OECD-EDU-2015-Adults Computers and Problem Solving-Whats the Problem

OECD Skills Studies
2015
OECDSkillsStudies Adults,ComputersandProblemSolving:What’stheProblem?
OECD Skills Studies
Adults, Computers and Problem Solving:
What’s the Problem?
The report provides an in-depth analysis of the results from the Survey of Adult Skills related to problem solving
in technology-rich environments, along with measures concerning the use of ICT and problem solving. The
Nordic countries and the Netherlands have the largest proportions of adults (around 40%) who score at the
higher levels in problem solving, while Ireland, Poland and the Slovak Republic have the smallest proportions
of adults (around 20%) who score at those levels. Variations in countries’ proficiency in problem solving using
ICT are found to reflect differences in access to the Internet and in the frequency with which adults use e-mail.
The report finds that problem-solving proficiency is strongly associated with both age and general cognitive
proficiency, even after taking other relevant factors into account. Proficiency in problem solving using ICT
is related to greater participation in the labour force, lower unemployment, and higher wages. By contrast,
a lack of computer experience has a substantial negative impact on labour market outcomes, even after
controlling for other factors. The discussion considers policies that promote ICT access and use, opportunities
for developing problem-solving skills in formal education and through lifelong learning, and the importance of
problem-solving proficiency in the context of e-government services.
Contents
Chapter 1. Problem solving in technology rich environments and the Survey of Adult Skills
Chapter 2. Proficiency in problem solving in technology-rich environments
Chapter 3. Differences within countries in proficiency in problem solving in technology-rich environments
Chapter 4. Proficiency in problem solving in technology-rich environments, the use of skills and labour
market outcomes
Chapter 5. Some pointers for policy
Related publications
• OECD Skills Outlook 2013: First Results from the Survey of Adult Skills
• The Survey of Adult Skills: Reader’s Companion
• Literacy, Numeracy and Problem Solving in Technology-Rich Environments:
Framework for the OECD Survey of Adult Skills
• OECD Skills Studies series
http://www.oecd-ilibrary.org/education/oecd-skills-studies_23078731
Related website
The Survey of Adult Skills (PIAAC)
http://www.oecd.org/site/piaac/
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Adults, Computers
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Adults, Computers and Problem Solving: What’s the Problem? © OECD 2015 3
Foreword
Information and communication technologies (ICT) permeate every aspect of our lives, from how we “talk” with friends
to how we participate in the political process. The volume of information now accessible at the click of a mouse or the
touch of a fingertip is overwhelming. But how skilled are we at using these technologies, and the information we can
collect through them, to solve problems we encounter in daily life, such as using e-mail to communicate with a friend
or knowing how to work with a spreadsheet?
Based on results from the 2012 Survey of Adult Skills, a product of the OECD Programme for the International Assessment
of Adult Competencies (PIAAC), this report reveals the extent to which today’s adults can and do use computers to solve
problems in their work and personal lives. The report shows that the ability to use computers is not only becoming an
essential skill, but proficiency in computer use has an impact on the likelihood of participating in the labour force and
on workers’ wages. It also shows that there are many adults in all countries that participated in the Survey of Adult Skills
who do not possess sufficient skills in managing information in digital environments and are not comfortable using
ICT to solve the kinds of problems that they are likely to encounter at work or in everyday life. These adults are at a
considerable disadvantage in 21st-century societies.
As this detailed examination makes clear, adults’ proficiency in problem solving using ICT includes both proficiency
in the cognitive skills needed to solve problems and the ability to use digital devices and functionality to access and
manage information. Governments need to ensure that all adults have access to digital technologies and networks, and
are given opportunities to develop their proficiency in using them, whether in formal education, on-the-job training, or
through lifelong learning activities. Opting out of this increasingly wired world is no longer a viable option.
Andreas Schleicher
Director
Directorate for Education and Skills

Acknowledgements
The Survey of Adult Skills, a product of the OECD Programme for the International Assessment of Adult Competencies
(PIAAC), was developed collaboratively by the participating countries, the OECD Secretariat, the European Commission
and an international consortium led by Educational Testing Service (ETS). This report was prepared by Ji Eun Chung
and Stuart Elliott, under the supervision of William Thorn, with assistance from Veronica Borg, Vanessa Denis and
François Keslair. Editorial assistance was provided by Marilyn Achiron and Célia Braga-Schich. Administrative assistance
was provided by Sabrina Leonarduzzi.
