, ownership, size, markets of reference and also to specific characteristics of

the workforce employed in the workplaces. Multivariate analyses, namely principal

component analysis, have been carried out also as an exploratory tool to assess

correlations between variables and to identify different pieces of elementary

37.0

29.7

20.6

12.7

55.8

25.6

13.7

4.9

47.9

28.7

17.2

6.1

72.3

16.3

9.3

2.0

58.6

21.0

12.8

7.6

46.1

27.1

19.4

7.3

2 to 9 10 to49

50 to249

250and

more

2 to 9 10 to49

50 to249

250and

more

2 to 9 10 to49

50 to249

250and

more

2 to 9 10 to49

50 to249

250and

more

2 to 9 10 to49

50 to249

250and

more

2 to 9 10 to49

50 to249

250and

more

DE PT SK FI SE UK

123

information to compose possible synthetic indicators. Synthetic indicators have then

been calculated employing the appropriate additive procedures defined ad hoc in

relation in particular to the areas in which ICT investments have been done and also in

the area of digital skills, for a better understanding of types and levels of digital skills

required in different jobs.

A1.8 The survey questionnaire

The survey questionnaire was designed operationalising the research questions

underpinning the survey the overall study, and therefore to collect evidence on the level

and type of digital skills that are required in different jobs and sectors as well as how

the need for such skills has changed over the past few years and is expected to change

in the future. Also, the survey questionnaire was designed to investigate the actions

undertaken by the employers to address digital skill gaps (e.g. training). The survey

questionnaire, designed bearing in mind the type of respondent (establishments) and

surveying technique of reported in the following of this section, is structured as follows:

Module 1 is a set of questions which aims at collecting background information on the

respondents while filtering out those not possessing the required characteristics to be

covered by the survey. In addition to the information on type of respondent (single

workplace or organisation with multiples workplaces), economic sector of activity,

number of employees in the workplace and in the organisation, ownership (public or

private) and main market of reference, a number of questions aimed at collecting

information on the employees and their occupational categories (ISCO 1-digit) are

asked.

Module 2 aims to collect information on ICT use and the digital skills available in the

workplace. More in particular a list of types of ICT and digital devices is presented, and

– for each of the occupational categories (ISCO 1-digit) identified in Module 1 – a list of

digital skills (ranging from basic to advanced and including also some specialised ICT

skills such the use and programming of CNC machines or robots) and the related level

of importance is presented, and the number of employees in each occupational category

for each digital skills (the total number and those proficient) is recorded. The digital

skills are investigated using as a proxy a list of tasks, assuming that those carrying out

a specific task possess as well the required skill to do so. The difference between the

number of employees in a specific occupational category with a specific digital skill and

the proportion of them who have a proficient level of that digital skill allows to identify

the digital skill gap.

Module 3 investigates the impact of digital skill gaps on the workplace performance, and

also the actions undertaken to deal with digital skill gaps. The reasons why workplaces

did not undertake any actions to reduce digital skill gaps.

Module 4 aims to collect information on type and level of digital skills and digital skill

gaps of up to three specific occupations amongst the most important for workplace’s

day-to-day operations. In relation to these occupation, a set of questions aim to identify

recent recruitment, expected impact of ICT on the way tasks are carried out and

estimated risk of disappearance due to ICT in relation to the selected occupations.

124

Module 5 concludes the questionnaire and surveys the reasons and the level of

importance of investment in ICT in the recent past and also the trends in the use of ICT

in the last five years.

The questionnaire was developed by core team members (Maurizio Curtarelli, Valentina

Gualtieri, Vicki Donlevy, Mike Blakemore, Martin Eggert Hansen, Hanne Shapiro) with

the support of the Scientific Committee (Ferrán Mañé, Graham Vickery, Irene Mandl),

taking into account the outcome of the literature and survey review carried out

previously and following the indications of the Steering Committee.

125

ICT for work: Digital skills in the workplace

Survey questionnaire

Module 0: Introduction

Ecorys and the Danish Technological Institute are conducting a study on behalf of the European Commission on Information and Communication Technology (ICT) skills needs for enterprises across the European Union. By ICT skills needs, we mean the skills in the area of ICT that your enterprise needs for its day-to-day activities.

The aim of the survey is to collect evidence of the level and types of ICT skills required in different jobs and sectors and what strategies are proposed to meet your skills needs. We would also like to find out how the need for these specific type of skills has changed over the past few years and how enterprises expect it to change in the future.

We guarantee that your responses will be presented completely anonymously and will never be analysed or displayed individually. The survey length is approximately 15-20 minutes. We would very much appreciate your participation.

Module 1: Background questions

[ASK ALL]

We would like to ask you some questions about the place in which you work. By this, we mean the location in

which you work all or most of the time which could be a factory, office, workshop, farm, warehouse, or other

place of work. For the purpose of this survey we will use the word “workplace” to describe this location.

Before we start with the main questionnaire, we would like to make sure that you are the correct

person at your workplace to consult for this survey.

Q1. We would like to survey the person who has the best overview of working tasks of the

employees in your workplace, for example the person responsible for human resources

issues or the managing director. Are you the correct person to survey in this case?

Yes……………………………………………….. 1

GO TO Q2

No………………………………………...……….. _

CONTINUE

Q1a. Can you please provide the name and contact of the person who has the best overview of working

tasks of the employees in your workplace?

Name ________________________________email ______________________________________

[ASK ALL]

Q2. Is your workplace one of many different workplaces belonging to the same organisation,

or is it the only workplace your organisation has?

One of many different workplaces belonging to the same organisation ……………………………………………………. 1 GO TO Q3

126

It is the only workplace your organisation has …………… 2 GO TO Q6

Don’t know…………………………………………………….. 3 GO TO Q6

Q3. Is your workplace the headquarters of your organisation, or is it a subsidiary site?

Headquarters.......................…………………….. 1 CONTINUE

Subsidiary site..................................................... 2 CONTINUE

Don’t know…………………………………….. 3 CONTINUE

Q4. In total and including yourself, approximately how many employees work in your

organisation?

Please include yourself if you are an employee and any other employees on the payroll of the organisation, any

employed proprietors or owners, employed family members and trainees, but do NOT include the self-

employed, freelance and outside contractors or agency staff.

Number of employees: _____________ 1 IF ANSWER IS 0 OR 1

EXIT, OTHERWISE GO

TO Q5

Don’t know……………...………………………….. 3 CONTINUE

Q5. Could you please give your best estimate using the following categories?

1……………………………………………….. 1 EXIT

2-9…………………………………………….. 2 CONTINUE

10-49………………………………………….. 3 CONTINUE

50-249………………………………………… 4 CONTINUE

250 or more………………………………….. 5 CONTINUE

Q6. In total and including yourself, approximately how many employees work in THIS workplace?

Please include yourself if you are an employee and any other employees on your payroll, any employed

proprietors or owners, but do NOT include the self-employed, freelance and outside contractors or agency staff.

Number of employees: _____________ 1 IF ANSWER IS 0 OR 1

EXIT, OTHERWISE GO

TO Q7

Don’t know……………...………………………….. 2 CONTINUE

Q7. Could you please give your best estimate using the following categories?

1……………………………………………….. 1 EXIT

2-9…………………………………………….. 2 CONTINUE

10-49………………………………………….. 3 CONTINUE

50-249………………………………………… 4 CONTINUE

250 or more………………………………….. 5 CONTINUE

Q8. Could you please indicate, for this workplace, the number or percentage of employees who…

127

% Absolute

number DK

…are female? __ __ 99

…have a university degree? __ __ 99

…are younger than 30 years of age? __ __ 99

…are older than 50 years of age? __ __ 99

Q9. A public sector organisation is either wholly owned by the public authorities or they own more than

50%. Is your workplace part of…

The private sector.................…………………….. 1 CONTINUE

The public sector................................................. 2 CONTINUE

Don’t know……………...………………………….. 3 CONTINUE

Q10. What is the main area of activity of your workplace? [ONLY ONE SELECTION IS POSSIBLE]

Agriculture, forestry and fishing………..………… 1 CONTINUE

Mining and Quarrying…………………………….. 2 EXIT

Manufacturing……………………………………… 3 CONTINUE

Electricity, gas, steam, air conditioning supply…. 4 CONTINUE

Water supply, sewerage, waste management and remediation activities…………………………

5 CONTINUE

Construction………………………………………... 6 CONTINUE

Wholesale and retail trade, repair of motor vehicles and motorcycles………………………….

7 CONTINUE

Transportation and Storage……………………… 8 CONTINUE

Accommodation and Food Service Activities…... 9 CONTINUE

Information and Communication…………………. 10 CONTINUE

Financial and Insurance Activities……………….. 11 EXIT

Real Estate Activities……………………………… 12 EXIT

Professional, Scientific and Technical Activities.. 13 CONTINUE

Administrative and Support Service Activities….. 14 CONTINUE

Public Administration and Defence, Compulsory Social Security……………………………………...

15 EXIT

Education…………………………………………… 16 CONTINUE

Human Health and Social Work Activities………. 17 CONTINUE

Arts, Entertainment and Recreation……………... 18 EXIT

Other Service Activities…………………………… 19

Please specify sector:…………………………….. CONTINUE

Activities of households as employers, undifferentiated goods and services, producing activities of households for own use, activities of extraterritorial organisations and bodies………...

20 EXIT

128

Q11. Which of the following markets is most important for the main activity of your

workplace? (Please select all that apply)

Local (i.e. the town or village in which your workplace is located)………………………..

1

CONTINUE

Regional (i.e. your county or region).…… 2 CONTINUE

National (i.e. your country as a whole) ….. 3 CONTINUE

International………………………………… 4 CONTINUE

Don’t know………………………………….. 5 CONTINUE

Q12. Does your workplace have any employees in any of the following job categories?

(Please select all that apply)

Occupation Examples YES NO

Plant and machine

operators and

assemblers

Examples include transport and mobile

machine drivers, plant and machine

operators, routine operatives (sorters,

assemblers), HGV, van, fork lift, train,

bus and taxi drivers.

1 0 CONTINUE

Building, craft and

related trade workers

Examples include electricians, motor

mechanics, machine repairers, metal

workers, blacksmiths, welders, TV

engineers, plumbers, carpenters,

printers, butchers, furniture makers.

1 0 CONTINUE

Skilled agricultural,

forestry and fishery

workers.

Examples include dairy producers,

landscape gardeners and horticultural

workers.

1 0 CONTINUE

Sales, customer or

personal service workers

Examples include sales assistants and retail cashiers, telesales, call centre agents, customer care occupations. Personal care workers such as those providing care to children, elderly and disabled people, ambulance workers. Personal service workers such as hairdressers, cooks, driving instructors, undertakers, housekeepers, waiters/waitresses and bar tenders. Protective service workers such as

security guards, and junior police, fire

and prison officers.

1 0 CONTINUE

Clerical support workers

Examples include secretaries,

receptionists, telephonists, book-

keepers, credit controllers/wage clerks,

assistants/clerks, communication

operators, market research interviewers,

pension and insurance clerks, office

assistants, database assistants.

1 0 CONTINUE

Technicians and

associate professionals

Examples include science, engineering and IT technicians, accounting technicians, manufacturing/ construction supervisors, draughtspersons, insurance underwriters, finance and investment analysts and advisers, buyers, estate

1 0 CONTINUE

129

agents, pilots, graphic designers, fitness instructors, chefs, junior nurses, therapists, community workers, careers advisors, health and safety officers, housing officers, fitness instructors, police inspectors and detectives, photographers, interior designers, sports players.

Professionals

Examples include professional

engineers, software and IT

professionals, accountants, chemists,

scientific researchers, solicitors and

lawyers, economists, architects,

actuaries, doctors, senior nurses,

midwifes, psychologists, teachers, social

workers, librarians, actors, artists,

authors, writers/journalists, musicians.

1 0 CONTINUE

Managers

Examples include chief executives,

senior officials, legislators, managing

directors, senior business managers,

senior production managers, senior

service managers.

1 0 CONTINUE

Elementary occupations

Examples include labourers, packers,

goods handling and storage staff,

cleaners, shelf fillers, kitchen/catering

assistants, postal workers, road

sweepers, traffic wardens.

1 0 CONTINUE

[ONLY OCCUPATIONS SELECTED IN Q12 APPEAR IN SUBSQUENT QUESTION]

Q13. Could you please indicate approximately how many employees your workplace has in

these job categories?

N DK

Plant and machine operators and assemblers __ 99 CONTINUE

Craft and related trade workers __ 99 CONTINUE

Skilled agricultural, forestry and fishery workers __ 99 CONTINUE

Service and sales workers __ 99 CONTINUE

Clerical support workers __ 99 CONTINUE

Technicians and associate professionals __ 99 CONTINUE

Professionals __ 99 CONTINUE

Managers __ 99 CONTINUE

Elementary occupations __ 99 CONTINUE

[IF 0=99 GO TO Q14]

[ONLY OCCUPATIONS SELECTED IN Q12 APPEAR IN SUBSQUENT QUESTION]

130

Q14. Could you please provide your best estimate of the approximate percentage of

employees in your workplace in these job categories? [TOTAL MUST BE 100%]

% DK

Plant and machine operators and assemblers __ 99 CONTINUE

Craft and related trade workers __ 99 CONTINUE

Skilled agricultural, forestry and fishery workers __ 99 CONTINUE

Service and sales workers __ 99 CONTINUE

Clerical support workers __ 99 CONTINUE

Technicians and associate professionals __ 99 CONTINUE

Professionals __ 99 CONTINUE

Managers __ 99 CONTINUE

Elementary occupations __ 99 CONTINUE

Module 2: ICT use and digital skills in the workplace

[ASK ALL]

We would now like to ask you a number of questions on Information Communication Technology (ICT) use, and existing digital skills in your workplace.

Q15. Please indicate if your workplace currently uses computers, CNC machines or tools, and other digital devices to carry out its main business activity.

By digital device an electronic device which uses discrete, numerable data and processes for all its operations should be meant. This includes personal computers connected or not to the Internet or to an intranet, nettops, portable computers (e.g. laptops, notebooks, netbooks, tablets), or other portable devices (e.g. Smartphones, Personal Digital Assistants (PDA), GPS navigator) and CNC (Computer Numerical Control) machines or tools used in manufacturing (machine tools that are operated by precisely programmed commands encoded on a storage medium)..

YES NO DK

a. Desktop computers.…………………… 1 2 99 CONTINUE

b. Portable computers………………… 1 2 99 CONTINUE

c. Other portable devices…………… 1 2 99 CONTINUE

d. Broadband technology to access the Internet…………………………………. 1 2 99 CONTINUE

e. Intranet platform……………………… 1 2 CONTINUE

f. CNC machines or tools……………… 1 2 99 CONTINUE

g. Programmable robots ……………… 1 2 99 CONTINUE

[IF Q15.a AND Q15.b AND Q15.c AND Q15.d AND Q15.e AND Q15.f AND Q15.g=2 EXIT;

PLEASE NOTE THAT IN THIS CASE RESPONDENT HAS TO BE RECONTACTED BY CATI]

Q16. Thinking about the job categories in your workplace, please indicate, using a scale of 1

to 5 (where 1 means not at all important, 3 means moderately important and 5 means

essential,) how important it is for employees in these categories to…:

131

[GRID BELOW APPEARS FOR EACH OF THE OCCUPATIONAL GROUPS SELECTED IN Q12]

No

t at

all

imp

ort

an

t

Mo

de

rate

ly

imp

ort

an

t

Essen

tial

DK

a. Use a word processor (e.g. Word)…………….. 1 2 3 4 5 99 CONTINUE

b. Create a spreadsheet (e.g. Excel)…………….. 1 2 3 4 5 99 CONTINUE

c. Search for, collect and process information using ICT (e.g. online/Internet)…………………

1 2 3 4 5 99 CONTINUE

d. Communicate through ICT using email .……… 1 2 3 4 5 99 CONTINUE

e. Communicate through ICT using social media, Skype/video calls…………………………………

1 2 3 4 5 99 CONTINUE

f. Use software for design, calculation or simulation……………………………………...……

1 2 3 4 5 99 CONTINUE

g. Undertake programming and software development……………………………………….

1 2 3 4 5 99 CONTINUE

h. Design and maintain ICT architecture for the workplace…………………………………………..

1 2 3 4 5 99 CONTINUE

i. Programme CNC machines ……………………. 1 2 3 4 5 99 CONTINUE

j. Programme robots………………………………..

k. Use CNC machines ……………………………. 1 2 3 4 5 99 CONTINUE

l. Use programmable robots……………………… 1 2 3 4 5 99 CONTINUE

Q17/Q23. Please provide your best estimate of the approximate number or share of employees carrying out such tasks and indicate how many of them are fully proficient in carrying out the tasks.

Please note that a proficient employee is someone who is able to do the job/carrying out the task to the required level.

[GRID BELOW APPEARS FOR EACH OF THE OCCUPATIONAL GROUPS SELECTED IN Q12;

ONLY IF Q16=2 TO 5 CORRESPONDING ITEM IS DISPLAYED]

TASKS NUMBER OF

EMPLOYEES

NUMBER OF

PROFICIENT

EMPLOYEES

N % DK N % DK

a. Use a word processor (e.g. Word)………….. __ __ 99 __ __ 99 CONTINUE

b. Create a spread-sheet (e.g. Excel)…………… __ __ 99 __ __ 99 CONTINUE

c. Search for, collect and process information using ICT (e.g. online/Internet)…………………. __ __ 99 __ __ 99 CONTINUE

d. Communicate through ICT using email ………… __ __ 99 __ __ 99 CONTINUE

e. Communicate through ICT using social media, Skype/video calls …………………………………. __ __ 99 __ __ 99 CONTINUE

f. Use software for design, calculation or simulation………………………………………….. __ __ 99 __ __ 99 CONTINUE

g. Undertake programming and software development………………………………………. __ __ 99 __ __ 99 CONTINUE

h. Design and maintain ICT architecture for the workplace………………………………………….. __ __ 99 __ __ 99 CONTINUE

i. Programme CNC machines ……………………. __ __ 99 __ __ 99 CONTINUE

j. Programme robots………………………………. __ __ 99 __ __ 99 CONTINUE

k. Use CNC machines ……………………………. __ __ 99 __ __ 99 CONTINUE

l. Use programmable robots……………………… __ __ 99 __ __ 99 CONTINUE

132

Module 3: ICT and digital skill gaps in the workplace

In the previous section, we have asked you about the use of ICT in your workplace and the level of proficiency of the employees in your workplace in carrying out tasks requiring ICT. We would now like to ask some questions about the steps your workplace has taken to increase the level of proficiency of your employees in the use of ICT.

[ASK IF Q23.a OR Q23.b OR Q23.c OR Q23.d OR Q23.e OR Q23.f OR Q23.g OR Q23.h OR Q23.i OR Q23.j OR Q23.k OR Q23.l ≠ 100% IN ANY OF THE JOB CATEGORIES]

Q26. Thinking about your workplace as a whole, does the fact that some of your employees are not fully proficient in carrying out the indicated tasks involving ICT use have an impact on your workplace performance?

Yes, a major impact…………………………….. 1 GO TO Q26a

Yes, a minor impact…………………………… 2 GO TO Q26a

No…………………………………………….. 3 GO TO Q27

Don’t know………………………………………… 4 GO TO Q27

Not applicable (100% proficient)…………………. 5 GO TO MODULE 4

Q26a. What type of impact does this have on your workplace performance?

Decrease in the number of customers……… 1 CONTINUE

Decrease in the number of contracts………… 2 CONTINUE

Loss of productivity…………………………….. 3 CONTINUE

Other negative impact…………………………. 4 CONTINUE

Don’t know………………………………………… 5 CONTINUE

Q27. Has your workplace taken any steps to improve the proficiency of employees to enable them to carry out the tasks involving ICT use?