This document is one of a series of thematic reports prepared as part of the analytical work programme of the
PIAAC Board of Participating Countries jointly chaired by Dan McGrath (United States) and Patrick Bussière (Canada).

Table of Contents
Executive Summary�� 13
About The Survey of Adult Skills�� 15
Reader’s Guide�� 17
CHAPTER 1 Problem solving in technology-rich environments and the Survey
of Adult Skills�� 21
The importance of problem-solving skills�� 22
Problem solving using ICT �� 22
Living with ICT�� 23
Working with ICT�� 26
Using ICT to interact with public authorities�� 26
Challenges in working with ICT�� 27
CHAPTER 2 Proficiency in problem solving in technology-rich environments�� 29
Information on adults who lack basic ICT skills �� 31
Proficiency across countries�� 34
Differences in frequency of ICT use �� 34
Proficiency and ICT access and use�� 37
CHAPTER 3 Differences within countries in proficiency in problem solving
in technology-rich environments�� 43
Proficiency in problem solving in technology-rich environments, and computer experience,
related to various socio-demographic characteristics �� 44
• Differences related to age�� 44
• Differences related to educational attainment�� 47
• Differences related to adult education and training �� 48
• Differences related to gender�� 48
• Differences related to socio-economic status �� 49
• Differences related to immigrant and language background �� 49
• Differences related to ICT use �� 51
• Differences related to literacy proficiency�� 51
Differences in proficiency related to specific characteristics, after accounting for other variables �� 52
• Opportunities to develop skills �� 52
• Background characteristics �� 53
• ICT use�� 54
• Literacy proficiency�� 54
Differences in experience with computers related to specific characteristics, after accounting
for other variables �� 55

Table of contents
8 © OECD 2015 Adults, Computers and Problem Solving: What’s the Problem?
CHAPTER 4 Proficiency in problem solving in technology-rich environments,
the use of skills and labour market outcomes�� 57
A profile of workers’ skills in problem solving and using ICT �� 58
• Current and recent workers’ proficiency in problem solving in technology-rich environments�� 58
• Proficiency in problem solving in technology-rich environments related to occupation �� 58
• Frequency of ICT use at work�� 59
• Problem solving at work�� 61
• Adequacy of ICT skills for work�� 61
Relationships among adults’ problem-solving and ICT skills, frequency of ICT use
and various economic outcomes �� 63
• Relationship with labour force participation�� 63
• Relationship with unemployment�� 65
• Relationship with wages�� 66
Relationships among adults’ problem-solving and ICT skills, frequency of ICT use
and various economic outcomes, after accounting for other factors �� 69
• Relationships with labour force participation, after accounting for other factors �� 70
• Relationships with unemployment, after accounting for other factors �� 71
• Relationship with wages, after accounting for other factors �� 72
Relationship with labour productivity �� 74
The complex relationship between problem solving using ICT and labour market outcomes�� 74
CHAPTER 5 Some pointers for policy�� 79
Adults with low proficiency in problem solving using ICT�� 80
The importance of access to and use of ICT and problem-solving skills at work�� 80
• Increasing access to ICT �� 80
• Policies to encourage greater use of ICT and problem-solving skills�� 81
Developing proficiency in problem solving using ICT in formal education�� 81
E-government and proficiency in problem solving using ICT�� 82
High-performing countries�� 83
ANNEX A TABLES OF RESULTS�� 87
ANNEX B additional TABLES�� 139

Table of contents
BOXES
Box 1.1 Transformation in making travel reservations�� 24
Box 2.1 Adults who “opted out” of taking the computer-based assessment�� 32
Box 2.2 Sample task in problem solving in technology-rich environments�� 39
Box 3.1 Using odds ratios when comparing a group to a reference group�� 55
Box 5.1 Korea: The largest proportion of highly proficient young adults�� 83
Box 5.2 The Nordic Countries: High proficiency, particularly among older adults�� 84
FIGURES
Figure 1.1 Jobs involving routine tasks or solving unforeseen problems�� 23
Figure 1.2 Evolution of online purchases�� 24
Figure 1.3 Evolution of using the Internet to search or apply for a job�� 25
Figure 1.4 Using technology, by sector of work�� 26
Figure 1.5 Evolution of using the Internet to interact with public authorities�� 27
Figure 2.1 Pathways to completing the Survey of Adult Skills�� 32
Figure 2.a Percentage of adults who opted out of taking the computer-based assessment, by various characteristics�� 33
Figure 2.2 Proficiency in problem solving in technology-rich environments�� 35