Yes…………………..…………………………….. 1 CONTINUE

No, but have plans to…………………………… 2 GO TO MODULE 4

No…………………………………………….. 3 GO TO MODULE 4

[ASK IF Q27 = 1]

Q28. Which of the following steps is your workplace taking to overcome the fact that some of its employees are not fully proficient in carrying out tasks involving ICT use? (Please select all that apply)

YES NO

On the job training and development programmes………. 1 2 CONTINUE

External training and development programmes…………. 1 2 CONTINUE

Changing working practices (e.g. task sharing)…………… 1 2 CONTINUE

Reallocating tasks……………………………………………. 1 2 CONTINUE

Recruiting new staff with needed skills............................. 1 2 CONTINUE

Hiring temporary staff with needed skills (e.g. temporary agency workers)………………………………………………. 1 2 CONTINUE

Outsourcing of tasks involving ICT use………………… 1 2 CONTINUE

Secondment of employees from other workplaces within the same organisation [ONLY FOR MULTI- 1 2 CONTINUE

133

ESTABLISHMENT ORGANISATIONS]

Other…………………………………………………………… 1 2 CONTINUE

[ASK IF Q27 = 1]

Q29. Which – if any – of the following difficulties has your workplace encountered when taking steps to

overcome the fact that some employees are not fully proficient in carrying out tasks involving ICT use?

(Please select all that apply)

YES NO

Excessive cost of training and development programmes. 1 2 CONTINUE

Vacancies for jobs involving ICT stay open for a long time due to low number of applicants with the required skills… 1 2 CONTINUE

Vacancies for jobs involving ICT are not filled due to lack of applicants with the required skills………………………. 1 2 CONTINUE

Modifications to work organisation are not possible due to the limited number of employees in this workplace……… 1 2 CONTINUE

Excessive cost of hiring temporary staff with the required skills…………………………………………………………… 1 2 CONTINUE

Excessive cost of outsourcing of tasks involving ICT use…………………………………………………………… 1 2 CONTINUE

Module 4: ICT use, digital skills and digital skill gaps in specific occupations

We would now like to ask you some questions regarding up to three specific jobs of your choice.

Q18. Please select up to THREE specific jobs existing in your workplace which are amongst

the most important for your day-to-day operations. If possible, please select them from

different job categories.

[FOR EACH OCCUPATIONAL GROUP SELECTED IN Q12, ONLY THE CORRESPONDING

DROP-DOWN MENUS WITH THE LIST OF ISCO 4-DIGIT OCCUPATIONS APPEAR FOR

SELECTION]

[IF NO OCCUPATIONS SELECTED GO TO MODULE 5]

[NOTE: Q19 HAS TO BE ASKED FOR EACH OF THE OCCUPATION SELECTED IN Q18,

THEREFORE MAKE SURE THAT IT IS ASKED MAX 3 TIMES]

134

Q19. Thinking about these jobs in your workplace, please indicate using a scale of 1 to 5

(where 1 means not at all important, 3 means moderately important and 5 means essential)

how important for day-to-day activities it is for employees in these jobs to…:

[IF Q19.a AND Q19.b AND Q19.c AND Q19.d AND Q19.e AND Q19.f AND Q19.g AND Q10.h

AND Q19.i AND Q19.j AND Q19.k AND Q19.l = 1 GO TO Q21]

Q24. Thinking about the existing employees in your workplace employed as [TITLES OF

OCCUPATIONS SELECTED IN Q18 APPEAR], could you indicate if you think they are fully

proficient in carrying out the following tasks involving ICT use? Please note that a proficient

employee is someone who is able to do the job to the required level.

[GRID BELOW APPEARS FOR EACH OF THE OCCUPATIONS SELECTED IN Q18; ONLY IF

Q19=2 TO 5 CORRESPONDING ITEM IS DISPLAYED]

YES NO DK

a. Use a word processor (e.g. Word)………………………..…….…… 1 2 99 CONTINUE b. Create a spread-sheet (e.g. Excel)………………………………...…. 1 2 99 CONTINUE c. Search for, collect and process information using ICT (e.g.

online/Internet)……………………………………………………….…. 1 2 99 CONTINUE d. Communicate through ICT using email..…………………………..… 1 2 99 CONTINUE e. Communicate through ICT using social media, Skype/video calls.. 1 2 99 CONTINUE f. Use software for design, calculation or simulation………………..… 1 2 99 CONTINUE g. Undertake programming and software development....................... 1 2 99 CONTINUE h. Design and maintain ICT architecture for the workplace…………… 1 2 99 CONTINUE i. Programme CNC machines…………..………………………………. 1 2 99 CONTINUE j. Programme robots…………………………………………….……….. 1 2 99 CONTINUE k. Use CNC machines.......................................................................... 1 2 99 CONTINUE l. Use programmable robots……………………………………………... 1 2 99 CONTINUE

[GRID BELOW APPEARS FOR THE OCCUPATIONS SELECTED IN Q18]

[NOTE: Q20 IS DISPLAYED ONLY ONCE WITH THE TITLES OF THE OCCUPATIONS

SELECTED IN Q18]

No

t at

all

imp

ort

an

t

Mo

de

rate

ly

imp

ort

an

t

Essen

tial

DK

a. Use a word processor (e.g. Word)……… 1 2 3 4 5 99 CONTINUE

b. Create a spread-sheet (e.g. Excel)………… 1 2 3 4 5 99 CONTINUE

c. Search for, collect and process information using ICT (e.g. online/Internet)………………… 1 2 3 4 5 99 CONTINUE

d. Communicate through ICT using email….…. 1 2 3 4 5 99 CONTINUE

e. Communicate through ICT using social media, Skype/video calls……………………………….. 1 2 3 4 5 99 CONTINUE

f. Use software for design, calculation or simulation……………………………………... 1 2 3 4 5 99 CONTINUE

g. Undertake programming and software development…………………………………… 1 2 3 4 5 99 CONTINUE

h. Design and maintain ICT architecture for the workplace………………………………………… 1 2 3 4 5 99 CONTINUE

i. Programme CNC machines ……………. 1 2 3 4 5 99 CONTINUE

j. Programme robots 1 2 3 4 5 99 CONTINUE

k. Use CNC machines…………………………. 1 2 3 4 5 99 CONTINUE

l. Use programmable robots 1 2 3 4 5 99 CONTINUE

135

Q20. Thinking about these jobs in your workplace, please indicate if and to what extent the

use of ICT has changed the way job tasks are carried out. Please refer to the timespan of the

last 5 years.

No

ch

an

ge a

t al

l

Min

or

chan

ge (

e.g

. IC

T is

no

w s

up

po

rtin

g so

me

task

s)

Mo

de

rate

ch

ange

(e

.g.

som

e t

asks

are

no

w

carr

ied

ou

t in

a d

iffe

ren

t

way

as

con

seq

ue

nce

of

ICT

use

)

Maj

or

chan

ge (

e.g

. mo

st

or

all t

he

tas

ks a

re c

arri

ed

ou

t in

a d

iffe

ren

t w

ay a

s

a co

nse

qu

en

ce o

f IC

T u

se)

DK

a. Occupation 1 1 2 3 4 99 CONTINUE

b. Occupation 2 1 2 3 4 99 CONTINUE

c. Occupation 3 1 2 3 4 99 CONTINUE

[GRID BELOW APPEARS FOR EACH OF THE OCCUPATIONS IF Q20a _c=4]

Q21. Thinking about these jobs in your workplace, please indicate if you started recruiting

employees for this job(s) in the last 5 years.

YES NO DK

a. Occupation 1 1 2 99 CONTINUE

b. Occupation 2 1 2 99 CONTINUE

c. Occupation 3 1 2 99 CONTINUE

[GRID BELOW APPEARS FOR EACH OF THE OCCUPATIONS SELECTED IN Q18]

Q21a. Thinking about these jobs in your workplace, please indicate if and to what extent you

think the use of ICT will change the way job tasks are carried out. Please refer to the timespan

of the next 5 years.

No

ch

an

ge a

t al

l

Min

or

chan

ge (

e.g

. IC

T

will

su

pp

ort

so

me

tas

ks)

Mo

de

rate

ch

ange

(e

.g.

som

e t

asks

will

be

car

rie

d

ou

t in

a d

iffe

ren

t w

ay a

s

con

seq

ue

nce

of

ICT

use

)

Maj

or

chan

ge (

e.g

. mo

st

or

all t

he

tas

ks w

ill b

e

carr

ied

ou

t in

a d

iffe

ren

t

way

as

a co

nse

qu

en

ce o

f

ICT

use

)

DK

d. Occupation 1 1 2 3 4 99 CONTINUE

e. Occupation 2 1 2 3 4 99 CONTINUE

f. Occupation 3 1 2 3 4 99 CONTINUE

[IF Q21.a OR Q21.b OR Q21.c = 1 DO NOT DISPLAY RELATED OCCUPATION IN Q22]

[GRID BELOW APPEARS FOR EACH OF THE OCCUPATIONS SELECTED IN Q18]

136

Q22. Thinking about these jobs in your workplace, please indicate if you think they will

disappear in the next 5 years due to ICT.

YES NO DK

a. Occupation 1 1 2 99 CONTINUE

b. Occupation 2 1 2 99 CONTINUE

c. Occupation 3 1 2 99 CONTINUE

Module 5: Trends in ICT investment and use in the workplace

[ASK ALL]

In this final section of the survey, we would like to find out how investment in ICT and ICT use has changed over the past few years and how you expect it to change in the near future.

Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has your workplace invested in ICT for…? (Please select all that apply)

No

t a

t a

ll

Mo

de

rate

ly

Sig

nif

ican

tly

DK

Improving overall efficiency……………….……. 1 2 3 4 5 99 CONTINUE

Improving quality of existing products and services……. 1 2 3 4 5 99 CONTINUE

Launching new products and services………… 1 2 3 4 5 99 CONTINUE

Implementing new marketing methods………… 1 2 3 4 5 99 CONTINUE

Engaging customers, users, suppliers or other companies to improve or create products or services…………………………………………. 1 2 3 4 5 99 CONTINUE

Tracking and analysing data from business processes, customers, and transactions to improve or create products or services …… 1 2 3 4 5 99 CONTINUE

Making the production process leaner 1 2 3 4 5 99 CONTINUE

Delocalising the production of goods or services within the country…………………………………. 1 2 3 4 5 99 CONTINUE

Delocalising the production of goods or services abroad……………………………………………… 1 2 3 4 5 99 CONTINUE

Improving work organisation or working procedures……………………………………….. 1 2 3 4 5 99 CONTINUE

Making work easier and less stressful for employees 1 2 3 4 5 99 CONTINUE

137

Q31. Thinking about your workplace as a whole, would you say (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), that the use of ICT in your workplace…?

No

t at

all

Mo

dera

tely

Sig

nif

ican

tly

Has increased in the last 5 years……………………… 1 2 3 4 5 99 CONTINUE

Will increase in the next 5 years……………………… 1 2 3 4 5 99 CONTINUE

Q32. Do you think that ICT will cause a new job profile to emerge in your workplace in the

next 5 years?

Yes………………….………………….…………………. CONTINUE

No……………….………………….……………………… EXIT

Don’t know……………….………………….…………… EXIT

Q33. Can you briefly describe this new job profile?

_________________________________________________________________________________

_________________________________________________________

END OF QUESTIONNAIRE

Many thanks for taking part in our survey. For quality control purposes, we would like you to

provide some details regarding yourself:

Position__________________________________

Gender______

Contact details_________________________________________________________

138

ANNEX 2. COMPLEMENTARY STATISTICAL EVIDENCE

Chapter 2. Workplaces’ characteristics: describing the survey population

Table A2.1 – Workplaces by economic sector groupings, in sampled countries and EU28 (N and %)

Co

un

try

Sectors

A.

Ag

ric

ult

ure

CD

. M

an

ufa

ctu

rin

g a

nd

uti

liti

es

F.

Co

nstr

ucti

on

GH

I.

Co

mm

erce,

tra

nsp

ort,

acco

mm

od

ati

on

an

d f

oo

d

servic

e

JM

N.

In

form

ati

on

an

d

co

mm

un

ica

tio

n;

pro

fessio

nal,

scie

nti

fic a

nd

tech

nic

al

acti

vit

ies;

Ad

min

istr

ati

ve

servic

es

PQ

. E

du

cati

on

an

d h

um

an

healt

h

To

tal

N % N % N % N % N % N % N %

DE 208,824 14.4 153,329 10.6 160,233 11.1 491,697 33.9 274,656 19.0 160,068 11.0 1,448,807 100.0

FI 24,045 18.7 10,554 8.2 19,194 14.9 38,701 30.1 22,451 17.5 13,447 10.5 128,392 100.0

UK 166,439 9.4 123,532 7.0 219,316 12.3 483,033 27.2 658,255 37.1 125,523 7.1 1,776,098 100.0

PT 151,910 31.0 39,320 8.0 37,554 7.7 161,707 33.0 50,263 10.3 48,992 10.0 489,746 100.0

SE 28,315 10.0 24,218 8.5 41,404 14.6 88,671 31.3 80,940 28.5 20,059 7.1 283,607 100.0

SK 6,811 4.0 18,675 11.1 16,597 9.8 70,781 42.0 47,990 28.4 7,841 4.6 168,695 100.0

EU28 3,057,598 22.2 1,264,916 9.2 1,438,086 10.4 4,643,863 33.6 2,346,319 17.0 1,052,331 7.6 13,803,113 100.0

Q10. What is the main area of activity of your workplace? Note: totals from the Digital Skills Survey correspond mathematically to those from Eurostat Structural Business Statistics, Business Demography Statistics and Farm Structure Statistics (2013) with regards to the distribution of workplaces, as a result of the calculation of sample weights according to the calibration procedure as described in Annex 1. Number of valid responses: 7,800 N=13,803,113 (EU28) N=4,295,345 (Six countries) Source: Digital Skills Survey (weighted values), Eurostat (2013)

Table A2.2 – Workplaces by size (expressed in number of employees), in sampled countries and

EU28 (N and %)

Size

Country

DE FI UK PT SE SK EU 28

N % N % N % N % N % N % N %

2-9 893,849 61.7 104,419 81.3 1,421,896 80.1 436,327 89.1 234,752 82.8 142,801 84.7 11,256,481 81.6

10-49 420,724 29.0 16,888 13.2 294,050 16.6 45,633 9.3 34,245 12.1 20,522 12.2 2,082,572 15.1

50 - 249 112,997 7.8 6,228 4.9 49,156 2.8 7,306 1.5 13,084 4.6 4,618 2.7 396,316 2.9

250 + 21,238 1.5 858 0.7 10,996 0.6 478 0.1 1,526 0.5 753 0.4 67,744 0.5

Total 1,448,808 100.0 128,393 100.0 1,776,098 100.0 489,744 100.0 283,607 100 168,694 100.0 13,803,113 100.0

Q6: In total and including yourself, approximately how many employees work in THIS workplace? Q7: Could you please give your best estimate using the following categories? (2-9, 10-49, 50-249, 250+)

Note: totals from the Digital Skills Survey correspond mathematically to those from Eurostat Structural Business Statistics, Business Demography Statistics and Farm Structure Statistics (2013) with regards to the distribution of workplaces, as a result of the calculation of sample weights according to the calibration procedure as described in Annex 1. Number of valid responses: 7,800 N=13,803,113 (EU28) N=4,295,345 (Six countries) Source: Digital Skills Survey (weighted values), Eurostat (2013)

139

Figure A2.1 – Workplaces by type of organizational structure in sampled countries and EU28 (N

and %)

Q2: Is your workplace one of many different workplaces belonging to the same organisation, or is it the only

workplace your organisation has?

Number of valid responses: 7,800 N=13,803,113 (EU28)

N=4,295,345 (Six countries)

Source: Digital Skills Survey (weighted values)

Figure A2.2 – Workplaces belonging to a group by role within the group in sampled countries and

EU28 (N and %)

Q3: Is your workplace the headquarters of your organisation, or is it a subsidiary site?

Number of valid responses: 1,893 N=1,979,446 (EU28)

N=492,359 (Six countries)

Source: Digital Skills Survey (weighted values)

12.8

17.4

11.1

5.6

10.4

17.6

14.2

87.2

82.5

88.6

94.1

89.6

81.5

85.6

0.1

0.2

0.4

0.1

0.9

0.2

DE

FI

GB

PT

SE

SK

EU level

Several office branches Single office Don't know

64.2% 53.7% 58.4%

90.7% 74.7%

54.4% 69.5%

35.8% 42.4% 38.6%

5.8% 25.3%

44.8% 28.9%

4.0% 3.0% 3.5% 0.8% 1.6%

DE FI UK PT SE SK

Country EU28

Headquarters Subsidiary site Don't know

140

Figure A2.3 - Workplaces by sector (public/private) in sampled countries and EU28 (%)

Q9. A public sector organisation is either wholly owned by the public authorities or they own more than 50%.

Is your workplace part of…(the private sector/the public sector/don’t know)

Number of valid responses: 7,800 N=13,803,113 (EU28)

N=4,295,345 (Six countries)

Source: Digital Skills Survey (weighted values)

Figure A2.4 - Workplaces by main market of reference, in sampled countries and EU28 (%)

Q11. Which of the following markets is most important for the main activity of your workplace?

Number of valid responses: 7,800 N=13,803,113 (EU28)

N=4,295,345 (Six countries)

Source: Digital Skills Survey (weighted values)

90.7% 98.1% 96.6% 99.0%

79.2% 95.4% 94.7%

9.3% 1.7% 2.7% 1.0%

15.3%

4.4% 4.5% 0.2% 0.8%

5.4% 0.3% 0.8%

DE FI UK PT SE SK

Country EU28

The private sector? The public sector? Don't know

18.9%

36.3%

43.2%

36.6%

65.8%

26.9%

36.6%

36.4%

33.5%

21.3%

19.8%

18.5%

28.5%

29.0%

27.2%

17.3%

22.7%

26.5%

8.7%

30.3%

21.3%

17.5%

12.1%

12.7%

17.1%

7.1%

14.2%

13.0%

0.8%

0.1%

0.2%

DE

FI

UK

PT

SE

SK

Co

un

try

EU2

8

Local Regional National International Don't know

141

Table A2.3 - Workplaces by main market of reference by sector and size, EU28 (%)

Market of reference

Local Regional National International Don't know

Size

2-9 39.8 30.5 19.8 9.7 0.2

10-49 23.4 21.2 28.0 27.3 0.1

50-249 20.6 25.5 27.6 26.2 0.2

250+ 6.8 33.8 19.8 39.6 0.0

Sector

A. Agriculture 29.7 35.0 22.6 12.7 0.0

CD. Manufacturing

and utilities 25.6 24.5 25.2 24.6 0.1

F. Construction 39.7 40.3 15.6 4.2 0.2

GHI. Commerce,

transport,

accommodation and

food service 44.9 23.7 18.6 12.5 0.3

JMN. Information and

communication;

professional,

scientific and

technical activities;

Administrative

services 29.7 24.0 29.3 16.9 0.2

PQ. Education and

human health 44.5 35.9 14.5 5.1 0.0

Total 36.6 29.0 21.3 13.0 0.2

Q11. Which of the following markets is most important for the main activity of your workplace?

Number of valid responses: 7,800 N=13,803,113 (EU28)

N=4,295,345 (Six countries)

Source: European Digital Skills Survey (weighted values)

142

Figure A2.5 – Employees by specific characteristics in workplaces in the EU by sector and size (%

of total employees)

* A: Agriculture; C,D: Manufacturing and utilities; F: Costruction; G,H,I: Other services: Wholesale and retail trade,

repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities;

J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support

service activities ; P,Q: Education; Human health and social work activities

Q8: Could you please indicate, for this workplace, the number or percentage of employees who…(are female/have a

university degree, are younger than 30 years of age/are older than 50 years of age)

Number of valid responses: 6,917 (female rate); 6,334 (university degree rate); 6,114 (younger than 30 rate); 6,245 (older than 30 rate) N=12,269,195 (female rate); N=11,596,077 (university degree rate); N=11,365,645 (younger than 30 rate);

N=11,829,617 (older than 30 rate)

Source: European Digital Skills Survey (weighted values)

38.9

3.5

33.1

21.9

36.7

35.9

56.3

33.4

37.5

40.6

43.5

26.8

9.0

15.4

21.0

21.6

39.2

39.1

25.7

27.5

31.7

23.3

18.9

0.9

13.1

21.4

21.6

20.7

20.9

16.9

21.2

21.2

17.1

20.5

11.1

14.9

25.3

21.7

20.5

23.3

24.6

20.5

21.4

16.1

A

CD

F

GHI

JMN

PQ

2 - 9

10 - 49

50 - 249

250 +

Tota

lSe

cto

rSi

ze

Female employees Employees with a university degree

Employees younger than 30 years of age Employees older than 50 years of age

143

Figure A2.6 – Employees by specific characteristics in sampled countries (% of total employees)

Q8: Could you please indicate, for this workplace, the number or percentage of employees who…(are

female/have a university degree, are younger than 30 years of age/are older than 50 years of age)

Number of valid responses: 6,917 (female rate); 6,334 (university degree rate); 6,114 (younger than 30 rate); 6,245 (older than 30 rate) N=3,944,489 (female rate); N=3,745,575 (university degree rate); N=3,759,190 (younger than 30 rate);

N=3,799,479 (older than 30 rate)

Source: European Digital Skills Survey (weighted values)

45.7

44.0

29.2

38.0

46.6

35.1

44.1

18.2

19.7

38.2

21.7

28.2

34.2

18.0

11.6

13.2

15.5

20.2

34.8

18.9

11.2

14.3

20.1

20.3

DE

FI

PT

SE

SK

UK

Female employees Employees with a university degree

Employees younger than 30 years of age Employees older than 50 years of age

144

Table A2.4 – Employees by specific characteristics in sampled countries by sector and size (% of

total employees)

Sector Size

A.