Figure 2.3 Country comparison of proficiency in problem solving in technology-rich environments�� 36
Figure 2.4 Using information technologies in everyday life�� 36
Figure 2.5 Relationship between proficiency in problem solving in technology-rich environments and access to or use of ICT�� 37
Figure 2.6 Relationship between ICT use in the Survey of Adult Skills and in the Eurostat Community Survey�� 38
Figure 3.1 Differences in problem solving in technology-rich environments proficiency between various groups�� 45
Figure 3.2 Differences in computer experience between various groups�� 45
Figure 3.3 Problem-solving proficiency and computer experience, by age�� 46
Figure 3.4 Problem-solving proficiency and computer experience, by educational attainment�� 47
Figure 3.5 Problem-solving proficiency and computer experience, by gender�� 49
Figure 3.6 Problem-solving proficiency and computer experience, by immigrant and language status�� 50
Figure 3.7 Problem-solving proficiency and computer experience, by level of literacy proficiency�� 51
Figure 3.8 How problem-solving proficiency and lack of computer experience are affected by various characteristics�� 53
Figure 4.1 Problem-solving proficiency and computer experience, by employment status�� 59
Figure 4.2 Using information technologies at work�� 60
Figure 4.3 Problem-solving proficiency and computer experience, by frequency of complex problem solving�� 61
Figure 4.4 Workers who reported insufficient computer skills�� 62
Figure 4.5 Workers who reported insufficient computer skills, by the effect on employment�� 63
Figure 4.6 Labour force participation, by problem-solving proficiency�� 64
Figure 4.7 Labour force participation, by e-mail use in everyday life�� 65
Figure 4.8 Unemployment rate, by problem-solving proficiency�� 66
Figure 4.9 Unemployment rate, by e-mail use in everyday life�� 67
Figure 4.10 Wage premium, by problem-solving proficiency�� 67
Figure 4.11 Wage premium associated with e-mail use at work�� 68
Figure 4.12 Wage premium associated with regular use of complex problem-solving skills�� 69

Table of contents
Figure 4.13 Wage premium associated with reported employment difficulties due to lack of computer skills �� 70
Figure 4.14 How labour force participation is affected by problem-solving proficiency and lack of computer experience�� 71
Figure 4.15 How unemployment rates are affected by problem-solving proficiency and lack of computer experience�� 72
Figure 4.16 How wages are affected by problem-solving proficiency and lack of computer experience�� 73
Figure 4.17 Labour productivity and high performance in problem solving in technology-rich environments�� 75
Figure 4.18 Labour productivity and frequent use of e-mail�� 75
TABLES
Table A1.1 Percentage of workers aged 16-74 who are in jobs that require solving unforeseen problems or conducting routine tasks�� 89
Table A1.2 Percentage of 25-64 year-olds who made online purchases, 2005 and 2013�� 89
Table A1.3 Percentage of unemployed individuals aged 16-74 who used the Internet to look for a job or send a job application�� 90
Table A1.4 Percentage of workers reporting frequent use* of technology, by sector of work, EU 27 average�� 90
Table A1.5 Percentage of individuals aged 16-74 who used the Internet to interact with public authorities�� 91
Table A2.1 Tasks in the problem solving in technology-rich environments assessment�� 92
Table A2.2 Percentage of adults scoring at each proficiency level in problem solving in technology-rich environments�� 93
Table A2.3 Percentage of adults with high proficiency in problem solving in technology-rich environments�� 94
Table A2.4a Frequency of e-mail use in everyday life�� 95
Table A2.4b Frequency of Internet use to better understand issues related to everyday life (e.g. health, financial matters,
or environmental issues)�� 96
Table A2.4c Frequency of Internet use for conducting transactions (e.g. buying or selling products or services, or banking)�� 97
Table A2.4d Frequency of spreadsheet software use (e.g. Excel)�� 98
Table A2.4e Frequency of a word processor use (e.g. Word)�� 99
Table A2.5 Literacy proficiency, frequent e-mail use and access to the Internet at home�� 100
Table A3.1 Percentage differences between groups of adults who score at Level 2 or 3 in problem solving in technology-rich
environments, before and after accounting for various characteristics (country average)�� 101
Table A3.2 Percentage differences between various groups of adults who have no computer experience, before
and after accounting for various characteristics (country average)�� 103
Table A3.3 Percentage of adults who score at Level 2 or 3 in problem solving in technology-rich environments
or have no computer experience, by age�� 104
or have no computer experience, by educational attainment�� 106
or have no computer experience, by age and gender�� 107
or have no computer experience, by immigrant and language status�� 108
or have no computer experience, by level of literacy proficiency�� 109
Table A4.1 Percentage of adults scoring at Level 2 or 3 in problem solving in technology-rich environments
or have no computer experience, by employment status�� 110
Table A4.2a Frequency of e-mail use at work�� 111
Table A4.2b Frequency of Internet use to better understand issues related to work�� 112
Table A4.2c Frequency of Internet use for conducting transactions (e.g. buying or selling products or services, or banking) at work�� 113
Table A4.2d Frequency of spreadsheet software (e.g. Excel) use at work�� 114
Table A4.2e Frequency of a word processor (e.g. Word) use at work�� 115
Table A4.2f Use of a computer at work�� 116

Table of contents
Table A4.3 Percentage of workers scoring at Level 2 or 3 in problem solving in technology-rich environments
or having no computer experience, by frequency of complex problem solving�� 117
Table A4.4a Percentage of workers, by adequacy of reported computer skills to do their job well�� 118
Table A4.4b Percentage of workers by adequacy of reported computer skills affecting the chances of getting
a job, promotion or pay raise�� 119
Table A4.5 Percentage of workers who reported that their lack of computer skills either have or have not affected
their chances of getting a job, promotion or pay raise�� 120
Table A4.6 Labour force participation rate, by proficiency in problem solving in technology-rich environments
among adults aged 25-65�� 121
Table A4.7 Labour force participation rate, by frequency of e-mail use in everyday life among adults aged 25-65�� 122
Table A4.8 Employment and unemployment rates, by proficiency in problem solving in technology-rich environments
among adults aged 25-65�� 123
Table A4.9 Employment and unemployment rates, by frequency of e-mail use in everyday life among adults aged 25-65�� 125
Table A4.10 Mean hourly wage, by proficiency in problem solving in technology-rich environments�� 126
Table A4.11 Mean hourly wage, by frequency of e-mail use at work�� 127
Table A4.12 Mean hourly wage, by frequency of complex problem solving�� 128
Table A4.13 Mean hourly wage and wage premium, by adequacy of computer skills affecting the chances of getting a job,
promotion or pay raise�� 129
Table A4.14 Differences in the rate of labour force participation between various groups after accounting for various characteristics�� 130
Table A4.15 Differences in the rate of unemployment between various groups after accounting for various characteristics�� 132
Table A4.16 Percentage differences in wages between various groups, before and after accounting for various characteristics�� 134
Table B1.1 Percentage of households with access to a computer at home (including PC, portable, handheld), 2000 to 2011�� 141
Table B1.2 Percentage of households with access to the Internet, 2000-2011�� 142
Table B1.3 Percentage of individuals aged 16-74 using any handheld device to access the Internet�� 143
Table B1.4 Percentage of Individuals using the Internet in middle income and developing countries�� 143
Table B1.5 Percentage of individuals aged 16-74 using online banking�� 144
Table B1.6 Percentage of individuals aged 16-74 using the Internet for sending and/or receiving e-mails �� 144
Table B1.7 Percentage of enterprises (with at least 10 employees) sending and/or receiving e-invoices �� 145
Table B2.1 Percentage of adults who opted out of taking the computer-based assessment by various characteristics�� 146
Table B2.2 Percentage of individuals aged 16-74 using the Internet for seeking health-related information �� 149
Table B3.1 Likelihood of adults scoring at Level 2 or 3 in problem solving in technology-rich environments,
by socio-demographic characteristics (Version 1)�� 150
by socio-demographic characteristics and ICT use (Version 2)�� 153
by socio-demographic characteristics, e-mail use and cognitive skills (Version 3)�� 156
Table B3.4 Likelihood of adults having no computer experience, by socio-demographic characteristics (Version 1)�� 160
Table B3.5 Likelihood of adults having no computer experience, by socio-demographic characteristics and cognitive skills (Version 3)�� 163
Table B3.6 Percentage of adults scoring at Level 2 or 3 in problem solving in technology-rich environments
or have no computer experience, by participation in adult education and training (formal and non-formal)�� 167
Table B3.7 Percentage of adults scoringe at Level 2 or 3 in problem solving in technology-rich environments
or have no computer experience, by parents’ educational attainment�� 168
or have no computer experience, by frequency of e-mail use�� 169
or having no computer experience, by occupation type�� 170
Table B4.2 Frequency of e-mail use at work and in everyday life�� 171
Table B4.3 Frequency of Internet use to better understand issues related to work and to everyday life �� 172