Agriculture

CD

. M

anufa

ctu

ring a

nd

utilities

F.

Constr

uction

GH

I. C

om

merc

e,

transport

,

accom

modation a

nd food

serv

ice

JMN

. In

form

ation a

nd

com

munic

ation;

pro

fessio

nal, s

cie

ntific a

nd

technic

al activitie

s;

Adm

inis

trative s

erv

ices

PQ

. Education a

nd h

um

an

health

2 -

9

10 -

49

50 -

249

250 +

Country Total Female employees

DE 45.7 54.5 31.1 40.0 41.1 41.4 71.2 56.4 45.4 42.3 44.8

FI 44.0 14.5 36.0 13.1 26.4 56.6 63.0 30.2 40.7 48.5 48.1

PT 29.2 11.4 37.4 19.0 28.3 30.8 28.6 28.4 40.0 12.3 44.6

SE 38.0 13.9 22.8 13.2 34.3 25.1 58.5 27.5 32.0 42.9 41.5

SK 46.6 37.2 36.4 20.5 39.4 44.4 89.5 41.2 30.9 81.9 30.8

UK 35.1 31.6 19.7 36.4 31.4 38.8 43.0 40.2 35.2 31.0 33.5

Employees with a university degree

DE 44.1 58.7 30.6 43.8 37.4 51.5 61.5 54.9 39.9 44.6 42.7

FI 18.2 2.2 12.6 14.4 11.6 34.9 17.6 16.1 15.9 25.0 13.5

PT 19.7 27.8 17.5 15.9 13.4 30.8 17.7 21.1 28.8 9.4 15.6

SE 38.2 2.4 18.4 8.8 13.0 60.0 58.3 18.8 23.9 41.2 58.1

SK 21.7 13.5 12.1 14.3 24.1 50.8 26.0 31.3 16.2 25.1 11.6

UK 28.2 4.0 10.4 35.8 24.8 41.8 31.2 37.1 26.7 28.4 19.9

Employees younger than 30 years of age

DE 34.2 41.3 26.6 41.2 32.2 30.5 44.4 41.3 34.0 33.3 32.1

FI 18.0 2.3 11.0 23.4 25.8 27.2 9.6 13.2 23.2 25.1 11.1

PT 11.6 0.5 21.4 6.3 11.5 13.7 5.5 9.6 14.6 13.5 7.2

SE 13.2 1.4 8.9 11.3 17.4 9.7 14.6 9.4 18.0 16.1 8.0

SK 15.5 15.4 14.7 9.1 23.5 16.6 6.9 11.8 23.5 14.3 14.1

UK 20.2 6.3 14.0 35.4 24.6 19.8 16.6 26.4 21.7 9.2 22.8

Employees older than 50 years of age

DE 34.8 40.7 30.3 35.9 33.1 30.7 43.5 48.5 33.7 35.0 29.6

FI 18.9 29.8 11.7 28.5 21.1 22.6 16.1 29.2 19.7 18.5 13.7

PT 11.2 16.5 10.1 8.0 15.4 5.2 12.3 12.7 12.4 5.0 17.4

SE 14.3 25.1 11.6 22.4 21.2 17.2 7.4 28.4 20.3 11.9 3.4

SK 20.1 32.6 11.9 15.5 18.3 18.4 38.8 17.2 16.9 39.5 6.6

UK 20.3 45.4 12.2 39.2 28.3 22.1 10.5 36.7 20.1 9.7 13.4

Q8: Could you please indicate, for this workplace, the number or percentage of employees who…(are

female/have a university degree, are younger than 30 years of age/are older than 50 years of age)

Number of valid responses: 6,917 (female rate); 6,334 (university degree rate); 6,114 (younger than 30 rate); 6,245 (older than 30 rate) N=3,944,489 (female rate); N=3,745,575 (university degree rate); N=3,759,190 (younger than 30

rate); N=3,799,479 (older than 30 rate)

Source: European Digital Skills Survey (weighted values)

145

Table A2.5 – Workplaces by specific occupations in the European Union by sectors and number of

employees (%)

Workplaces with…

Managers

Pro

fessio

nals

Technic

ians

Cle

rical

Sale

s

Skille

d a

gric w

ork

ers

Buildin

g w

ork

ers

Pla

nt

machin

e

opera

tors

Ele

menta

ry

occupations

Secto

rs

A. Agriculture 32.3 17.7 0.6 5.7 0.4 54.8 9.5 0.1 0.1

CD. Manufacturing and utilities 45.8 17.2 24.7 45.3 24.9 0.3 42.9 17.7 13.8

F. Construction 41.3 12.7 14.9 39.2 8.7 0.1 63.8 6.6 7.0

GHI. Commerce, transport,

accommodation and food service 47.3 11.9 11.3 33.3 55.2 0.4 12.6 13.1 11.9

JMN. Information and communication;

Professional, scientific and technical

activities; Administrative services

36.8 45.9 27.0 34.2 18.6 0.0 4.3 2.9 6.0

PQ. Education and human health 38.5 57.0 22.0 44.4 28.4 0.2 0.8 1.5 15.1

Siz

e

2 - 9 39.0 21.2 11.5 23.5 25.7 13.8 16.7 5.0 5.2

10 - 49 51.2 28.3 22.4 57.1 32.7 6.8 22.2 17.3 20.1

50 - 249 39.8 42.2 38.9 66.5 39.9 0.7 21.9 21.0 26.8

250 + 14.6 46.0 35.5 48.7 21.0 0.0 18.2 17.0 39.6

Total 40.8 23.0 14.0 29.9 27.2 12.3 17.7 7.4 8.2

Q12: Does your workplace have any employees in any of the following job categories?

Number of valid responses: 7,800 N=13,803,113

Source: European Digital Skills Survey (weighted values)

Table A2.6 – Workplaces by specific occupations in sampled countries (%)

Workplaces with…

Managers

Pro

fessio

nals

Technic

ians

Cle

rical

Sale

s

Skille

d a

gric

work

ers

Buildin

g

work

ers

Pla

nt

machin

e

opera

tors

Ele

menta

ry

occupations

Co

un

try

DE 37.1 26.3 16.5 56.7 27.1 10.5 21.9 8.9 7.5

FI 31.6 13.3 24.5 14.7 30.4 16.7 18.3 6.8 5.9

UK 49.7 26.2 17.4 42.0 27.5 5.0 16.7 6.7 8.6

PT 30.0 35.0 6.5 26.0 20.7 7.4 7.6 5.7 8.1

SE 40.1 20.8 15.8 19.5 31.3 8.5 29.9 5.2 9.4

SK 30.4 35.1 16.8 36.6 35.0 2.2 11.0 12.8 13.4

Total 41.3 26.9 15.9 42.6 27.2 7.6 18.1 7.5 8.3

Q12: Does your workplace have any employees in any of the following job categories?

Number of valid responses: 7,800 N=4,295,345

Source: European Digital Skills Survey (weighted values)

146

Table A2.7 - Employees in specific occupations, EU28 (total number and % of employees, and

average number of workers in specific occupation per workplace)

Q13. Could you please indicate approximately how many employees your workplace has in these job

categories?

Q14. Could you please provide your best estimate of the approximate percentage of employees in your

workplace in these job categories?

Note: totals from the European Digital Skills Survey correspond mathematically to those from Eurostat

Labour Force Survey (2015) with regards to the distribution of employees, as a result of the calculation of

sample weights according to the calibration procedure as described in Annex 1.

Number of valid responses: 7,800 N=13,803,113

Source: European Digital Skills Survey (weighted values), Eurostat (2015)

Occupations N %

Average number

on total

workplaces

Average

number on

workplaces

with employees

in occupation

Managers 7,564,363 5.0 0.5 1.3

Professionals 28,452,941 18.9 2.1 9.0

Technicians 25,519,514 16.9 1.8 13.2

Clerical 16,921,402 11.2 1.2 4.1

Sales 25,923,570 17.2 1.9 6.9

Skilled agric workers 1,526,330 1.0 0.1 0.9

Building workers 16,887,614 11.2 1.2 6.9

Plant machine operators 12,206,673 8.1 0.9 12.0

Elementary occupations 15,561,133 10.3 1.1 13.7

Total number of employees 150,563,540 100.0

Total number of workplaces 13,803,113

147

Table A2.8 - Employees in specific occupations in workplaces by sector and size, EU28 (% of total

employees)

Occupations

Managers

Pro

fessio

nals

Technic

ians

Cle

rical

Sale

s

Skille

d a

gric

work

ers

Buildin

g

work

ers

Pla

nt

machin

e

opera

tors

Ele

menta

ry

occupations

Secto

r

A. Agriculture 31.2 10.0 1.0 1.5 0.7 46.2 9.1 0.2 0.2

CD. Manufacturing and utilities 2.8 7.9 37.2 8.3 5.8 0.0 20.0 12.7 6.0

F. Construction 7.4 9.2 8.6 12.8 4.6 0.0 47.8 6.0 4.8

GHI. Commerce, transport, accommodation and food service

6.7 6.9 6.7 12.7 30.9 0.0 9.0 13.0 15.4

JMN. Information and communication; Professional, scientific and technical activities; Administrative services

5.7 27.6 21.4 15.7 14.7 0.0 4.3 7.9 4.6

PQ. Education and human health 1.9 42.1 13.1 10.5 18.7 0.0 0.3 0.2 14.5

Siz

e

2 - 9 13.4 14.4 8.8 12.8 23.8 3.7 14.1 5.0 4.0

10 - 49 5.6 16.4 9.1 15.8 19.3 0.4 14.6 10.6 8.2

50 - 249 1.4 21.8 15.2 14.3 19.4 0.0 9.1 11.3 7.5

250 + 0.2 22.6 32.1 3.5 8.0 0.0 7.5 6.1 20.0

Total 5.0 18.9 16.9 11.2 17.2 1.0 11.2 8.1 10.3

Q13. Could you please indicate approximately how many employees your workplace has in these job categories? Q14. Could you please provide your best estimate of the approximate percentage of employees in your workplace in these job categories? Number of valid responses: 7,800 N=13,803,113 Source: European Digital Skills Survey (weighted values)

Table A2.9 - Employees in specific occupations in workplaces by country (%)

Occupations

Managers

Pro

fessio

nals

Technic

ians

Cle

rical

Sale

s

Skille

d a

gric

work

ers

Buildin

g

work

ers

Pla

nt

machin

e

opera

tors

Ele

menta

ry

occupations

Co

un

try

DE 3.7 15.5 23.0 13.8 14.5 1.1 13.0 6.7 8.8

FI 3.3 24.7 20.9 6.8 19.2 1.2 9.9 7.2 6.8

UK 10.1 24.4 13.1 11.4 19.7 0.8 6.5 4.8 9.2

PT 4.4 21.9 12.5 10.2 15.7 2.7 11.2 9.9 11.6

SE 5.9 26.3 18.1 7.1 20.8 0.9 8.9 7.0 5.0

SK 3.7 12.0 15.6 10.0 17.9 0.6 13.5 16.9 9.7

Total 6.2 20.2 18.8 12.3 17.3 1.1 10.6 6.7 9.0

Q13. Could you please indicate approximately how many employees your workplace has in these

job categories?

Q14. Could you please provide your best estimate of the approximate percentage of employees in

your workplace in these job categories?

Number of valid responses: 7,800 N=4,295,345

Source: European Digital Skills Survey (weighted values)

148

Chapter 3. Digital technologies for work

Table A3.1 – Workplaces by use of computers and other digital devices by sector and size of

workplace, EU28 (%)

Workplace currently uses…

Deskto

p c

om

pute

rs

Port

able

com

pute

rs

Oth

er

port

able

devic

es

Bro

adband t

echnolo

gy t

o

access t

he I

nte

rnet

Intr

anet

pla

tform

CN

C m

achin

es o

r to

ols

Pro

gra

mm

able

robots

Secto

r

A. Agriculture 91.8 84.8 46.4 99.3 9.6 12.4 12.3

CD. Manufacturing and utilities 94.7 71.8 63.8 91.6 26.4 21.4 8.4

F. Construction 87.3 73.7 71.6 91.1 15.1 5.1 1.7

GHI. Commerce, transport, accommodation and

food service 91.4 64.2 60.8 91.0 21.5 4.5 2.6

JMN. Information and communication; Professional,

scientific and technical activities; Administrative

services

96.4 85.2 79.9 93.9 37.4 4.3 2.4

PQ. Education and human health 97.9 81.5 73.9 94.0 35.8 4.2 3.5

Siz

e

2 - 9 91.4 74.6 60.9 94.3 16.9 5.1 3.5

10 - 49 98.4 76.6 71.8 90.4 41.5 18.7 12.2

50 - 249 97.0 86.5 81.4 91.7 70.3 21.6 13.5

250 + 100.

0 95.8 95.9 97.1 80.7 34.2 32.5

Total 92.7 75.3 63.3 93.6 22.5 7.8 5.2

Q15. Please indicate if your workplace currently uses computers, CNC machines or tools, and other digital

devices to carry out its main business activity. By digital device an electronic device which uses discrete,

numerable data and processes for all its operations should be meant.

Number of valid responses: 7,800 N=13,803,113

Source: European Digital Skills Survey (weighted values)

149

Figure A3.1 – Workplaces by use of computers and other digital devices by type of device, sampled

countries (%)

Q15. Please indicate if your workplace currently uses computers, CNC machines or tools, and other digital

devices to carry out its main business activity. By digital device an electronic device which uses discrete,

numerable data and processes for all its operations should be meant.

Number of valid responses: 7,800 N=13,803,113 (EU28)

N=4,295,345 (Six countries)

Source: European Digital Skills Survey (weighted values)

99

.7

62

.2

59

.0

76

.5

41

.3

14

.0

5.6

96

.9

87

.5

86

.8

97

.4

32

.7

10

.2

8.8

94

.6

56

.7

21

.6

93

.1

11

.0

6.2

4.0

77

.5

81

.0

79

.2

96

.4

18

.8

3.2

1.4

94

.8

78

.2

63

.8

86

.4

25

.5

5.8

1.0

95

.6

61

.8 6

7.6

91

.8

22

.4

2.4

1.7

92

.7

75

.3

63

.3

93

.6

22

.5

7.8

5.2

Desktopcomputers

Portablecomputers

Other portabledevices

Broadbandtechnology to

access theInternet

Intranetplatform

CNC machinesor tools

Programmablerobots

DE FI PT SE SK UK EU28

150

Table A3.2 - Workplaces by trends and importance in the use of ICT in the last and in next five

years by sector and size, EU28 (%, importance on a scale from 1 to 5)

Level of importance

1 N

ot

at

all

2

3 M

odera

tely

4

5 S

ignific

antly

DK

The use of ICT…has increased in the last 5 years

Secto

r

A. Agriculture 14.8 13.4 27.7 23.7 15.9 4.6

CD. Manufacturing and utilities 9.8 14.6 43.6 13.8 15.8 2.4

F. Construction 10.5 25.9 38 10.6 12.4 2.6

GHI. Commerce, transport, accommodation and food service

8.9 24.5 40.8 10.3 12.6 2.9

JMN. Information and communication; Professional, scientific and technical activities; Administrative services

6.4 12 42.3 15.6 21.6 2.2

PQ. Education and human health 5.7 16.9 42.9 13.1 17.8 3.5

Siz

e

2 - 9 10.7 19.9 38.5 14.9 14.1 1.9

10 - 49 6.0 13.0 38.1 13.6 19.8 9.5

50 - 249 5.7 9.9 30.1 18.3 33.2 2.8

250 + 5.3 10.4 42.1 8.7 22.6 10.9

Total 9.8 18.6 38.2 14.8 15.5 3.1

The use of ICT…will increase in the next 5 years

Secto

r

A. Agriculture 37.9 5.5 15.3 23.7 5.1 12.5

CD. Manufacturing and utilities 17.0 14.4 41.2 10.4 12.3 4.7

F. Construction 19.6 23.0 35.4 7.6 9.1 5.3

GHI. Commerce, transport, accommodation and food service

25.2 21.2 32.2 9.4 6.8 5.2

JMN. Information and communication; Professional, scientific and technical activities; Administrative services

20.1 11.3 36.4 12.9 15.4 3.9

PQ. Education and human health 14.9 16.1 40.0 11.1 12.9 5.1

Siz

e

2 - 9 27.3 15.8 29.7 13.5 7.9 5.7

10 - 49 16.1 12.8 35.6 11.3 12.5 11.7

50 - 249 10.1 10.7 39.6 14.3 21.2 4.2

250 + 5.5 11.9 35.1 14.1 22.4 10.9

Total 25.0 15.2 30.9 13.2 9.1 6.5

Q31. Thinking about your workplace as a whole, would you say (using a scale of 1 to 5, where 1 means not

at all important, 3 means moderately important and 5 means essential), that the use of ICT in your

workplace…?

Number of valid responses: 7,773 N=13,763,547

Source: European Digital Skills Survey (weighted values)

151

Table A3.3 Logistic regression: probability that the use of ICT in the workplace has increased in the last five years (Odds ratio)

N. obs 7800

Normalized weight

used yes

Model estimates

statistics Only intercept

Intercept and

covariates

AIC 4934.491 4596.947

SC 4941.425 4784.167

-2 Log L 4932.491 4542.947

Test Chi-square DF Pr > ChiSQ

Likelihood ratio 389.5439 26 <.0001

Score 356.1349 26 <.0001

Wald 318.6058 26 <.0001

Parameters B SE Chi-square

(Ward) Pr > ChiSQ Odds Ratio

Intercept 1.6574 0.0927 319.5038 <.0001

Workplace type

Only workplace (omitted)

Headquarters 1.5626 0.229 46.5494 <.0001 4.771

Subsidiary_site 1.3801 0.35 15.5462 <.0001 3.975

Workplace size

size < 10 (omitted)

size10_49 0.1515 0.1428 1.1251 0.2888 1.164

size50_249 0.2356 0.3139 0.5635 0.4529 1.266

size250 0.1374 0.7777 0.0312 0.8597 1.147

Sector

sectorA (omitted)

sectorCD 0.6517 0.1606 16.4776 <.0001 1.919

sectorF 0.823 0.1474 31.1709 <.0001 2.277

sectorGHI 0.7461 0.1185 39.6464 <.0001 2.109

sectorJMN 1.068 0.1501 50.6168 <.0001 2.91

sectorPQ 1.244 0.2249 30.6034 <.0001 3.469

Female rate

fem_rate<26 (omitted)

fem_rate_26_50 0.2942 0.118 6.2134 0.0127 1.342

fem_rate_51_75 0.394 0.1985 3.9393 0.0472 1.483

fem_rate_75 0.2938 0.1715 2.9334 0.0868 1.341

University rate

univ_rate<26 (omitted)

univ_rate_26_50 -0.0547 0.145 0.1423 0.706 0.947

univ_rate_51_75 0.0882 0.2431 0.1318 0.7166 1.092

univ_rate_75 0.3068 0.1632 3.5327 0.0602 1.359

Young rate

young_rate<26 (omitted)

young_rate_26_50 -0.2682 0.1317 4.1444 0.0418 0.765

young_rate_51_75 -0.3745 0.2843 1.7352 0.1877 0.688

young_rate_75 -0.6436 0.2446 6.9221 0.0085 0.525

Old rate

old_rate<26 (omitted)

old_rate_26_50 -0.4369 0.1143 14.6032 0.0001 0.646

old_rate_51_75 -0.4778 0.2116 5.0977 0.024 0.62

old_rate_75 -0.3391 0.1579 4.6125 0.0317 0.712

Ownership (q9) private (omitted)

public -0.8689 0.1693 26.3469 <.0001 0.419

Markets (q11)

Local_market (omitted)

Regional_market -0.6507 0.0904 51.8263 <.0001 0.522

National_market 0.7157 0.1478 23.4438 <.0001 2.046

International_market -0.0814 0.1517 0.2883 0.5913 0.922

* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; P,Q: Education and human health and social work activities. Q31. Thinking about your workplace as a whole, would you say (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), that the use of ICT in your workplace…? Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Source: Own elaboration on European Digital Skills Survey

152

Figure A3.2 - Workplaces by trends and importance in the use of ICT in the last and in next five

years, sampled countries (%, importance on a scale from 1 to 5)

Q31. Thinking about your workplace as a whole, would you say (using a scale of 1 to 5, where 1 means not at

all important, 3 means moderately important and 5 means essential), that the use of ICT in your workplace…?

Number of valid responses: 7,773 N=13,763,547 (EU 28)

N=4,280,432 (six countries)

Source: European Digital Skills Survey (weighted values)

Figure A3.3 - Workplaces by area and level of importance of investment in ICT in the past five

years, UE28 (%, importance on a scale from 1 to 5)

Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not

at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree

has your workplace invested in ICT for…? (Please select all that apply)

Number of valid responses: 7,773 N=13,763,547

Source: European Digital Skills Survey (weighted data)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

DE

FI

UK

PT

SE

SK

EU28

DE

FI

UK

PT

SE

SK

EU28

Use

of

ICT

in w

ork

pla

ce in

crea

sed

inla

st 5

yea

rsU

se o

f IC

T in

wo

rkp

lace

will

incr

ease

in n

ext

5 y

ears

1 Not at all 2 3 Moderately 4 5 Significantly DK

21.7

32.2

37.0

56.4

10.3

16.3

17.8

10.5

27.4

20.3

20.5

12.5

16.3

13.0

7.5

4.0

14.2

6.3

3.7

4.4

10.2

11.9

13.5

12.2

Overall efficiency

Marketing and sales

Internal organisation

Delocalisation

1 Not at all 2 3 Moderately 4 5 Significantly DK

153

Figure A3.4 - Workplaces by area and level of importance of investment in ICT in the past five years, by

sector, UE28 (%, importance on a scale from 1 to 5)

Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at all

important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has your

workplace invested in ICT for…? (Please select all that apply)

Note: the levels of importance of investment have been recoded in a dichotomic variable (No/Yes)

Number of valid responses: 7,773 N=13,763,547

Source: European Digital Skills Survey (weighted values)

32.3

42.2

42.0

47.7

21.9

26.1

34.5

51.6

25.3

37.8

42.3

65.2

20.9

32.7

37.9

62.6

11.0

18.7

28.0

49.8

13.0

30.8

34.1

63.2

57.7

47.6

40.2

42.3

68.2

62.2

54.3

36.5

61.4

47.1

42.6

19.2

69.4

55.8

50.7

25.6

80.4

69.2

59.9

37.6

75.0

54.9

51.9

22.1

10.0

10.2

17.8

10.1

9.9

11.7

11.1

11.9

13.3

15.1

15.0

15.6

9.7

11.5

11.4

11.8

8.7

12.1

12.1

12.7

11.9

14.2

14.0

14.6

Overall efficiency

Marketing and sales

Internal organisation

Delocalisation

Overall efficiency

Marketing and sales

Internal organisation

Delocalisation

Overall efficiency

Marketing and sales

Internal organisation

Delocalisation

Overall efficiency

Marketing and sales

Internal organisation

Delocalisation

Overall efficiency

Marketing and sales

Internal organisation

Delocalisation

Overall efficiency

Marketing and sales

Internal organisation

Delocalisation

A. A

gric

ult

ure

CD

. Man

ufa

ctu

rin

g an

d u

tilit

ies

F. C

on

stru

ctio

n

GH

I. C

om

mer

ce, t

ran

spo

rt,

acco

mm

od

atio

n a

nd

fo

od

serv

ice

JMN

. In

form

atio

n a

nd

com

mu

nic

atio

n;

Pro

fess

ion

al,

scie

nti

fic

and

tec

hn

ical

acti

viti

es;

Ad

min

istr

ativ

ese

rvic

esP

Q. E

du

cati

on

an

d h

um

anh

ealt

h

No Yes Don't know

154

Table A3.4 - Workplaces by area and level of importance of investment in ICT in the past five years,

by sector, UE28 (%, importance on a scale from 1 to 5)

Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at

all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has

your workplace invested in ICT for…? (Please select all that apply)

Number of valid responses: 7,773 N=13,763,547

Source: European Digital Skills Survey (weighted values)

Level of investment

Total

Sector Area

1 N

ot

at

all

2

3 M

odera

tely

4

5 S

ignific

antly

DK

A. Agriculture

Overall efficiency 32.3 0.9 21.9 22.4 12.5 10.0 100.0

Marketing and sales 42.2 8.3 9.7 24.9 4.7 10.2 100.0

Internal organisation 42.0 1.0 17.3 17.4 4.6 17.8 100.0

Delocalisation 47.7 8.0 17.0 9.6 7.7 10.1 100.0

CD. Manufacturing and utilities

Overall efficiency 21.9 12.0 30.1 12.1 14.0 9.9 100.0

Marketing and sales 26.1 18.4 26.7 9.2 7.9 11.7 100.0

Internal organisation 34.5 19.4 24.8 5.9 4.3 11.1 100.0

Delocalisation 51.6 13.7 14.5 3.0 5.2 11.9 100.0

F. Construction

Overall efficiency 25.3 13.3 26.0 12.1 10.0 13.3 100.0

Marketing and sales 37.8 17.0 19.6 5.5 5.0 15.1 100.0

Internal organisation 42.3 20.3 16.8 2.9 2.6 15.0 100.0

Delocalisation 65.2 8.3 7.7 1.4 1.8 15.6 100.0

GHI. Commerce, transport,

accommodation and food

service

Overall efficiency 20.9 15.4 27.3 14.8 11.9 9.7 100.0

Marketing and sales 32.7 19.9 21.2 9.6 5.2 11.5 100.0

Internal organisation 37.9 27.0 18.7 3.3 1.7 11.4 100.0

Delocalisation 62.6 11.0 9.6 1.9 3.1 11.8 100.0

JMN. Information and

communication; Professional,

scientific and technical

activities; Administrative

services

Overall efficiency 11.0 8.7 32.8 17.0 21.9 8.7 100.0

Marketing and sales 18.7 17.0 28.4 12.9 10.8 12.1 100.0

Internal organisation 28.0 17.9 28.1 7.6 6.3 12.1 100.0

Delocalisation 49.8 12.7 15.9 4.1 4.9 12.7 100.0

PQ. Education and human

health

Marketing and sales 30.8 18.9 23.0 7.4 5.7 14.2 100.0

Internal organisation 34.1 20.9 21.4 4.8 4.8 14.0 100.0

Delocalisation 63.2 9.7 8.6 2.0 1.9 14.6 100.0

155

Table A3.5 - Workplaces by area and level of importance of investment in ICT in the past five

years, by size, UE28 (%, importance on a scale from 1 to 5)

Level of investment

Total

Size Area

1 N

ot

at

all

2

3 M

odera

tely

4

5

Sig

nific

antly

DK

2-9

Overall efficiency 23.9 10.4 27.3 16.6 12.3 9.5 100.0

Marketing and sales 35.5 16.3 18.9 13.2 5.1 11.0 100.0

Internal organisation 39.7 17.5 19.0 7.8 3.1 12.9 100.0

Delocalisation 58.7 10.0 12.2 4.0 3.9 11.2 100.0

10-49

Overall efficiency 12.4 9.8 28.5 13.8 20.6 14.9 100.0

Marketing and sales 17.7 16.4 25.7 11.5 11.0 17.7 100.0

Internal organisation 25.8 19.6 26.2 5.5 5.1 17.8 100.0

Delocalisation 46.0 12.4 13.5 3.9 6.0 18.2 100.0

50-249

Overall efficiency 9.6 8.4 24.9 19.7 32.7 4.7 100.0

Marketing and sales 16.3 15.9 30.8 14.9 15.3 6.9 100.0

Internal organisation 20.2 19.0 32.6 9.9 11.3 7.0 100.0

Delocalisation 46.8 15.1 13.8 6.7 8.4 9.2 100.0

250 or more

Overall efficiency 11.4 11.9 21.5 23.2 21.4 10.6 100.0

Marketing and sales 17.2 26.7 26.4 8.7 10.3 10.8 100.0

Internal organisation 29.3 13.1 25.8 7.0 14.3 10.6 100.0

Delocalisation 48.4 12.0 18.8 0.9 9.3 10.6 100.0

Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1

means not at all important, 3 means moderately important and 5 means essential), in the last 5 years

to what degree has your workplace invested in ICT for…? (Please select all that apply)

Number of valid responses: 7,773 N=13,763,547

Source: European Digital Skills Survey (weighted values)

156

Figure A3.5 - Workplaces by area and level of importance of investment in ICT in the past five

years, by size, UE28 (%, importance on a scale from 1 to 5)

Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not

at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree

has your workplace invested in ICT for…? (Please select all that apply)

Number of valid responses: 7,773 N=4,280,432

Source: European Digital Skills Survey (weighted values)

31.7

30.8

49.3

62.9

5.3

29.7

22.4

63.9

9.1

19.6

19.5

42.9

7.6

17.9

26.3

39.7

52.5

60.4

71.5

74.2

17.4

27.4

38.7

68.4

64.7

65.2

46.5

32.8

75.8

48.2

57.4

16.1

71.8

60.1

60.1

36.7

91.3

80.2

59.9

54.4

35.7

26.7

15.1

12.0

71.2

56.8

45.5

17.8

3.7

4.0

4.2

4.3

18.9

22.1

20.2

19.9

19.1

20.3

20.5

20.4

1.1

1.9

13.8

5.9

11.8

12.8

13.5

13.8

11.4

15.9

15.8

13.8

Overall efficiency

Marketing and sales

Internal organisation

Delocalisation

Overall efficiency

Marketing and sales

Internal organisation

Delocalisation

Overall efficiency

Marketing and sales

Internal organisation

Delocalisation

Overall efficiency

Marketing and sales

Internal organisation

Delocalisation

Overall efficiency

Marketing and sales

Internal organisation

Delocalisation

Overall efficiency

Marketing and sales

Internal organisation

Delocalisation

DE

FIU

KP

TSE

SK

No Yes Don't know

157

Table A3.6 - Workplaces by area and level of importance of investment in ICT in

the past five years, by size, UE28 (%, importance on a scale from 1 to 5)

Level of importance

Total

Country Area

1 N

ot

at

all

2

3 M

odera

tely

4

5 S

ignific

antly

DK

DE

Overall efficiency 31.7 14.3 22.6 8.4 19.3 3.7 100.0

Marketing and sales 30.8 16.3 25.3 15.6 8.1 4.0 100.0

Internal organisation 49.3 23.5 15.1 4.6 3.3 4.2 100.0

Delocalisation 62.9 18.2 7.6 3.5 3.5 4.3 100.0

FI

Overall efficiency 5.3 14.0 29.1 24.7 8.0 18.9 100.0

Marketing and sales 29.7 22.9 19.0 5.4 1.0 22.1 100.0

Internal organisation 22.4 23.8 24.0 7.8 1.7 20.2 100.0

Delocalisation 63.9 11.3 3.7 0.8 0.3 19.9 100.0

UK

Overall efficiency 9.1 11.1 28.2 13.4 19.0 19.1 100.0

Marketing and sales 19.6 16.3 24.3 8.0 11.5 20.3 100.0

Internal organisation 19.5 21.3 19.8 6.5 12.5 20.5 100.0

Delocalisation 42.9 11.5 14.2 2.3 8.7 20.4 100.0

PT

Overall efficiency 7.6 18.8 38.9 10.0 23.5 1.1 100.0

Marketing and sales 17.9 13.9 25.9 32.0 8.3 1.9 100.0

Internal organisation 26.3 17.2 27.9 13.2 1.6 13.8 100.0

Delocalisation 39.7 9.7 29.8 2.0 12.9 5.9 100.0

SE

Overall efficiency 52.5 10.7 12.1 4.8 8.1 11.8 100.0

Marketing and sales 60.4 11.5 7.5 4.0 3.7 12.8 100.0

Internal organisation 71.5 6.6 6.3 1.5 0.7 13.5 100.0

Delocalisation 74.2 3.9 4.9 1.1 2.1 13.8 100.0

SK

Overall efficiency 17.4 2.8 37.0 18.6 12.7 11.4 100.0

Marketing and sales 27.4 19.1 28.7 5.3 3.7 15.9 100.0

Internal organisation 38.7 22.4 17.4 2.5 3.1 15.8 100.0

Delocalisation 68.4 7.2 6.5 2.2 1.9 13.8 100.0

Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5,

where 1 means not at all important, 3 means moderately important and 5 means essential),

in the last 5 years to what degree has your workplace invested in ICT for…? (Please select

all that apply)

Number of valid responses: 7,773 N=4,280,432

Source: European Digital Skills Survey (weighted values)

158

Table A3.7 – Logistic regression: probability that workplace has invested in ICT to improve

the overall efficiency in the last five years.

N. obs 7800

normalized

weight used yes

Model estimates

statistics Only intercept

Intercept and

covariates

AIC 7723.619 6665.496

SC 7730.476 6850.634

-2 Log L 7721.619 6611.496

Test Chi-square DF Pr > ChiSQ

Likelihood ratio 1110.1227 26 <.0001 Score 942.8074 26 <.0001

Wald 797.3302 26 <.0001

Parameters B SE

Chi-square

(Ward) Pr > ChiSQ

Odds

Ratio

Intercept -0.2999 0.0716 17.5274 <.0001

Workplace type

Only workplace (omitted)

Headquarters 2.0499 0.1828 125.7528 <.0001 7.767

Subsidiary_site 1.669 0.2207 57.1829 <.0001 5.307

Workplace size

size < 10 (omitted)

size10_49 0.1613 0.1073 2.2577 0.133 1.175

size50_249 0.2894 0.2509 1.3301 0.2488 1.336

size250 -0.4368 0.5622 0.6038 0.4371 0.646

Sector

sectorA (omitted)

sectorCD 0.8638 0.1257 47.2021 <.0001 2.372

sectorF 0.9185 0.1129 66.1909 <.0001 2.506

sectorGHI 1.2246 0.0941 169.4025 <.0001 3.403

sectorJMN 1.6327 0.1205 183.5962 <.0001 5.118

sectorPQ 1.6137 0.1664 94.0531 <.0001 5.021

Female rate

fem_rate<26 (omitted)

fem_rate_26_50 -0.2278 0.0879 6.7106 0.0096 0.796

fem_rate_51_75 -0.2907 0.1332 4.7648 0.029 0.748

fem_rate_75 -0.2486 0.1272 3.8196 0.0507 0.78

University rate

univ_rate<26 (omitted)

univ_rate_26_50 0.8394 0.1273 43.4871 <.0001 2.315

univ_rate_51_75 0.7212 0.196 13.5467 0.0002 2.057

univ_rate_75 0.8146 0.1349 36.454 <.0001 2.258

Young rate

young_rate<26 (omitted)

young_rate_26_50 -0.0919 0.1032 0.7926 0.3733 0.912

young_rate_51_75 -0.4327 0.2224 3.7849 0.0517 0.649

young_rate_75 -0.5586 0.1993 7.8573 0.0051 0.572

Old rate

old_rate<26 (omitted)

old_rate_26_50 -0.7091 0.0872 66.0667 <.0001 0.492

old_rate_51_75 -0.9867 0.1565 39.748 <.0001 0.373

old_rate_75 -0.2959 0.1282 5.3322 0.0209 0.744

Ownership (q9) private (omitted)

public 0.0434 0.1774 0.06 0.8065 1.044

Markets (q11)

Local_market (omitted)

Regional_market 0.4069 0.0713 32.5426 <.0001 1.502

National_market 1.115 0.0949 137.955 <.0001 3.05

International_market 1.427 0.14 103.9116 <.0001 4.166

* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; P,Q: Education and human health and social

work activities. Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has your workplace invested in ICT for…? (Please select all that apply) Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Source: Own elaboration on European Digital Skills Survey (weighted values)

159

Table A3.8 – Logistic regression: probability that workplace has invested in ICT in the area of

marketing and sales in the last five years.

N. obs 7800

normalized weight

used yes

Model estimates

statistics Only intercept

Intercept and

covariates

AIC 8999.634 7526.397

SC 9006.464 7710.814

-2 Log L 8997.634 7472.397

Test Chi-square DF Pr > ChiSQ

Likelihood ratio 1525.2364 26 <.0001

Score 1337.5086 26 <.0001

Wald 1105.9404 26 <.0001

Parameters B SE Chi-square

(Ward) Pr > ChiSQ

Odds Ratio

Intercept -0.9872 0.0717 189.7071 <.0001

Workplace type

Only workplace (omitted)

Headquarters 1.7704 0.1433 152.6849 <.0001 5.873

Subsidiary_site 0.0309 0.1453 0.0452 0.8316 1.031

Workplace size

size < 10 (omitted)

size10_49 0.4275 0.0959 19.8865 <.0001 1.533

size50_249 0.512 0.21 5.9418 0.0148 1.669

size250 0.1224 0.4863 0.0633 0.8013 1.13

Sector

sectorA (omitted)

sectorCD 0.7644 0.1209 39.9887 <.0001 2.148

sectorF 0.3964 0.1078 13.5166 0.0002 1.486

sectorGHI 0.7134 0.0875 66.5575 <.0001 2.041

sectorJMN 1.1164 0.1062 110.6006 <.0001 3.054

sectorPQ 0.4725 0.1407 11.2834 0.0008 1.604

Female rate

fem_rate<26 (omitted)

fem_rate_26_50 0.119 0.0805 2.1817 0.1397 1.126

fem_rate_51_75 0.0312 0.1216 0.0659 0.7974 1.032

fem_rate_75 -0.2879 0.1118 6.6347 0.01 0.75

University rate

univ_rate<26 (omitted)

univ_rate_26_50 0.748 0.1073 48.5778 <.0001 2.113

univ_rate_51_75 0.6439 0.1603 16.1349 <.0001 1.904

univ_rate_75 0.9501 0.1177 65.14 <.0001 2.586

Young rate

young_rate<26 (omitted)

young_rate_26_50 0.1767 0.0942 3.5176 0.0607 1.193

young_rate_51_75 0.0912 0.2083 0.1916 0.6616 1.095

young_rate_75 0.2006 0.1895 1.1199 0.2899 1.222

Old rate

old_rate<26 (omitted)

old_rate_26_50 -0.5453 0.0821 44.1723 <.0001 0.58

old_rate_51_75 -0.7016 0.1544 20.6408 <.0001 0.496

old_rate_75 -0.0871 0.116 0.564 0.4527 0.917

Ownership (q9) private (omitted)

public 0.0139 0.1556 0.0079 0.929 1.014

Markets (q11)

Local_market (omitted)

Regional_market 0.7165 0.0664 116.5994 <.0001 2.047

National_market 1.5327 0.0849 325.7634 <.0001 4.631

International_market 1.6344 0.1179 192.1355 <.0001 5.126

* A: Agriculture; CD: Manufacturing and utilities; F: Construction; GHI: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; JMN: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; PQ: Education and human health and social work activities. . Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has your workplace invested in ICT for…? (Please select all that apply) Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Source: Own elaboration on European Digital Skills Survey (weighted values)

160

Table A3.9 – Logistic regression: probability that workplace has invested in ICT to improve

internal organisation in the last five years.