Table of contents
Table B4.4 Frequency of Internet use for conducting transactions (e.g. buying or selling products or services, or banking) at work
and in everyday life �� 173
Table B4.5 Frequency of spreadsheet software use (e.g. Excel) at work and in everyday life�� 174
Table B4.6 Frequency of a word processor use (e.g. Word) at work and in everyday life�� 175
Table B4.7 Percentage of workers, by frequency of complex problem solving�� 176
Table B4.8 Percentage of workers who reported lack of computer skills to do their job well, by age�� 177
Table B4.9 Percentage of workers whose lack of computer skills have affected their chances of getting
a job, promotion or pay raise, by age�� 178
Table B4.10 Percentage of workers who reported that they lack the computer skills to do the job well, by proficiency
in problem solving in technology-rich environments�� 179
Table B4.11 Percentage of workers who reported that their lack of computer skills has affected the chances of getting
a job, promotion or pay raise, by proficiency in problem solving in technology-rich environments�� 180
Table B4.12 Likelihood of participating in the labour force, by proficiency in problem solving in technology-rich environments
and use of e-mail in everyday life�� 181
Table B4.13 Likelihood of being unemployed, by proficiency in problem solving in technology-rich environments
and e-mail use in everyday life�� 183
Table B4.14 Percentage of adults aged 16-65 who worked during previous five years, by type of occupation�� 185
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Executive Summary
Problem solving is an important part of work and daily life. The labour market now places a premium on higher-
order cognitive skills that involve processing, analysing and communicating information. Meanwhile, citizens are daily
confronted with a plethora of choices concerning such important matters as retirement planning and saving, health care,
and schools for their children that require managing and evaluating multiple and competing sources of information.
In addition, the widespread diffusion of information and communication technologies (ICT) has transformed ways of
working, learning and interacting. As a result, the capacity to manage information and solve problems using digital
devices, applications and networks has become essential for life in the 21st century.
To understand how well-equipped adults are to manage information in digital environments, the Survey of Adult Skills,
a product of the OECD Programme for the International Assessment of Adult Competencies (PIAAC), includes an
assessment of problem solving in technology-rich environments. This assessment measures the ability of adults to solve
the types of problems they commonly face as ICT users in modern societies. The assessment includes problem-solving
tasks that require the use of computer applications, such as e-mail, spreadsheets, word-processing applications and
websites, that adults often encounter in daily life. The survey also collects information on the frequency with which
adults use different types of ICT applications, both at work and in their daily lives.
One in three adults is highly proficient in using ICT, on average, although
results vary across countries
• Across the OECD countries that participated in the survey, one-third of adults score at the highest levels on the
proficiency scale (Level 2 or 3). These adults can solve problems that require the co-ordinated use of several different
applications, can evaluate the results of web searches, and can respond to occasional unexpected outcomes.
• The Nordic countries and the Netherlands have the largest proportions of adults (around 40%) who score at the
highest levels of proficiency. In contrast, Ireland, Poland and the Slovak Republic have the smallest proportions of
adults (around 20%) who score at these levels.
Having good literacy or numeracy skills and being younger have the strongest
relationships to high proficiency in problem solving in technology-rich
environments
• On average, adults with good literacy or numeracy skills as well as younger adults (16-24 years old) have better skills
in problem solving in technology-rich environments. Having tertiary qualifications and being a regular user of ICT are
also factors that are strongly and positively related to proficiency in problem solving using ICT, even after accounting
for other factors. Being an immigrant and speaking a language other than the test language as a child have no effect
on proficiency after other factors are accounted for.
• Younger adults and those with tertiary qualifications are more likely to have some computer experience. However,
after other factors are taken into account, the likelihood of having experience with computers is unrelated to literacy
proficiency.