N. obs 7800

Normalized weight

used yes

model estimates

statistics Only intercept

Intercept and

covariates AIC 9187.636 8147.086

SC 9194.463 8331.42

-2 Log L 9185.636 8093.086

Test Chi-square DF Pr > ChiSQ

Likelihood ratio 1092.5498 26 <.0001

Score 989.6332 26 <.0001

Wald 846.6196 26 <.0001

Parameters B SE Chi-square

(Ward) Pr > ChiSQ

Odds

Ratio

Intercept -0.9741 0.0718 183.9287 <.0001

Workplace type

Only workplace (omitted)

Headquarters 1.789 0.1286 193.4274 <.0001 5.984

Subsidiary_site -0.0279 0.1352 0.0426 0.8365 0.972

Workplace size

size < 10 (omitted)

size10_49 0.2091 0.0852 6.0226 0.0141 1.233

size50_249 0.6231 0.1906 10.6884 0.0011 1.865

size250 -0.3102 0.4098 0.5731 0.449 0.733

Sector

sectorA (omitted)

sectorCD 0.5571 0.1148 23.544 <.0001 1.746

sectorF 0.3677 0.1057 12.0998 0.0005 1.444

sectorGHI 0.7018 0.0859 66.7678 <.0001 2.017

sectorJMN 0.8664 0.1006 74.241 <.0001 2.378

sectorPQ 0.7203 0.1352 28.3641 <.0001 2.055

Female rate

fem_rate<26 (omitted)

fem_rate_26_50 -0.0642 0.075 0.7329 0.3919 0.938

fem_rate_51_75 -0.1531 0.1119 1.8709 0.1714 0.858

fem_rate_75 -0.2716 0.1044 6.7624 0.0093 0.762

University rate

univ_rate<26 (omitted)

univ_rate_26_50 0.5228 0.0956 29.8869 <.0001 1.687

univ_rate_51_75 0.2464 0.1412 3.0442 0.081 1.279

univ_rate_75 0.3648 0.1101 10.9863 0.0009 1.44

Young rate

young_rate<26 (omitted)

young_rate_26_50 0.084 0.0863 0.9474 0.3304 1.088

young_rate_51_75 0.2512 0.1895 1.7573 0.185 1.286

young_rate_75 -0.2779 0.1671 2.7643 0.0964 0.757

Old rate

old_rate<26 (omitted)

old_rate_26_50 -0.4078 0.0773 27.8561 <.0001 0.665

old_rate_51_75 -0.4205 0.1448 8.4361 0.0037 0.657

old_rate_75 -0.0623 0.1071 0.3385 0.5607 0.94

Ownership (q9) private (omitted)

public -0.3653 0.1405 6.7609 0.0093 0.694

Markets (q11)

Local_market (omitted)

Regional_market 0.725 0.0646 125.7638 <.0001 2.065

National_market 1.2528 0.0767 266.608 <.0001 3.5

International_market 1.1887 0.1041 130.3802 <.0001 3.283

* A: Agriculture; CD: Manufacturing and utilities; F: Construction; GHI: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; JMN: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; PQ: Education and human health and social work activities. Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has your workplace invested in ICT for…? (Please select all that apply) Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Source: Own elaboration on European Digital Skills Survey (weighted values)

161

Table A3.10 – Logistic regression: probability that workplace has invested in ICT to support

delocalization strategy in the last five years.

N. obs 7800

normalized weight

used

yes

Model estimates

statistics Only intercept

Intercept and

covariates

AIC 8907.536 7539.692

SC 8914.359 7723.923

-2 Log L 8905.536 7485.692

Test Chi-square DF Pr > ChiSQ

Likelihood ratio 1419.8438 26 <.0001

Score 1341.3262 26 <.0001

Wald 1112.429 26 <.0001

Parameters B SE Chi-square

(Ward) Pr > ChiSQ

Odds

Ratio

Intercept -1.2689 0.0759 279.2057 <.0001

Workplace type

Only workplace (omitted)

Headquarters 1.1953 0.0963 153.9627 <.0001 3.304

Subsidiary_site -0.1502 0.1417 1.1237 0.2891 0.86

Workplace size

size < 10 (omitted)

size10_49 0.2791 0.085 10.7838 0.001 1.322

size50_249 0.3013 0.17 3.1436 0.0762 1.352

size250 0.0145 0.3952 0.0013 0.9708 1.015

Sector

sectorA (omitted)

sectorCD -0.313 0.1154 7.3535 0.0067 0.731

sectorF -0.8649 0.1178 53.8703 <.0001 0.421

sectorGHI -0.5154 0.0875 34.7109 <.0001 0.597

sectorJMN -0.2647 0.0989 7.1609 0.0075 0.767

sectorPQ -0.7809 0.1467 28.3441 <.0001 0.458

Female rate

fem_rate<26 (omitted)

fem_rate_26_50 -0.2336 0.0804 8.4331 0.0037 0.792

fem_rate_51_75 -0.2151 0.1188 3.2819 0.07 0.806

fem_rate_75 -0.6144 0.1157 28.2188 <.0001 0.541

University rate

univ_rate<26 (omitted)

univ_rate_26_50 0.4964 0.0938 28.0073 <.0001 1.643

univ_rate_51_75 0.3564 0.1405 6.4302 0.0112 1.428

univ_rate_75 0.6856 0.1002 46.8286 <.0001 1.985

Young rate

young_rate<26 (omitted)

young_rate_26_50 0.113 0.0904 1.5619 0.2114 1.12

young_rate_51_75 0.0959 0.1839 0.2723 0.6018 1.101

young_rate_75 -0.3156 0.1799 3.0759 0.0795 0.729

Old rate

old_rate<26 (omitted)

old_rate_26_50 -0.3009 0.0853 12.4284 0.0004 0.74

old_rate_51_75 -0.7266 0.1757 17.1098 <.0001 0.484

old_rate_75 -0.0703 0.1087 0.4181 0.5179 0.932

Ownership (q9) private (omitted)

public -0.0226 0.1502 0.0226 0.8806 0.978

Markets (q11)

Local_market (omitted)

Regional_market 0.9167 0.0755 147.4195 <.0001 2.501

National_market 1.5649 0.0794 388.0824 <.0001 4.782

International_market 1.7954 0.0974 339.5397 <.0001 6.022

* A: Agriculture; CD: Manufacturing and utilities; F: Construction; GHI: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; JMN: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; PQ: Education and human health and social work activities.

Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has your workplace invested in ICT for…? (Please select all that apply)

Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey.

Source: Own elaboration on European Digital Skills Survey (weighted values)

162

Chapter 4. Digital skills availability in European workplaces

Table A4.1 – Workplaces by type and level of importance of digital skills of employees in specific

occupations, EU28 (% of total employers)

Occupation Digital skills

Level of importance

1 N

ot

at

all im

port

ant

2

3 M

odera

tely

im

port

ant

4

5 E

ssential

DK

Managers

Use a word processor 5.8 4.2 21.1 18.4 50.2 0.2

Create a spreadsheet 3.8 4.0 21.2 17.5 53.3 0.2

Use of Internet 2.3 2.5 10.0 14.8 70.2 0.2

Email 1.8 1.7 4.2 10.0 82.1 0.1

Social media, video calls 41.9 11.2 22.6 6.9 17.3 0.2

Use software for design, calculation and simulation

48.2 10.5 9.3 9.7 21.7 0.5

Programming and software develop.

81.4 4.9 4.5 2.5 5.6 1.2

Design and maintain ICT 67.7 4.1 11.3 6.7 9.8 0.5

Programme and use CNC machines

96.1 0.4 0.7 0.3 1.0 1.5

Programme and use robots 97.4 0.3 0.2 0.2 0.5 1.5

Professionals

Use a word processor 6.9 3.7 17.6 13.2 58.3 0.3

Create a spreadsheet 7.2 3.9 18.9 13.4 56.4 0.2

Use of Internet 4.4 2.1 8.2 11.7 73.3 0.2

Email 4.4 1.7 4.2 10.0 79.5 0.2

Social media, video calls 37.2 17.0 17.4 7.3 20.7 0.3

Use software for design, calculation and simulation

36.6 4.0 7.2 16.5 35.0 0.6

Programming and software develop.

68.1 3.3 5.6 4.8 17.2 1.0

Design and maintain ICT 60.4 3.6 7.0 13.4 14.6 1.0

Programme and use CNC machines

92.7 1.3 2.1 1.0 1.4 1.5

Programme and use robots 93.8 1.1 1.7 0.8 1.1 1.5

Technicians

Use a word processor 13.9 9.1 16.9 15.6 44.0 0.4

Create a spreadsheet 12.4 7.2 18.6 16.7 44.7 0.4

Use of Internet 8.1 6.1 13.0 13.5 58.7 0.6

Email 8.8 3.6 9.7 11.7 65.8 0.4

Social media, video calls 39.4 13.2 16.7 9.0 21.3 0.5

Use software for design, calculation and simulation

31.2 7.6 12.0 13.1 34.8 1.4

Programming and software develop.

64.4 7.8 5.9 5.8 14.4 1.6

Design and maintain ICT 55.3 8.2 12.2 9.4 12.9 2.0

Programme and use CNC machines

88.1 2.2 2.6 0.6 3.7 2.8

Programme and use robots 91.8 1.7 1.3 0.6 2.0 2.7

Clerical support workers

Use a word processor 4.4 4.6 12.8 17.6 60.4 0.2

Create a spreadsheet 3.1 3.6 13.9 17.3 61.8 0.2

Use of Internet 1.5 3.2 13.7 13.2 68.0 0.3

Email 1.5 1.6 8.4 9.0 79.2 0.3

Social media, video calls 45.7 7.7 15.7 7.6 22.8 0.5

Use software for design, calculation and simulation

41.7 5.5 12.8 8.1 31.2 0.7

Programming and software develop.

81.4 4.9 7.4 1.5 3.3 1.4

Design and maintain ICT 79.2 4.0 8.0 4.0 3.9 0.9

Programme and use CNC machines

92.3 1.4 3.8 0.2 0.5 1.7

163

Occupation Digital skills

Level of importance

1 N

ot

at

all im

port

ant

2

3 M

odera

tely

im

port

ant

4

5 E

ssential

DK

Programme and use robots 93.4 1.0 3.6 0.0 0.4 1.6

Sales, customer or

personal service

Use a word processor 23.7 11.4 22.4 19.1 23.4 0.2

Create a spreadsheet 24.1 11.8 20.1 20.0 23.8 0.3

Use of Internet 12.6 9.7 19.6 21.5 36.4 0.2

Email 14.0 6.9 13.4 16.6 48.6 0.5

Social media, video calls 55.5 13.9 12.3 6.6 11.4 0.3

Use software for design, calculation and simulation

58.5 7.6 10.2 7.0 15.9 0.8

Programming and software develop.

89.6 3.2 2.4 1.1 2.2 1.4

Design and maintain ICT 83.3 3.6 5.2 2.8 4.0 1.0

Programme and use CNC machines

95.6 1.4 0.6 0.3 0.6 1.5

Programme and use robots 96.7 1.0 0.4 0.1 0.3 1.5

Skilled agricultural

workers

Use a word processor 15.5 17.9 25.3 16.3 24.9 0.0

Create a spreadsheet 26.7 17.4 9.1 13.9 32.8 0.0

Use of Internet 9.6 9.4 25.8 14.2 41.0 0.0

Email 1.5 0.9 25.9 16.9 54.7 0.0

Social media, video calls 52.3 30.6 8.5 0.3 8.3 0.0

Use software for design, calculation and simulation

52.7 8.9 22.1 16.2 0.2 0.1

Programming and software develop.

82.7 8.5 8.3 0.2 0.2 0.2

Design and maintain ICT 66.7 8.6 24.3 0.3 0.0 0.1

Programme and use CNC machines

82.9 8.4 0.3 8.1 0.1 0.3

Programme and use robots 91.0 8.4 0.3 0.0 0.0 0.3

Building workers

Use a word processor 51.3 12.8 23.8 4.2 7.5 0.4

Create a spreadsheet 50.4 25.1 10.5 6.3 7.3 0.4

Use of Internet 39.3 23.2 14.3 10.1 12.5 0.5

Email 36.8 10.2 24.4 9.1 19.0 0.4

Social media, video calls 84.0 8.5 3.2 1.1 2.7 0.5

Use software for design, calculation and simulation

71.0 6.9 8.8 4.7 7.9 0.6

Programming and software develop.

90.8 2.6 1.8 1.7 1.4 1.7

Design and maintain ICT 88.7 3.7 3.2 1.8 1.6 0.9

Programme and use CNC machines

89.0 1.8 2.0 1.5 3.7 2.0

Programme and use robots 93.7 1.1 0.5 0.8 1.6 2.3

Plant machine

operators

Use a word processor 68.8 9.5 11.3 2.6 7.1 0.7

Create a spreadsheet 68.0 8.4 11.6 4.5 6.8 0.7

Use of Internet 54.7 8.4 14.8 8.0 13.5 0.7

Email 50.6 8.5 15.1 8.4 16.6 0.8

Social media, video calls 79.7 7.3 5.1 2.6 4.4 0.9

Use software for design, calculation and simulation

77.6 5.2 5.6 3.4 6.9 1.2

Programming and software develop.

89.6 2.1 3.8 0.8 2.3 1.3

Design and maintain ICT 88.6 2.5 4.2 2.4 0.9 1.3

Programme and use CNC machines

80.1 1.7 5.6 1.8 8.7 2.1

Programme and use robots

90.4 1.6 2.9 1.4 2.0 1.7

Elementary occupations

Use a word processor 68.7 11.5 8.6 3.3 7.2 0.7

Create a spreadsheet 70.4 10.1 8.5 3.2 7.1 0.7

164

Occupation Digital skills

Level of importance

1 N

ot

at

all im

port

ant

2

3 M

odera

tely

im

port

ant

4

5 E

ssential

DK

Use of Internet 62.9 12.1 9.6 4.2 10.5 0.7

Email 61.1 11.9 9.3 5.0 12.4 0.3

Social media, video calls 80.7 8.0 3.1 2.2 5.0 0.9

Use software for design, calculation and simulation

83.3 6.0 4.6 0.8 3.8 1.5

Programming and software develop.

92.0 2.3 2.7 0.2 1.2 1.6

Design and maintain ICT 91.1 3.5 1.8 0.2 1.5 1.9

Programme and use CNC machines

91.9 3.0 1.2 0.9 1.3 1.6

Programme and use robots 95.6 1.4 0.5 0.5 0.5 1.6

Q16. Thinking about the job categories in your workplace, please indicate, using a scale of 1 to 5 (where 1 means not at all important, 3 means moderately important and 5 means essential,) how important it is for employees in these categories to…: Number of valid responses=4,608 (job category: Managers); 2,224 (job category: Professionals); 1,886 (job category: Technicians); 3,929 (job category: Clerical workers); 2,298 (job category: Sales workers); 858 (job category: Skilled agricultural workers); 1,824 (job category: Building workers); 1,145 (job category: Plant machine operators); 1,319 (job category: Elementary occupations) N=5,644,799 (Managers); 3,463,858 (Professionals); 2,045,270 (Technicians); 4,172,004 (Clerical workers); 3,815,976 (Sales workers); 1,740,841 (Skilled agricultural workers); 2,475,089 (Building workers); 1,059,179 (Plant machine operators); 1,164,035 (Elementary occupations) Source: European Digital Skills Survey (weighted values)

Table A4.2 – Workplaces by type and level of importance of digital skills of employees in specific

occupations, EU28 (% of total employers)

Occupation Digital skills

Level of importance

Not

at

all im

port

ant

Som

ehow

im

port

ant

to e

ssential

Don’t k

now

Managers

Use a word processor 5.8 93.9 0.2

Create a spreadsheet 3.8 96.0 0.2

Use of Internet 2.3 97.5 0.2

Email 1.8 98.0 0.1

Social media, video calls 41.9 58.0 0.2

Use software for design, calculation and simulation 48.2 51.2 0.5

Programming and software develop. 81.4 17.5 1.2

Design and maintain ICT 67.7 31.9 0.5

Programme and use CNC machines 96.1 2.4 1.5

Programme and use robots 97.4 1.2 1.5

Professionals

Use a word processor 6.9 92.8 0.3

Create a spreadsheet 7.2 92.6 0.2

Use of Internet 4.4 95.3 0.2

Email 4.4 95.4 0.2

165

Occupation Digital skills

Level of importance

Not

at

all im

port

ant

Som

ehow

im

port

ant

to e

ssential

Don’t k

now

Social media, video calls 37.2 62.4 0.3

Use software for design, calculation and simulation 36.6 62.7 0.6

Programming and software develop. 68.1 30.9 1.0

Design and maintain ICT 60.4 38.6 1.0

Programme and use CNC machines 92.7 5.8 1.5

Programme and use robots 93.8 4.7 1.5

Technicians

Use a word processor 13.9 85.6 0.4

Create a spreadsheet 12.4 87.2 0.4

Use of Internet 8.1 91.3 0.6

Email 8.8 90.8 0.4

Social media, video calls 39.4 60.2 0.5

Use software for design, calculation and simulation 31.2 67.5 1.4

Programming and software develop. 64.4 33.9 1.6

Design and maintain ICT 55.3 42.7 2.0

Programme and use CNC machines 88.1 9.1 2.8

Programme and use robots 91.8 5.6 2.7

Clerical support workers

Use a word processor 4.4 95.4 0.2

Create a spreadsheet 3.1 96.6 0.2

Use of Internet 1.5 98.1 0.3

Email 1.5 98.2 0.3

Social media, video calls 45.7 53.8 0.5

Use software for design, calculation and simulation 41.7 57.6 0.7

Programming and software develop. 81.4 17.1 1.4

Design and maintain ICT 79.2 19.9 0.9

Programme and use CNC machines 92.3 5.9 1.7

Programme and use robots 93.4 5.0 1.6

Sales, customer or personal service

Use a word processor 23.7 76.3 0.2

Create a spreadsheet 24.1 75.7 0.3

Use of Internet 12.6 87.2 0.2

Email 14.0 85.5 0.5

Social media, video calls 55.5 44.2 0.3

Use software for design, calculation and simulation 58.5 40.7 0.8

Programming and software develop. 89.6 8.9 1.4

Design and maintain ICT 83.3 15.6 1.0

Programme and use CNC machines 95.6 2.9 1.5

Programme and use robots 96.7 1.8 1.5

Skilled agricultural workers

Use a word processor 15.5 84.4 0.0

Create a spreadsheet 26.7 73.2 0.0

Use of Internet 9.6 90.4 0.0

Email 1.5 98.4 0.0

Social media, video calls 52.3 47.7 0.0

Use software for design, calculation and simulation 52.7 47.4 0.1

Programming and software develop. 82.7 17.2 0.2

Design and maintain ICT 66.7 33.2 0.1

Programme and use CNC machines 82.9 16.9 0.3

Programme and use robots 91.0 8.7 0.3

Building workers

Use a word processor 51.3 48.3 0.4

Create a spreadsheet 50.4 49.2 0.4

Use of Internet 39.3 60.1 0.5

Email 36.8 62.7 0.4

Social media, video calls 84.0 15.5 0.5

Use software for design, calculation and simulation 71.0 28.3 0.6

Programming and software develop. 90.8 7.5 1.7

Design and maintain ICT 88.7 10.3 0.9

166

Occupation Digital skills

Level of importance

Not

at

all im

port

ant

Som

ehow

im

port

ant

to e

ssential

Don’t k

now

Programme and use CNC machines 89.0 9.0 2.0

Programme and use robots 93.7 4.0 2.3

Plant machine operators

Use a word processor 68.8 30.5 0.7

Create a spreadsheet 68.0 31.3 0.7

Use of Internet 54.7 44.7 0.7

Email 50.6 48.6 0.8

Social media, video calls 79.7 19.4 0.9

Use software for design, calculation and simulation 77.6 21.1 1.2

Programming and software develop. 89.6 9.0 1.3

Design and maintain ICT 88.6 10.0 1.3

Programme and use CNC machines 80.1 17.8 2.1

Programme and use robots 90.4 7.9 1.7

Elementary occupations

Use a word processor 68.7 30.6 0.7

Create a spreadsheet 70.4 28.9 0.7

Use of Internet 62.9 36.4 0.7

Email 61.1 38.6 0.3

Social media, video calls 80.7 18.3 0.9

Use software for design, calculation and simulation 83.3 15.2 1.5

Programming and software develop. 92.0 6.4 1.6

Design and maintain ICT 91.1 7.0 1.9

Programme and use CNC machines 91.9 6.4 1.6

Programme and use robots 95.6 2.9 1.6

Q16. Thinking about the job categories in your workplace, please indicate, using a scale of 1 to 5 (where 1 means not at all important, 3 means moderately important and 5 means essential,) how important it is for employees in these categories to…: Number of valid responses=4,608 (job category: Managers); 2,224 (job category: Professionals); 1,886 (job category: Technicians); 3,929 (job category: Clerical workers); 2,298 (job category: Sales workers); 858 (job category: Skilled agricultural workers); 1,824 (job category: Building workers); 1,145 (job category: Plant machine operators); 1,319 (job category: Elementary occupations) N=5,644,799 (Managers); 3,463,858 (Professionals); 2,045,270 (Technicians); 4,172,004 (Clerical workers); 3,815,976 (Sales workers); 1,740,841 (Skilled agricultural workers); 2,475,089 (Building workers); 1,059,179 (Plant machine operators); 1,164,035 (Elementary occupations) Source: European Digital Skills Survey (weighted values)

167

Table A4.3 – Workplaces by type and level of importance of digital skills of employees in

specific occupations, EU28 (% of total employers)

Occupation Digital skills

Level of importance

1 N

ot

at

all i

mp

ort

an

t

2

3 M

od

erate

ly i

mp

orta

nt

4

5 E

ssen

tial

DK

Managers

Basic 2.4 8.3 30.4 44.6 14.3 0.1

Advanced 66.7 30.2 1.0 0.7 1.2 0.2

Specialist 66.6 16.1 9.7 2.9 4.3 0.4

Professionals

Basic 5.4 6.7 24.8 45.4 17.4 0.2

Advanced 45.7 48.0 3.5 0.9 1.5 0.3

Specialist 55.9 16.2 9.8 5.6 11.6 0.7

Technicians

Basic 12.1 11.3 23.0 37.9 15.3 0.4

Advanced 46.8 43.7 5.3 0.8 2.4 1.0

Specialist 55.0 14.8 13.1 6.8 9.0 1.2

Clerical support workers

Basic 1.8 7.4 25.4 44.9 20.4 0.2

Advanced 54.9 38.9 4.3 0.5 0.9 0.5

Specialist 77.8 8.5 8.9 1.6 2.6 0.7

Sales, customer or personal service

Basic 19.1 21.0 29.9 23.0 6.9 0.1

Advanced 74.5 22.3 1.2 0.7 0.9 0.3

Specialist 83.6 8.1 4.4 1.5 1.7 0.7

Skilled agricultural workers

Basic 10.5 26.2 38.3 16.8 8.3 0.0

Advanced 74.7 16.7 8.3 0.2 0.1 0.1

Specialist 74.7 16.5 8.4 0.3 0.0 0.1

Building workers

Basic 50.4 27.5 14.3 6.1 1.5 0.3

Advanced 66.7 30.2 1.0 0.7 1.2 0.2

Specialist 88.5 5.6 2.9 1.2 1.2 0.7

Plant machine operators

Basic 65.3 15.4 10.1 7.0 1.6 0.7

Advanced 77.3 13.8 4.8 1.9 1.5 0.7

Specialist 88.3 3.8 5.1 0.9 0.9 1.0

Elementary occupations

Basic 72.2 12.7 6.3 3.9 4.6 0.3

Advanced 89.5 7.8 1.0 0.8 0.3 0.7

Specialist 91.6 3.0 2.5 0.3 1.2 1.4

Q16. Thinking about the job categories in your workplace, please indicate, using a scale of 1 to 5

(where 1 means not at all important, 3 means moderately important and 5 means essential,) how

important it is for employees in these categories to…:

Number of valid responses=4,608 (job category: Managers); 2,224 (job category: Professionals); 1,886 (job category: Technicians); 3,929 (job category: Clerical workers); 2,298 (job category: Sales workers); 858 (job category: Skilled agricultural workers); 1,824 (job category: Building workers); 1,145 (job category: Plant machine operators); 1,319 (job category: Elementary occupations) N=5,644,799 (Managers); 3,463,858 (Professionals); 2,045,270 (Technicians); 4,172,004 (Clerical

workers); 3,815,976 (Sales workers); 1,740,841 (Skilled agricultural workers); 2,475,089 (Building

workers); 1,059,179 (Plant machine operators); 1,164,035 (Elementary occupations)

Source: European Digital Skills Survey (weighted values)

168

Table A4.4 – Workplaces by type and level of importance of digital skills of employees

in specific occupations, EU28 (% of total employers)

Occupation Digital skills

Level of importance:

No

t at

all

im

po

rtan

t

So

meh

ow

im

po

rta

nt

to

essen

tial

Do

n't

kn

ow

Managers

Basic 2.4 97.6 0.1

Advanced 66.7 33.1 0.2

Specialist 66.6 33.0 0.4

Professionals

Basic 5.4 94.3 0.2

Advanced 45.7 53.9 0.3

Specialist 55.9 43.2 0.7

Technicians

Basic 12.1 87.5 0.4

Advanced 46.8 52.2 1.0

Specialist 55.0 43.7 1.2

Clerical support workers

Basic 1.8 98.1 0.2

Advanced 54.9 44.6 0.5

Specialist 77.8 21.6 0.7

Sales, customer or personal service

Basic 19.1 80.8 0.1

Advanced 74.5 25.1 0.3

Specialist 83.6 15.7 0.7

Skilled agricultural workers

Basic 10.5 89.6 0.0

Advanced 74.7 25.3 0.1

Specialist 74.7 25.2 0.1

Building workers

Basic 50.4 49.4 0.3

Advanced 66.7 33.1 0.2

Specialist 88.5 10.9 0.7

Plant machine operators

Basic 65.3 34.1 0.7

Advanced 77.3 22.0 0.7

Specialist 88.3 10.7 1.0

Elementary occupations

Basic 72.2 27.5 0.3

Advanced 89.5 9.9 0.7

Specialist 91.6 7.0 1.4

Q16. Thinking about the job categories in your workplace, please indicate, using a scale of 1

to 5 (where 1 means not at all important, 3 means moderately important and 5 means

essential,) how important it is for employees in these categories to…:

Number of valid responses=4,608 (job category: Managers); 2,224 (job category: Professionals); 1,886 (job category: Technicians); 3,929 (job category: Clerical workers); 2,298 (job category: Sales workers); 858 (job category: Skilled agricultural workers); 1,824 (job category: Building workers); 1,145 (job category: Plant machine operators); 1,319 (job category: Elementary occupations) N=5,644,799 (Managers); 3,463,858 (Professionals); 2,045,270 (Technicians); 4,172,004

(Clerical workers); 3,815,976 (Sales workers); 1,740,841 (Skilled agricultural workers);

2,475,089 (Building workers); 1,059,179 (Plant machine operators); 1,164,035 (Elementary

occupations)

Source: European Digital Skills Survey (weighted values)

169

Table A4.5 - Linear regression model on "How important ADVANCED DIGITAL

SKILLS are?"

Parameters B SE t Pr > |t|

Intercept 1.202415024 0.01879163 63.99 <.0001

Workplace type

Only workplace (omitted)

Headquarters 0.157872126 0.02452857 6.44 <.0001

Subsidiary_site -0.127779057 0.03533397 -3.62 0.0003

Workplace size

size < 10 (omitted)

size10_49 0.155830124 0.02095651 7.44 <.0001

size50_249 0.080852057 0.04335877 1.86 0.0623

size250 0.371274103 0.10121827 3.67 0.0002

Sector

sectorA (omitted)

sectorCD 0.268697838 0.03025655 8.88 <.0001

sectorF 0.198298166 0.03015942 6.57 <.0001

sectorGHI 0.120518218 0.02233051 5.4 <.0001

sectorJMN 0.292783933 0.02542001 11.52 <.0001

sectorPQ 0.126536436 0.03456203 3.66 0.0003

Female rate

fem_rate<26 (omitted)

fem_rate_26_50 -0.00906296 0.0194676 -0.47 0.6416

fem_rate_51_75 -0.052110779 0.02895877 -1.8 0.072

fem_rate_75 -0.116427039 0.02680266 -4.34 <.0001

University rate

univ_rate<26 (omitted)

univ_rate_26_50 0.026657418 0.02370968 1.12 0.2609

univ_rate_51_75 -0.01462403 0.03610693 -0.41 0.6855

univ_rate_75 -0.023931382 0.02526974 -0.95 0.3437

Young rate

young_rate<26 (omitted)

young_rate_26_50 -0.069732574 0.02261662 -3.08 0.0021

young_rate_51_75 -0.014938145 0.04756398 -0.31 0.7535

young_rate_75 0.015479685 0.04370476 0.35 0.7232

Old rate

old_rate<26 (omitted)

old_rate_26_50 -0.106391533 0.02040476 -5.21 <.0001

old_rate_51_75 -0.040525862 0.03969517 -1.02 0.3073

old_rate_75 -0.182940722 0.02746769 -6.66 <.0001

Ownership (q9) private (omitted)

public 0.112623557 0.03460364 3.25 0.0011

Markets (q11)

Local_market (omitted)

Regional_market 0.107557618 0.01821747 5.9 <.0001

National_market 0.204863766 0.01997028 10.26 <.0001

International_market 0.353760667 0.02442895 14.48 <.0001

N. obs 7448

normalized weight used yes

F-test Pr > F <.0001

R 0.328866234

Q16. Thinking about the job categories in your workplace, please indicate, using a scale of

1 to 5 (where 1 means not at all important, 3 means moderately important and 5 means

essential,) how important it is for employees in these categories to…:

Source: European Digital Skills Survey (weighted values)

170

Table A4.6 - Linear regression model on "How important SPECIALIST DIGITAL

SKILLS are?"

Parameters B SE t Pr > |t|

Intercept 1.169857735 0.02592531 45.12 <.0001

Workplace type

Only workplace (omitted)

Headquarters 0.294338023 0.03466174 8.49 <.0001

Subsidiary_site -0.083833934 0.0502513 -1.67 0.0953

Workplace size

size < 10 (omitted)

size10_49 0.067821095 0.02946885 2.3 0.0214

size50_249 0.007719273 0.06171388 0.13 0.9005

size250 0.251685083 0.14326898 1.76 0.079

Sector

sectorA (omitted)

sectorCD 0.22986343 0.04119149 5.58 <.0001

sectorF 0.177557568 0.03881864 4.57 <.0001

sectorGHI 0.175573224 0.03077729 5.7 <.0001

sectorJMN 0.699797332 0.03541365 19.76 <.0001

sectorPQ 0.262952294 0.04863621 5.41 <.0001

Female rate

fem_rate<26 (omitted)

fem_rate_26_50 -0.02981906 0.02715781 -1.1 0.2722

fem_rate_51_75 -0.229181797 0.04123441 -5.56 <.0001

fem_rate_75 -0.341556538 0.03776722 -9.04 <.0001

University rate

univ_rate<26 (omitted)

univ_rate_26_50 0.029761021 0.03359705 0.89 0.3757

univ_rate_51_75 0.03843772 0.05139634 0.75 0.4546

univ_rate_75 0.154764065 0.0355762 4.35 <.0001

Young rate

young_rate<26 (omitted)

young_rate_26_50 -0.035622886 0.03147051 -1.13 0.2577

young_rate_51_75 0.0362865 0.06711124 0.54 0.5887

young_rate_75 -0.024757471 0.06083973 -0.41 0.6841

Old rate

old_rate<26 (omitted)

old_rate_26_50 -0.033693283 0.02813237 -1.2 0.2311

old_rate_51_75 0.007131535 0.05450461 0.13 0.8959

old_rate_75 -0.003930795 0.03750943 -0.1 0.9165

Ownership (q9) private (omitted)

public 0.321606153 0.04871132 6.6 <.0001

Markets (q11)

Local_market (omitted)

Regional_market 0.098321768 0.02470761 3.98 <.0001

National_market 0.159948952 0.02804108 5.7 <.0001

International_market 0.274921547 0.03438102 8 <.0001

N. obs 7770

normalized weight used yes

F-test Pr > F <.0001

R 0.332625916

Q16. Thinking about the job categories in your workplace, please indicate, using a scale of

1 to 5 (where 1 means not at all important, 3 means moderately important and 5 means

essential,) how important it is for employees in these categories to…:

Source: European Digital Skills Survey (weighted values)

171

Table A4.7 – Employees in specific occupations by type of digital skills, EU28 (% of total

employees in specific occupational category)

Type of digital skill

Occupation

Man

ag

ers

Pro

fessio

nals

Tech

nic

ian

s

Cle

ric

al w

orkers

Sale

s w

orkers

Skille

d a

gric

wo

rkers

Bu

ild

ing

wo

rkers

Pla

nt

mach

ine

op

era

tors

Ele

men

tary

occu

pati

on

s

Use a word processor 93.2 41.7 42.2 58.7 35.9 63.9 31.2 28.9 37.6

Create a spreadsheet 95.0 41.5 42.4 59.6 36.5 63.8 33.4 30.3 37.6

Use of Internet 96.6 41.1 43.2 62.2 37.7 64.2 34.6 31.1 38.3

Email 97.3 42.0 42.7 61.8 37.8 64.6 34.9 30.2 38.5

Social media, video calls 57.9 25.4 30.8 38.0 23.4 41.8 19.0 16.7 21.5

Use software for design, calculation and simulation

51.3 25.6 25.8 35.8 21.5 45.9 21.5 19.7 20.8

Programming and software develop.

16.6 11.0 10.1 11.4 5.6 4.8 4.3 2.8 4.9

Design and maintain ICT 29.0 9.3 13.5 15.9 7.4 34.7 10.8 6.4 6.1

Programme and use CNC machines

1.9 0.5 1.9 1.6 0.6 1.7 1.7 1.1 0.6

Programme and use robots 0.8 0.3 1.1 0.5 0.2 0.7 0.6 0.3 0.3

Q17. Please provide your best estimate of the approximate number or share of employees carrying out such tasks (listed in Q16). Number of valid responses=4,608 (job category: Managers); 2,224 (job category: Professionals); 1,886 (job category: Technicians); 3,929 (job category: Clerical workers); 2,298 (job category: Sales workers); 858 (job category: Skilled agricultural workers); 1,824 (job category: Building workers); 1,145 (job category: Plant machine operators); 1,319 (job category: Elementary occupations) N=5,644,799 (Managers); 3,463,858 (Professionals); 2,045,270 (Technicians); 4,172,004 (Clerical workers); 3,815,976 (Sales workers); 1,740,841 (Skilled agricultural workers); 2,475,089 (Building workers); 1,059,179 (Plant machine operators); 1,164,035 (Elementary occupations) Source: European Digital Skills Survey (weighted values)

Table A4.8 – Occupations selected as the most important for day-to-day operations by sector and by type

and level of digital skills of employees in the occupation (level of importance 1 = not at all important, 3

= moderately important, 5 = essential)

Secto

r

Ran

k

Occupation (ISCO 4-digit)

Use a

word

pro

cessor

Cre

ate

a s

pre

asheet

Use o

f In

tern

et

Em

ail

Socia

l m

edia

, vid

eo c

alls

Use s

oft

ware

for

desig

n,

calc

ula

tion a

nd

sim

ula

tion

Pro

gra

mm

ing a

nd

soft

ware

dev

Desig

n a

nd m

ain

tain

ICT

Pro

gra

mm

e a

nd u

se

CN

C m

achin

es

Pro

gra

mm

e a

nd u

se

robots

A.

Agriculture

1 Agricultural and forestry production managers 4 4 4 4 2 3 1 2 1 1

2 Forestry and related workers 2 2 2 3 1 1 1 1 1 1

3 Managing directors and chief executives 4 4 4 5 2 3 1 2 1 1

4 Mixed crop growers 2 2 2 2 1 1 1 1 1 1

5 Mixed crop and animal producers 2 2 2 2 1 1 1 1 1 1

6 Field crop and vegetable growers 2 2 2 2 2 2 1 1 1 1

7 Gardeners, horticultural and nursery growers 2 2 2 3 2 2 1 1 1 1

8 Livestock and dairy producers 2 2 2 2 1 2 1 1 1 1

9 General office clerks 5 5 5 5 2 3 1 1 1 1

10 Animal producers not elsewhere classified 1 1 2 2 1 1 1 1 1 1

CD

.

Manuf

actu

ri

ng

and

utilitie

s

1 Manufacturing managers 4 4 4 4 2 3 2 2 2 1

2 Managing directors and chief executives 4 4 4 5 3 3 2 2 1 1

3 Clerical support workers not elsewhere 5 5 5 5 5 5 1 1 1 1

172

Secto

r

Ran

k

Occupation (ISCO 4-digit)

Use a

word

pro

cessor

Cre

ate

a s

pre

asheet

Use o

f In

tern

et

Em

ail

Socia

l m

edia

, vid

eo c

alls

Use s

oft

ware

for

desig

n,

calc

ula

tion a

nd

sim

ula

tion

Pro

gra

mm

ing a

nd

soft

ware

dev

Desig

n a

nd m

ain

tain

ICT

Pro

gra

mm

e a

nd u

se

CN

C m

achin

es

Pro

gra

mm

e a

nd u

se

robots

classified

4 Sales and marketing managers 5 5 5 5 4 4 2 3 1 1

5 General office clerks 5 5 5 5 3 4 1 1 1 1

6 Building frame and related trades workers not elsewhere classified

2 2 3 3 1 2 1 1 1 1

7 Finance managers 5 5 5 5 3 4 2 2 1 1

8 Industrial and production engineers 4 4 5 5 3 4 2 3 2 2

9 Craft and related workers not elsewhere

classified

1 1 2 2 1 1 1 1 1 1

10 Supply, distribution and related managers 4 4 5 5 3 3 1 2 1 1

F.

Constr

uction

1 Construction managers 3 3 4 4 2 3 1 2 1 1

2 House builders 2 2 2 2 1 1 1 1 1 1

3 Managing directors and chief executives 4 4 4 5 2 2 1 2 1 1

4 Building frame and related trades workers not

elsewhere classified

2 2 2 3 1 2 1 1 1 1

5 General office clerks 5 5 5 5 3 3 1 1 1 1

6 Civil engineers 4 4 5 5 2 5 2 3 1 1

7 Plumbers and pipe fitters 2 2 2 2 1 1 1 1 1 1

8 Clerical support workers not elsewhere

classified

5 4 5 5 4 4 1 1 1 1

9 Craft and related workers not elsewhere

classified

1 1 1 1 1 1 1 1 1 1

10 Carpenters and joiners 2 1 2 2 1 1 1 1 1 1

GH

I. C

om

merc

e,

transport

,

accom

modation a

nd food

serv

ice

1 Managing directors and chief executives 4 5 5 5 3 3 2 2 1 1

2 Shop sales assistants 3 3 3 4 2 2 1 1 1 1

3 Retail and wholesale trade managers 4 4 5 5 2 4 2 2 1 1

4 Sales and marketing managers 4 4 4 5 3 3 2 2 1 1

5 Waiters 1 1 2 2 1 1 1 1 1 1

6 Cooks 1 1 1 2 1 1 1 1 1 1

7 General office clerks 5 5 5 5 3 3 1 1 1 1

8 Shopkeepers 4 3 4 4 2 2 1 1 1 1

9 Sales workers not elsewhere classified 3 4 4 4 2 2 1 1 1 1

10 Supply, distribution and related managers 4 4 5 5 3 3 1 1 1 1

JMN

. In

form

ation a

nd

com

munic

ation;

pro

fessio

nal,

scie

ntific a

nd t

echnic

al activitie

s;

Adm

inis

trative s

erv

ices

1 Managing directors and chief executives 5 5 5 5 4 3 2 3 1 1

2 Clerical support workers not elsewhere

classified

4 4 4 5 3 3 1 1 1 1

3 Sales and marketing managers 4 4 4 5 4 3 2 2 1 1

4 Accountants 4 4 4 4 3 4 2 1 1 1

5 Information and communications technology service managers

4 4 5 5 4 4 3 4 1 1

6 General office clerks 5 5 5 5 4 3 1 1 1 1

7 Finance managers 5 5 5 5 4 4 2 2 1 1

8 Engineering professionals not elsewhere

classified

5 4 5 5 3 4 3 3 1 1

9 Software developers 4 4 5 5 4 5 5 4 1 1

10 Human resource managers 5 5 5 5 4 2 2 1 1 1

PQ

. Education a

nd h

um

an

health

1 Education managers 4 4 4 4 3 3 1 2 1 1

2 Managing directors and chief executives 4 4 4 5 3 2 2 2 1 1

3 Specialist medical practitioners 4 3 4 4 2 2 2 1 1 1

4 Health services managers 4 4 4 4 3 3 2 2 1 1

5 General office clerks 4 4 4 4 2 3 1 1 1 1

6 Dentists 3 3 3 3 2 2 2 2 1 1

7 Personal care workers in health services not

elsewhere classified

3 3 3 3 1 1 1 1 1 1

8 Generalist medical practitioners 3 3 3 3 2 2 1 1 1 1

9 Health professionals not elsewhere classified 3 3 3 3 2 2 1 1 1 1

173

Secto

r

Ran

k

Occupation (ISCO 4-digit)

Use a

word

pro

cessor

Cre

ate

a s

pre

asheet

Use o

f In

tern

et

Em

ail

Socia

l m

edia

, vid

eo c

alls

Use s

oft

ware

for

desig

n,

calc

ula

tion a

nd

sim

ula

tion

Pro

gra

mm

ing a

nd

soft

ware

dev

Desig

n a

nd m

ain

tain

ICT

Pro

gra

mm

e a

nd u

se

CN

C m

achin

es

Pro

gra

mm

e a

nd u

se

robots

10 Primary school teachers 4 4 5 4 3 3 1 1 1 1

Q19. Thinking about these jobs in your workplace, please indicate using a scale of 1 to 5 (where 1 means not at all important, 3 means moderately important and 5 means essential) how important for day-to-day activities it is for employees in these jobs to…: Number of valid responses: 6,264 Source: European Digital Skills Survey (unweighted values)

Table A4.9 – Occupations selected as the most important for day-to-day operations by type of change in the job tasks due to the use of ICT in last five years, by sector (%)

Secto

r

Rank Occupation (ISCO 4-digit)

Type of change

No

ch

an

ge a

t all

Min

or c

han

ge

Mo

derate

ch

an

ge

Majo

r c

han

ge

Ch

an

ge (

fro

m m

ino

r t

o m

ajo

r)

A.