Executive Summary
Proficiency in problem-solving in technology-rich environments is important
for work
• Adults who score at the highest levels of proficiency in problem solving in technology-rich environments are more
likely than other adults to be in the labour force and to have higher wages, although proficiency in literacy and
numeracy, as well as frequency of ICT use also play a large role in explaining these outcomes. As the nature of work
continues to evolve, it is likely that the rewards for proficiency in this domain will continue to increase.
The proportion of adults who use ICT frequently at and outside of work
varies considerably across countries
• Across participating OECD countries, two out of three adults use e-mail and the Internet in their everyday lives,
outside of work, at least once a month. Almost half of the workforce uses e-mail daily at work and almost half use
word-processing programmes at least once a month. These regular users of ICT thus have opportunities to continue to
develop their skills in problem solving in technology-rich environments.
• Differences in the degree of Internet access and ICT use explain much of the variation in proficiency in problem
solving in technology-rich environments across countries. The Netherlands and the Nordic countries show the most
frequent ICT use, with over 80% of adults using e-mail at least once a month and over 70% using the Internet to
understand issues with the same frequency. By contrast, in Japan less than 50% of adults use e-mail or use the Internet
to understand issues at least once a month, and less than 30% use the Internet to conduct transactions at least once
a month. Korea, Poland and the Slovak Republic also show infrequent use of ICT: around 60% of adults or less use
e-mail and the Internet to understand issues at least once a month and less than 40% of adults in Poland and the
Slovak Republic use the Internet to conduct transactions at least once a month.
Across all participating countries, many adults still have no experience with
computers at all
• Across participating OECD countries, 8% of adults had no computer experience prior to their participation in the
survey. The percentages range from less than 3% of 16-65 year-olds in the Netherlands, Norway and Sweden to around
15% or higher in Italy, Korea, Poland, the Slovak Republic and Spain. In addition, 5% of adults have such limited
computer experience that they lack basic computer skills, such as the ability to highlight text.
• Governments should consider their population’s proficiency in solving problems using ICT when they provide access
to government services through e-mail and the Internet. To encourage widespread use of such “e-government”
services, governments can provide assistance to adults with low proficiency in problem solving in technology-rich
environments, and ensure that websites intended for the general public are user-friendly.
• Government policies can also encourage those adults who have limited proficiency in ICT skills to participate in adult
education and training programmes that aim to help adults to develop these skills.

About The Survey of Adult Skills
The Survey of Adult Skills, a product of the OECD Programme for the International Assessment of Adult Competencies
(PIAAC), assesses the proficiency of adults aged 16-65 in literacy, numeracy and problem solving in technology-rich
environments. These three domains are key information-processing competencies that are relevant to adults in many
social contexts and work situations. They are necessary for fully integrating and participating in the labour market,
education and training, and social and civic life.
The Survey of Adult Skills also collects information about a number of factors in each respondent’s background and
context. This information includes participation in activities that use the competencies assessed in the three domains,
such as the frequency of reading different kinds of material or using different types of information and communication
technologies (ICT). The survey includes questions about the use of various generic skills at work, such as collaborating
with others and organising one’s time. Respondents are also asked whether their skills and qualifications match their
work requirements and whether they have autonomy with respect to key aspects of their work.
The first survey was conducted in 2011-2012 in 24 countries and sub-national regions: 22 OECD member countries
or regions – Australia, Austria, Belgium (Flanders), Canada, the Czech Republic, Denmark, Estonia, Finland, France,
Germany, Ireland, Italy, Japan, Korea, the Netherlands, Norway, Poland, the Slovak Republic, Spain, Sweden, the
United Kingdom (England and Northern Ireland), and the United States; and two partner countries – Cyprus* and
the Russian Federation**. Around 166 000 adults were surveyed during this first cycle. Additional countries will be
participating in the survey in the coming years.
The survey is administered under the supervision of trained interviewers, most often in the respondent’s home. It starts
with a background questionnaire, delivered in Computer-Aided Personal Interview format by the interviewer, and
typically takes 30-45 minutes to complete. The assessment of the domain competencies is conducted either on a laptop
computer or by completing a paper version, depending on the respondent’s computer skills. The respondents usually
take 50 minutes to complete the assessments, but there is no time limit. To reduce the time required for the survey,
respondents are assessed in only one or two of the three domains, not in all of them. Respondents with very low literacy
skills take an alternate assessment of basic reading skills.