Agriculture

1 Agricultural and forestry production

managers 16.6 29.7 39.3 14.5 83.4

2 Forestry and related workers 44.8 27.6 22.4 5.2 55.2

3 Managing directors and chief executives 8.2 32.7 51.0 8.2 91.8

4 Mixed crop growers 45.0 43.3 10.0 1.7 55.0

5 Mixed crop and animal producers 43.1 37.9 8.6 10.3 56.9

6 Field crop and vegetable growers 52.7 18.2 20.0 9.1 47.3

7 Gardeners, horticultural and nursery

growers 33.9 39.3 19.6 7.1 66.1

8 Livestock and dairy producers 31.9 38.3 21.3 8.5 68.1

9 General office clerks 11.1 8.9 64.4 15.6 88.9

10 Animal producers not elsewhere classified 55.9 20.6 14.7 8.8 44.1

CD

. M

anufa

ctu

ring a

nd u

tilities

1 Manufacturing managers 22.3 18.4 42.2 17.0 77.7

2 Managing directors and chief executives 6.4 16.8 59.2 17.6 93.6

3 Clerical support workers not elsewhere classified

4.1 1.4 68.5 26.0 95.9

4 Sales and marketing managers 9.4 29.7 32.8 28.1 90.6

5 General office clerks 3.6 14.3 55.4 26.8 96.4

6 Building frame and related trades workers

not elsewhere classified 22. 42.2 33.3 2.2 77.8

7 Finance managers 4.9 17.1 61.0 17.1 95.1

8 Industrial and production engineers 16.7 11.9 45.2 26.2 83.3

9 Craft and related workers not elsewhere

classified 65.1 14.0 14.0 7.0 34.9

10 Supply, distribution and related managers 21.2 36.4 27.3 15.2 78.8

F.

Constr

uction 1 Construction managers 26.6 29.7 30.4 13.3 73.4

2 House builders 39.3 32.9 25.0 2.9 60.7

3 Managing directors and chief executives 6.6 37.2 45.5 10.7 93.4

4 Building frame and related trades workers

not elsewhere classified 40.8 34.0 20. 4.9 59.2

5 General office clerks 11.1 5.6 44.4 38.9 88.9

174

Secto

r

Rank Occupation (ISCO 4-digit)

Type of change

No

ch

an

ge a

t all

Min

or c

han

ge

Mo

derate

ch

an

ge

Majo

r c

han

ge

Ch

an

ge (

fro

m m

ino

r t

o m

ajo

r)

6 Civil engineers 6.3 20.8 47.9 25.0 93.8

7 Plumbers and pipe fitters 50.0 28.6 11.9 9.5 50.0

8 Clerical support workers not elsewhere

classified 9.8 9.8 58.5 22.0 90.2

9 Craft and related workers not elsewhere

classified 83.8 10.8 5.4 0.0 16.2

10 Carpenters and joiners 68.8 12.5 12.5 6.3 31.3

GH

I. C

om

merc

e,

transport

,

accom

modation a

nd food

serv

ice

1 Managing directors and chief executives 4.6 20.8 61.5 13.1 95.4

2 Shop sales assistants 20.5 35.2 31.1 13.1 79.5

3 Retail and wholesale trade managers 9.8 29.3 39.1 21.7 90.2

4 Sales and marketing managers 16.9 25.8 37.1 20.2 83.1

5 Waiters 42.3 44.9 10.3 2.6 57.7

6 Cooks 66.7 20.0 10.7 2.7 33.3

7 General office clerks 9.5 4.8 61.9 23.8 90.5

8 Shopkeepers 6.6 37.7 39.3 16.4 93.4

9 Sales workers not elsewhere classified 44.3 9.8 34.4 11.5 55.7

10 Supply, distribution and related managers 6.8 25.4 47.5 20.3 93.2

JMN

. In

form

ation a

nd

com

munic

ation;

pro

fessio

nal,

scie

ntific a

nd t

echnic

al activitie

s;

Adm

inis

trative s

erv

ices

1 Managing directors and chief executives 7.7 14.6 46.9 30.8 92.3

2 Clerical support workers not elsewhere

classified 7.1 23.2 60.7 8.9 92.9

3 Sales and marketing managers 17.6 19.6 39.2 23.5 82.4

4 Accountants 2.0 38.8 36.7 22.4 98.0

5 Information and communications technology

service managers 15.6 6.7 35.6 42.2 84.4

6 General office clerks 9.3 2.3 67.4 20.9 90.7

7 Finance managers 8.1 10.8 35.1 45.9 91.9

8 Engineering professionals not elsewhere

classified 15.6 6.3 37.5 40.6 84.4

9 Software developers 15.6 9.4 25.0 50.0 84.4

10 Human resource managers 16.7 16.7 43.3 23.3 83.3

PQ

. Education a

nd h

um

an

health

1 Education managers 6.3 15.0 55.0 23.8 93.8

2 Managing directors and chief executives 7.0 11.3 67.6 14.1 93.0

3 Specialist medical practitioners 22.0 28.8 28.8 20.3 78.0

4 Health services managers 7.5 26.4 45.3 20.8 92.5

5 General office clerks 10.9 15.2 45.7 28.3 89.1

6 Dentists 9.3 41.9 37.2 11.6 90.7

7 Personal care workers in health services not

elsewhere classified 13.2 34.2 44.7 7.9 86.8

8 Generalist medical practitioners 5.6 8.3 52.8 33.3 94.4

9 Health professionals not elsewhere classified 10.0 30.0 50.0 10.0 90.0

10 Primary school teachers 6.5 9.7 41.9 41.9 93.5

Q20. Thinking about these jobs in your workplace, please indicate if and to what extent the use of ICT has changed

the way job tasks are carried out. Please refer to the timespan of the last 5 years.

Number of valid responses: 6,264 Source: European Digital Skills Survey (unweighted values)

175

Table A4.10 – Occupations selected as the most important for day-to-day operations by type of change in

the job tasks due to the use of ICT in next five years, by sector (%)

Secto

r

Rank Occupation (ISCO 4-digit)

Type of change

No

ch

an

ge a

t all

Min

or c

han

ge

Mo

derate

ch

an

ge

Majo

r c

han

ge

Ch

an

ge (

fro

m m

ino

r t

o m

ajo

r)

A.

Ag

ric

ult

ure

1 Agricultural and forestry production

managers 27.7 24.8 34.0 13.5 72.3

2 Forestry and related workers 51.8 19.6 19.6 8.9 48.2

3 Managing directors and chief executives 17.5 41.2 37.1 4.1 82.5

4 Mixed crop growers 66.7 21.7 8.3 3.3 33.3

5 Mixed crop and animal producers 53.4 24.1 12.1 10.3 46.6

6 Field crop and vegetable growers 60.7 14.3 16.1 8.9 39.3

7 Gardeners, horticultural and nursery

growers 51.8 23.2 12.5 12.5 48.2

8 Livestock and dairy producers 41.3 34.8 17.4 6.5 58.7

9 General office clerks 11.1 8.9 53.3 26.7 88.9

10 Animal producers not elsewhere classified 54.5 18.2 15.2 12.1 45.5

CD

. M

an

ufa

ctu

rin

g a

nd

uti

liti

es 1 Manufacturing managers 22.0 20.5 34.0 23.5 78.0

2 Managing directors and chief executives 9.9 24.8 54.5 10.7 90.1

3 Clerical support workers not elsewhere

classified 1.4 4.2 68.1 26.4 98.6

4 Sales and marketing managers 22.6 32.3 32.3 12.9 77.4

5 General office clerks 1.8 10.5 56.1 31.6 98.2

6 Building frame and related trades workers

not elsewhere classified 24.4 53.3 22.2 0.0 75.6

7 Finance managers 11.9 19.0 57.1 11.9 88.1

8 Industrial and production engineers 11.6 20.9 46.5 20.9 88.4

9 Craft and related workers not elsewhere

classified 69.0 4.8 19.0 7.1 31.0

10 Supply, distribution and related managers 16.1 38.7 16.1 29.0 83.9

F.

Co

nstr

ucti

on

1 Construction managers 29.7 23.2 29.0 18.1 70.3

2 House builders 50.0 20.7 26.4 2.9 50.0

3 Managing directors and chief executives 10.0 50.0 35.8 4.2 90.0

4 Building frame and related trades workers

not elsewhere classified 44.1 32.4 14.7 8.8 55.9

5 General office clerks 9.6 9.6 51.9 28.8 90.4

6 Civil engineers 8.5 19.1 51.1 21.3 91.5

7 Plumbers and pipe fitters 53.5 23.3 11.6 11.6 46.5

8 Clerical support workers not elsewhere

classified 4.9 17.1 56.1 22.0 95.1

9 Craft and related workers not elsewhere

classified 88.9 2.8 8.3 0.0 11.1

10 Carpenters and joiners 59.4 18.8 21.9 0.0 40.6

GH

I.

Co

mm

erce,

tran

sp

ort,

acco

mm

od

ati

on

an

d

foo

d s

ervic

e

1 Managing directors and chief executives 16.2 23.1 54.6 6.2 83.8

2 Shop sales assistants 32.5 29.1 24.8 13.7 67.5

3 Retail and wholesale trade managers 13.5 30.3 43.8 12.4 86.5

4 Sales and marketing managers 26.4 28.7 26.4 18.4 73.6

5 Waiters 54.7 26.7 18.7 0.0 45.3

6 Cooks 77.0 10.8 10.8 1.4 23.0

7 General office clerks 8.1 4.8 48.4 38.7 91.9

8 Shopkeepers 6.6 55.7 19.7 18.0 93.4

9 Sales workers not elsewhere classified 46.6 19.0 29.3 5.2 53.4

176

Secto

r

Rank Occupation (ISCO 4-digit)

Type of change

No

ch

an

ge a

t all

Min

or c

han

ge

Mo

derate

ch

an

ge

Majo

r c

han

ge

Ch

an

ge (

fro

m m

ino

r t

o m

ajo

r)

10 Supply, distribution and related managers 15.5 24.1 46.6 13.8 84.5

JM

N.

In

form

ati

on

an

d

co

mm

un

ica

tio

n;

pro

fessio

nal,

scie

nti

fic a

nd

tech

nic

al

acti

vit

ies;

Ad

min

istr

ati

ve

servic

es

1 Managing directors and chief executives 6.2 16.9 53.1 23.8 93.8

2 Clerical support workers not elsewhere classified

5.7 22.6 54.7 17.0 94.3

3 Sales and marketing managers 17.6 19.6 45.1 17.6 82.4

4 Accountants 14.9 31.9 36.2 17.0 85.1

5 Information and communications

technology service managers 15.6 17.8 31.1 35.6 84.4

6 General office clerks 9.3 4.7 60.5 25.6 90.7

7 Finance managers 2.9 17.1 42.9 37.1 97.1

8 Engineering professionals not elsewhere

classified 15.2 6.1 48.5 30.3 84.8

9 Software developers 9.7 9.7 32.3 48.4 90.3

10 Human resource managers 20.0 16.7 46.7 16.7 80.0

PQ

. E

du

cati

on

an

d h

um

an

healt

h

1 Education managers 6.3 21.5 50.6 21.5 93.7

2 Managing directors and chief executives 2.9 23.2 63.8 10.1 97.1

3 Specialist medical practitioners 29.3 31.0 24.1 15.5 70.7

4 Health services managers 15.4 23.1 34.6 26.9 84.6

5 General office clerks 13.3 15.6 51.1 20.0 86.7

6 Dentists 14.0 55.8 18.6 11.6 86.0

7 Personal care workers in health services

not elsewhere classified 13.5 54.1 27.0 5.4 86.5

8 Generalist medical practitioners 2.8 16.7 44.4 36.1 97.2

9 Health professionals not elsewhere

classified 17.2 27.6 48.3 6.9 82.8

10 Primary school teachers 3.2 25.8 54.8 16.1 96.8

Q21a. Thinking about these jobs in your workplace, please indicate if and to what extent you think the use of ICT will

change the way job tasks are carried out. Please refer to the timespan of the next 5 years.

Number of valid responses: 6,264 Source: European Digital Skills Survey (unweighted values)

177

Chapter 5. The digital skills challenge in European workplaces

Figure A5.1 – Workplaces reporting digital skill gaps in the workforce by size of workplace in

sampled countries (% of workplaces)

Q17/Q23. Please provide your best estimate of the approximate number or share of employees carrying out such tasks and indicate how many of them are fully proficient in carrying out the tasks. Please note that a proficient employee is someone who is able to do the job/carrying out the task to the required level.Number of valid responses: 4,569 N= 1,772,219 Source: European Digital Skills Survey (weighted values)

17.6

7.7

17.1

1.3

27.0

12.2

27.7

18.1 18.1

24.1

32.0

22.0 21.5 18.7

29.0

42.6

14.1

43.6 41.4

9.9

33.3

54.0

3.4

18.9

22.1

9.9

17.8

2.9

26.7

14.9

DE FI UK PT SE SK

2-9 10-49 50-249 250 or more Total

178

Figure A5.2 – Workplaces reporting digital skill gaps in the workforce by sector in

sampled countries (% of workplaces)

Q17/Q23. Please provide your best estimate of the approximate number or share of employees

carrying out such tasks and indicate how many of them are fully proficient in carrying out the

tasks. Please note that a proficient employee is someone who is able to do the job/carrying out the

task to the required level.

Number of valid responses: 4,569 N= 1,772,219

Source: European Digital Skills Survey (weighted values)

0.2

1.4

0.7

0.0

27.2

19.4

25.8

15.9

9.2

9.7

51.4

23.0

37.3 17.2

9.6

19.2

8.9

39.0

13.7

16.3

16.7

23.9

15.2

7.2

7.8

28.8

13.3

17.5

5.0

13.6

12.4

22.1

9.9

17.8

2.9

26.7

14.9

DE

FI

UK

PT

SE

SK

A. Agriculture

CD. Manufacturing and utilities

F. Construction

GHI. Commerce, transport, accommodation and food service

JMN. Information and communication; Professional, scientific and technical activities; Administrative services

PQ. Education and human health

Total

179

Table A5.1 – Workforce with digital skill gaps by occupation and type of digital skill, EU28 (%)

Type of digital skills

Occupations

Managers

Pro

fessio

nals

Technic

ians

Cle

rical w

ork

ers

Sale

s w

ork

ers

Skille

d a

gri

c w

ork

ers

Buildin

g w

ork

ers

Pla

nt

machin

e o

pera

tors

Ele

menta

ry o

ccupations

Use a word processor 14.1 11.0 21.9 17.4 21.0 8.2 13.4 16.7 20.7

Create a spreadsheet 13.7 11.0 21.4 17.0 19.1 8.1 12.6 15.9 20.2

Use of Internet 13.6 11.0 21.8 17.4 19.6 9.1 12.5 15.7 20.0

Email 13.6 11.1 20.9 16.1 19.7 9.1 12.9 13.4 19.0

Social media, video calls 14.4 15.8 23.2 18.3 17.3 6.7 10.6 18.6 22.5

Use software for design, calculation and

simulation 12.6 9.7 17.6 15.2 17.2 8.4 10.1 16.3 14.0

Programming and software develop 13.7 10.8 16.4 14.7 23.2 7.2 12.1 16.0 12.3

Design and maintain ICT 12.7 15.5 16.4 14.5 22.0 11.3 12.7 15.4 23.0

Programme and use CNC machines 16.2 18.2 12.4 16.0 18.4 37.5 12.4 16.5 3.0

Programme and use robots 18.7 9.4 10.2 15.0 18.8 93.6 8.2 15.8 4.4

Q17/Q23. Please provide your best estimate of the approximate number or share of employees carrying out

such tasks and indicate how many of them are fully proficient in carrying out the tasks. Please note that a

proficient employee is someone who is able to do the job/carrying out the task to the required level.

N=5,644,799 (Managers); 3,463,858 (Professionals); 2,045,270 (Technicians); 4,172,004 (Clerical workers);

3,815,976 (Sales workers); 1,740,841 (Skilled agricultural workers); 2,475,089 (Building workers);

1,059,179 (Plant machine operators); 1,164,035 (Elementary occupations)

Source: European Digital Skills Survey (weighted values)

Table A5.2 – Workforce with digital skill gaps by occupation, type of digital skill (three levels) and

sector, EU28 (%)

Occupation Sector

Digital skills

Basic

dig

ital skills

Advanced d

igital

skills

Specia

list

dig

ital

skills

Managers

A. Agriculture 0.3 2.0 1.0

CD. Manufacturing and utilities 19.9 17.9 19.4

F. Construction 17.5 14.8 17.0

GHI. Commerce, transport, accommodation and food service 15.3 12.9 13.2

JMN. Information and communication; professional,

scientific and technical activities; Administrative services 13.2 12.0 10.6

PQ. Education and human health 19.0 17.3 23.3

Total 13.9 13.2 13.3

Professionals

A. Agriculture 6.8 5.6 40.4

CD. Manufacturing and utilities 18.6 11.1 8.9

F. Construction 8.3 8.5 12.4

GHI. Commerce, transport, accommodation and food service 11.8 12.9 17.4

JMN. Information and communication; professional,

scientific and technical activities; Administrative services 11.6 9.7 9.9

PQ. Education and human health 9.9 8.4 18.7

Total 11.6 10.4 12.8

Technicians A. Agriculture 2.5 1.6 32.7

180

Occupation Sector

Digital skills

Basic

dig

ital skills

Advanced d

igital

skills

Specia

list

dig

ital

skills

CD. Manufacturing and utilities 19.5 13.3 9.8

F. Construction 18.1 18.0 10.2

GHI. Commerce, transport, accommodation and food service 22.8 23.4 25.3

JMN. Information and communication; professional,

scientific and technical activities; Administrative services 18.0 11.6 9.4

PQ. Education and human health 34.7 19.2 49.0

Total 21.8 17.4 16.4

Clerical workers

A. Agriculture 6.3 1.3 0.4

CD. Manufacturing and utilities 17.3 17.0 18.3

F. Construction 19.0 16.0 15.6

GHI. Commerce, transport, accommodation and food service 17.2 15.1 13.7

JMN. Information and communication; professional,

scientific and technical activities; Administrative services 15.2 14.8 8.5

PQ. Education and human health 18.5 19.0 40.8

Total 17.2 15.8 14.7

Sales workers

A. Agriculture 3.2 0.9 14.8

CD. Manufacturing and utilities 17.7 15.6 19.2

F. Construction 17.9 23.2 18.9

GHI. Commerce, transport, accommodation and food service 19.6 17.7 23.3

JMN. Information and communication; professional,

scientific and technical activities; Administrative services 15.1 11.2 9.6

PQ. Education and human health 30.5 30.8 55.0

Total 19.5 17.6 22.8

Skilled agric workers

A. Agriculture 2.0 9.4 1.1

CD. Manufacturing and utilities 5.2 2.6 4.3

F. Construction 73.6 98.1 94.4

GHI. Commerce, transport, accommodation and food service 12.9 0.0 22.6

JMN. Information and communication; professional,

scientific and technical activities; Administrative services 24.9 35.0 56.8

PQ. Education and human health 100.0 100.0 100.0

Total 8.4 8.0 14.0

Building workers

A. Agriculture 0.0 48.4 90.7

CD. Manufacturing and utilities 3.7 10.8 16.3

F. Construction 8.6 12.7 14.0

GHI. Commerce, transport, accommodation and food service 5.7 10.4 9.4

JMN. Information and communication; professional,

scientific and technical activities; Administrative services 3.8 5.8 3.7

PQ. Education and human health 0.0 7.4 70.1

Total 5.6 10.9 12.9

Plant machine operators

A. Agriculture 86.5 94.7 98.8

CD. Manufacturing and utilities 19.7 18.5 14.7

F. Construction 14.4 14.1 17.5

GHI. Commerce, transport, accommodation and food service 13.8 15.2 16.7

JMN. Information and communication; professional,

scientific and technical activities; Administrative services 7.1 0.6 0.4

PQ. Education and human health 16.7 21.0 35.1

Total 15.9 16.5 15.3

Elementary occupations

A. Agriculture 14.4 8.1 14.2

CD. Manufacturing and utilities 22.8 17.9 9.6

F. Construction 8.1 7.1 13.8

GHI. Commerce, transport, accommodation and food service 20.9 14.6 17.2

JMN. Information and communication; professional,

scientific and technical activities; Administrative services 18.4 16.1 9.4

PQ. Education and human health 24.1 16.3 53.4

Total 20.6 15.3 17.6

Q17/Q23. Please provide your best estimate of the approximate number or share of employees carrying out such tasks and indicate how many of them are fully proficient in carrying out the tasks. Please note that a proficient employee is someone who is able to do the job/carrying out the task to the required level. N=5,644,799 (Managers); 3,463,858 (Professionals); 2,045,270 (Technicians); 4,172,004 (Clerical workers); 3,815,976 (Sales workers); 1,740,841 (Skilled agricultural workers); 2,475,089 (Building workers); 1,059,179 (Plant machine operators); 1,164,035 (Elementary occupations) Source: European Digital Skills Survey (weighted values)

181

Table A5.3 – Workforce with digital skill gaps by occupation, type of digital skill (three levels) and size, EU28 (%)

Occupation Size

Basic

dig

ital skills

Advanced d

igital skills

Specia

list

dig

ital skills

Managers

2-9 11.4 10.6 10.6

10-49 18.1 15.9 15.9

50-249 20.6 26.0 31.0

250 or more 40.6 34.8 44.8

Total 13.9 13.2 13.3

Professionals

2-9 10.8 12.0 16.5

10-49 11.5 7.6 7.3

50-249 12.1 13.0 21.4

250 or more 30.1 28.5 32.8

Total 11.6 10.4 12.8

Technicians

2-9 17.8 20.3 18.1

10-49 22.7 18.9 14.7

50-249 24.9 11.3 19.5

250 or more 28.4 10.2 4.0

Total 21.8 17.4 16.4

Clerical workers

2-9 13.6 11.7 8.1

10-49 17.3 15.1 17.1

50-249 22.9 24.6 25.5

250 or more 40.9 31.3 41.8

Total 17.2 15.8 14.7

Sales workers

2-9 16.3 13.6 17.8

10-49 20.2 17.7 27.1

50-249 30.3 31.9 31.7

250 or more 21.9 13.4 16.9

Total 19.5 17.6 22.8

Skilled agric workers

2-9 7.4 7.6 13.3

10-49 37.9 43.0 43.9

50-249 10.5 7.2 6.1

250 or more 4.4 0.7 0.0

Total 8.4 8.0 14.0

Building workers

2-9 7.4 13.3 11.3

10-49 3.9 10.6 16.5

50-249 4.3 5.7 7.2

250 or more 8.5 8.7 10.1

Total 5.6 10.9 12.9

Plant machine operators

2-9 5.6 7.1 13.8

10-49 16.1 15.1 12.8

50-249 16.3 19.4 25.4

250 or more 59.6 40.4 2.5

Total 15.9 16.5 15.3

Elementary occupations

2-9 28.9 21.3 20.3

10-49 14.4 8.1 8.1

50-249 25.2 25.2 35.1

250 or more 25.4 7.5 7.3

Total 20.6 15.3 17.6

Q17/Q23. Please provide your best estimate of the approximate number or share of employees carrying out such tasks and indicate how many of them are fully proficient in carrying out the tasks. Please note that a proficient employee is someone who is able to do the job/carrying out the task to the required level. Number of valid responses=4,608 (job category: Managers); 2,224 (job category: Professionals); 1,886 (job category: Technicians); 3,929 (job category: Clerical workers); 2,298 (job category: Sales workers); 858 (job category: Skilled agricultural workers); 1,824 (job category: Building workers); 1,145 (job category: Plant machine operators); 1,319 (job category: Elementary occupations) N=5,644,799 (Managers); 3,463,858 (Professionals); 2,045,270 (Technicians); 4,172,004 (Clerical workers); 3,815,976 (Sales workers); 1,740,841 (Skilled agricultural workers); 2,475,089 (Building workers); 1,059,179 (Plant machine operators); 1,164,035 (Elementary occupations) Source: European Digital Skills Survey (weighted values)

182

Table A5.4 - Workplaces reporting impact of digital skills gaps on overall performance, by sector and

size, EU28 (%)

Impact of digital skill gaps on overall

performance

Yes,

a m

ajo

r im

pact

Yes,

a m

inor

impact

No

Don't k

now

Siz

e

2 to 9 16.5 17.6 63.8 2.1

10 to 49 24.4 21.8 51.2 2.5

50 to 249 18.6 25.9 53.6 1.8

250 + 10.4 22.9 61.5 5.2

Secto

r

A. Agriculture 25.8 8.8 60.5 4.9

CD. Manufacturing and utilities 12.5 14.8 71.4 1.3

F. Construction 8.0 15.8 75.2 0.9

GHI. Commerce, transport, accommodation and food service 12.9 24.3 61.6 1.3

JMN. Information and communication; Professional, scientific

and technical activities; Administrative services 28.5 19.4 51.0 1.2

PQ. Education and human health 10.3 29.9 57.8 2.0

Total 17.6 18.5 61.7 2.1

Q26. Thinking about your workplace as a whole, does the fact that some of your employees are not fully proficient in carrying out the indicated tasks involving ICT use have an impact on your workplace performance? (Yes, a major impact/Yes, a minor impact/No/Don’t know/Not applicable-100% proficient) Number of valid responses: 6,776 N=12,268,096 Source: European Digital Skills Survey (weighted values)

183

Table A5.5 - Logistic regression: probability that digital skills gaps have a major impact on overall

performance (Odds ratios).

N. obs 4466

normalized weight used yes

model estimates statistics Only intercept Intercept and covariates

AIC 4117.243 3833.566

SC 4123.647 4006.481

-2 Log L 4115.243 3779.566

Test Chi-square DF Pr > ChiSQ

Likelihood ratio 335.6769 26 <.0001

Score 374.0324 26 <.0001

Wald 322.1946 26 <.0001

Parameters B SE Chi-square

(Ward) Pr > ChiSQ Odds Ratio

Interc -1.8291 0.1039 310.0493 <.0001

Workplace type

Only workplace (omitted)

Headquarters -0.2045 0.1535 1.7745 0.1828 0.815

Subsidiary_site -1.4336 0.3116 21.1666 <.0001 0.238

Workplace size

size < 10 (omitted)

size10_49 0.875 0.1141 58.8331 <.0001 2.399

size50_249 0.6272 0.2335 7.2146 0.0072 1.872

size250 0.026 0.6038 0.0019 0.9656 1.026

Sector

sectorA (omitted)

sectorCD -0.7889 0.1924 16.8102 <.0001 0.454

sectorF -0.6616 0.1878 12.409 0.0004 0.516

sectorGHI -0.3583 0.1351 7.0355 0.008 0.699

sectorJMN 0.369 0.1404 6.9068 0.0086 1.446

sectorPQ -0.8143 0.2239 13.2265 0.0003 0.443

Female rate

fem_rate<26 (omitted)

fem_rate_26_50 0.2139 0.1162 3.3853 0.0658 1.238

fem_rate_51_75 0.2228 0.1711 1.6958 0.1928 1.25

fem_rate_75 0.187 0.1594 1.3771 0.2406 1.206

University rate

univ_rate<26 (omitted)

univ_rate_26_50 0.00723 0.1409 0.0026 0.9591 1.007

univ_rate_51_75 0.2298 0.2165 1.1258 0.2887 1.258

univ_rate_75 0.3255 0.1512 4.6311 0.0314 1.385

Young rate

young_rate<26 (omitted)

young_rate_26_50 -0.0763 0.1364 0.313 0.5758 0.927

young_rate_51_75 0.544 0.2459 4.894 0.0269 1.723

young_rate_75 -0.2224 0.2865 0.6025 0.4376 0.801

Old rate

old_rate<26 (omitted)

old_rate_26_50 -0.0563 0.1219 0.2129 0.6445 0.945

old_rate_51_75 -0.1388 0.2588 0.2874 0.5919 0.87

old_rate_75 -0.4031 0.1808 4.97 0.0258 0.668

Ownership (q9) private (omitted)

public -0.1131 0.2134 0.2807 0.5962 0.893

Markets (q11)

Local_market (omitted)

Regional_market -0.2998 0.1152 6.7725 0.0093 0.741

National_market -0.2684 0.1265 4.499 0.0339 0.765

International_market 0.7969 0.126 40.0012 <.0001 2.219

* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; P,Q: Education and human health and social work activities. Q26. Thinking about your workplace as a whole, does the fact that some of your employees are not fully proficient in carrying out the indicated tasks involving ICT use have an impact on your workplace performance? (Yes, a major impact/Yes, a minor impact/No/Don’t know/Not applicable-100% proficient)

Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Note: workplaces with fully proficient workers (e.g. without digital skills gaps) are excluded from the analysis Source: Elaboration on European Digital Skills Survey (weighted values)

184

Figure A5.3 - Workplaces reporting impact of digital skills gaps on overall performance, by sector

and size, in sampled countries (%)

Q26. Thinking about your workplace as a whole, does the fact that some of your employees are not fully proficient in carrying out the indicated tasks involving ICT use have an impact on your workplace performance? (Yes, a major impact/Yes, a minor impact/No/Don’t know/Not applicable-100% proficient) Number of valid responses: 6,776 N=3,750,539 Source: European Digital Skills Survey (weighted values)

Table A5.6 - Workplaces reporting having or not having taken action to tackle digital skill

gaps, EU28 (% of workplaces with digital skill gaps)

Action undertaken?

Yes No, but have plans to No

Siz

e

2 to 9 9.0 10.0 81.0

10 to 49 23.9 18.3 57.8

50 to 249 36.4 8.1 55.6

250 + 29.3 23.9 46.8

Secto

r

A. Agriculture 0.1 14.6 85.3

CD. Manufacturing and utilities 17.7 8.0 74.3

F. Construction 11.5 7.4 81.1

GHI. Commerce, transport, accommodation and food service

14.1 10.3 75.5

JMN. Information and communication; Professional, scientific and technical activities; Administrative services

18.4 10.6 71.0

PQ. Education and human health

21.4 15.5 63.1

Total 12.1 11.2 76.7

Q27. Has your workplace taken any steps to improve the proficiency of employees to enable them to

carry out the tasks involving ICT use?

Number of valid responses: 6,776 N=12,268,023

Source: European Digital Skills Survey (weighted values)

4.2

14.4

20.0

35.2

12.2

6.0

14.9

7.3

11.4

17.6

48.2

23.3

18.8

17.6

85.6

73.8

59.2

15.6

63.2

73.7

65.0

2.8

0.3

3.3

1.0

1.3

1.5

2.6

DE

FI

UK

PT

SE

SK

Total

Yes, a major impact Yes, a minor impact No Don't know

185

Table A5.7 - Logistic regression: probability that workplace has undertaken steps to tackle digital skills

gaps (Odds ratios)

N. obs 4466

normalized weight used yes

model estimates statistics Only intercept Intercept and

covariates

AIC 5097.372 4799.308

SC 5103.631 4968.314

-2 Log L 5095.372 4745.308

Test Chi-square DF Pr > ChiSQ

Likelihood ratio 350.0636 26 <.0001

Score 378.2455 26 <.0001

Wald 333.2119 26 <.0001

Parameters B SE

Chi-square

(Ward) Pr > ChiSQ Odds Ratio

Intercept -1.4703 0.0904 264.801 <.0001

Workplace type

Only workplace (omitted)

Headquarters 0.2063 0.1285 2.5764 0.1085 1.229

Subsidiary_site 0.1602 0.2001 0.6409 0.4234 1.174

Workplace size

size < 10 (omitted)

size10_49 1.0864 0.1009 115.8162 <.0001 2.964

size50_249 1.1978 0.1963 37.2138 <.0001 3.313

size250 1.3134 0.377 12.1359 0.0005 3.719

Sector

sectorA (omitted)

sectorCD -0.2521 0.1584 2.5334 0.1115 0.777

sectorF 0.0505 0.1441 0.123 0.7258 1.052

sectorGHI 0.1057 0.1153 0.8408 0.3592 1.111

sectorJMN 0.1934 0.1317 2.1554 0.1421 1.213

sectorPQ 0.1069 0.1725 0.3837 0.5357 1.113

Female rate

fem_rate<26 (omitted)

fem_rate_26_50 0.1258 0.1 1.5802 0.2087 1.134

fem_rate_51_75 0.2809 0.1506 3.4776 0.0622 1.324

fem_rate_75 0.3793 0.13 8.5131 0.0035 1.461

University rate

univ_rate<26 (omitted)

univ_rate_26_50 0.0976 0.1204 0.6574 0.4175 1.103

univ_rate_51_75 0.2626 0.1881 1.9481 0.1628 1.3

univ_rate_75 -0.0818 0.1374 0.3549 0.5514 0.921

Young rate

young_rate<26

(omitted)

young_rate_26_50 -0.2036 0.1148 3.1415 0.0763 0.816

young_rate_51_75 -0.5244 0.2398 4.7817 0.0288 0.592

young_rate_75 -0.5837 0.2334 6.2533 0.0124 0.558

Old rate

old_rate<26 (omitted)

old_rate_26_50 -0.3545 0.1044 11.5346 0.0007 0.702

old_rate_51_75 -0.1467 0.2079 0.4982 0.4803 0.864

old_rate_75 -0.01 0.1352 0.0055 0.9408 0.99

Ownership (q9) private (omitted)

public 0.0353 0.1651 0.0458 0.8305 1.036

Markets (q11)

Local_market (omitted)

Regional_market 0.0674 0.0913 0.5441 0.4607 1.07

National_market -0.1849 0.1098 2.8381 0.0921 0.831

International_market 0.8358 0.1178 50.3647 <.0001 2.307

* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; P,Q: Education and human health and social work activities. Q27. Has your workplace taken any steps to improve the proficiency of employees to enable them to carry out the tasks involving ICT use? Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Note: workplaces with fully proficient workers (e.g. without digital skills gaps) are excluded from the analysis Source: Elaboration on European Digital Skills Survey (weighted values)

186

Table A5.8 - Workplaces reporting having taken action to tackle digital skill gaps by type of

action, size and sector, EU28 (% of workplaces with digital skill gaps which undertook actions)

On t

he job t

rain

ing

Exte

rnal tr

ain

ing

Changin

g w

ork

ing p

ractices

Reallocating t

asks

Recru

itin

g n

ew

sta

ff

Hir

ing t

em

pora

ry s

taff

Outs

ourc

ing o

f ta

sks

Secondm

ent

of em

plo

yees

Oth

er

Siz

e

2 to 9 79.7 51.7 45.6 44.4 32.3 21.8 28.5 10.2 11.2

10 to 49 90.2 68.5 53.5 52.0 47.5 29.1 30.3 17.5 11.3

50 to 249 95.8 71.4 55.6 50.0 51.1 30.1 30.8 27.6 11.2

250 + 99.9 63.6 50.6 58.7 88.3 18.2 35.0 11.8 5.6

Secto

r

A. Agriculture 86.9 85.9 85.2 2.3 72.7 83.8 84.0 1.2 1.1

CD.

Manufacturing

and utilities

89.4 63.6 50.8 48.6 50.0 36.1 38.3 16.4 13.4

F. Construction 80.1 56.9 46.7 46.2 40.4 27.5 30.4 19.4 9.8

GHI. Commerce,

transport,

accommodation

and food service

82.6 51.6 44.8 43.1 34.8 19.7 27.3 11.0 11.7

JMN. Information

and

communication;

Professional, scientific and

technical

activities;

Administrative

services

81.4 60.6 52.5 52.2 38.3 28.4 29.3 13.9 12.2

PQ. Education and

human health 93.0 69.9 53.2 50.9 39.9 17.8 24.9 15.5 6.6

Total 84.4 58.3 48.8 47.2 39.0 24.6 29.3 13.8 11.2

Q28. Which of the following steps is your workplace taking to overcome the fact that some of its employees

are not fully proficient in carrying out tasks involving ICT use? (Please select all that apply)

Number of valid responses: 1,486 N=1,476,489

Source: European Digital Skills Survey (weighted values)

187

Table A5.9 - Workplaces reporting difficulties when taking action to tackle digital skill gaps by

type of difficulty encountered, EU28 (%)

Excessiv

e c

ost

of tr

ain

ing

Vacancie

s s

tay o

pen f

or

a long t

ime

Vacancie

s a

re n

ot

filled d

ue t

o lack o

f skills

Modific

ations t

o w

ork

org

anis

ation a

re n

ot

possib

le

Excessiv

e c

ost

of hir

ing t

em

pora

ry s

taff

Excessiv

e c

ost

of

outs

ourc

ing o

f ta

sks

Siz

e

2 to 9 33.0 14.7 13.3 32.7 23.5 20.8

10 to 49 28.3 10.9 12.0 18.0 17.0 16.9

50 to 249 29.1 9.3 13.2 11.2 15.0 11.6

250 + 7.4 5.4 37.1 2.6 2.7 3.1

Secto

r

A. Agriculture 96.6 13.0 12.7 25.3 25.3 25.7

CD. Manufacturing and utilities 28.5 11.4 9.7 22.1 16.4 14.2

F. Construction 47.2 22.4 20.7 26.3 28.7 22.6

GHI. Commerce, transport, accommodation and

food service 25.6 10.9 13.7 30.6 19.7 21.5

JMN. Information and communication;

Professional, scientific and technical activities;

Administrative services

34.0 15.4 13.4 25.6 23.5 16.9

PQ. Education and human health 30.8 9.6 9.3 18.4 16.6 14.7

Total 31.0 13.1 13.2 26.2 20.7 18.7

Q29. Which – if any – of the following difficulties has your workplace encountered when taking steps to

overcome the fact that some employees are not fully proficient in carrying out tasks involving ICT use?

(Please select all that apply)Number of valid responses: 1,486

N=1,469,631

Source: European Digital Skills Survey (weighted values)

188

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2017 - 195 pages ISBN 978-92-79-67761-8

doi: 10.2759/498467

196

doi:10.2759/498467 ISBN 978-92-79-67761-8

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