The problem-solving and basic-reading assessments are optional for countries; in the first cycle, several countries
declined to participate in those parts of the survey (Cyprus*, France, Italy and Spain). The survey is given in the official
language or languages of each participating country, sometimes also including a widely-spoken minority or regional
language. Sample sizes depend on the number of cognitive domains assessed, the number of languages used, and country
decisions about whether to increase the sample sizes to allow more precise estimates for individual geographic regions
or population subgroups. In the first cycle of the survey, the samples ranged from about 4 500 to about 27 300 adults.
During the process of scoring the assessment, a difficulty score is assigned to each task, based on the proportion of
respondents who complete it successfully. These scores are represented on a 500-point scale. Respondents are placed
on the same 500-point scale, using the information about the number and difficulty of the questions they answer
correctly. At each point on the scale, an individual with a proficiency score of that particular value has a 67% chance

About The Survey of Adult Skills
of successfully completing test items located at that point. This individual will also be able to complete more difficult
items with a lower probability of success and easier items with a greater chance of success. To help interpret the results,
the reporting scales are divided into four proficiency levels (Below Level 1 through Level 3) in the problem solving in
technology-rich environments domain. In addition to the four proficiency levels, there are three additional categories (no
computer experience, failed ICT core, and opted out) for those adults who were not able to demonstrate their proficiency
in this domain due to lack of basic computer skills necessary to sit the assessment.
* Notes regarding Cyprus
Note by Turkey: The information in this document with reference to “Cyprus” relates to the southern part of the Island. There is
no single authority representing both Turkish and Greek Cypriot people on the Island. Turkey recognises the Turkish Republic of
Northern Cyprus (TRNC). Until a lasting and equitable solution is found within the context of the United Nations, Turkey shall
preserve its position concerning the “Cyprus issue”.
Note by all the European Union Member States of the OECD and the European Union: The Republic of Cyprus is recognised
by all members of the United Nations with the exception of Turkey. The information in this document relates to the area under
the effective control of the Government of the Republic of Cyprus.
** A note regarding the Russian Federation
Readers should note that the sample for the Russian Federation does not include the population of the Moscow municipal area.
The data published, therefore, do not represent the entire resident population aged 16-65 in Russia but rather the population of
Russia excluding the population residing in the Moscow municipal area.
More detailed information re garding the data from the Russian Federation as well as that of other countries can be found in the
Technical Report of the Survey of Adult Skills (OECD, 2014).

Reader’s Guide
Data underlying the figures
Detailed data tables corresponding to the figures presented in the main body of the report can be found in Annex A.
These figures and tables share a common reference number and are numbered according to the corresponding
chapters.
Annex B includes other detailed data tables that correspond either to figures included in boxes or to citations in
the main body of the report, but for which no figure was provided.
Unless otherwise stated, the population underlying each of the figures and tables covers adults aged 16-65.
Web package
A comprehensive set of tables (and figures, when available) used in the report can be found on the web at
www.oecd.org/site/piaac/. The package consists of Excel workbooks that can be viewed and downloaded by
chapter.
StatLinks
A StatLink url address is provided under each figure and table. Readers using the pdf version of the report
can simply click on the relevant StatLink url to either open or download an Excel® workbook containing the
corresponding figures and tables. Readers of the print version can access the Excel® workbook by typing the
StatLink address in their Internet browser.
Calculating cross-country averages (means)
Most figures and tables presented in this report and in the web package include a cross-country average in addition
to values for individual countries or sub-national entities. The average in each figure or table corresponds to
the arithmetic mean of the respective estimates for each of the OECD member countries that participated in the
assessment of problem solving in technology-rich environments. For England (UK) and Northern Ireland (UK),
the weighted average of the two separate entities is used for the overall cross-country average. OECD countries
that did not participate in this assessment domain (France, Italy and Spain) are not included in the “Average”
presented in the figures and are not discussed in the main text; however, averages including these countries can
be found associated with the term “Average-22” in Annex A tables whenever the data are available. The results
for partner countries Cyprus* and the Russian Federation** are also not included in the cross-country averages
presented in any of the figures or tables.
Standard error (s.e.)
The statistical estimates presented in this report are based on samples of adults, rather than values that could be
calculated if every person in the target population in every country had answered every question. Therefore, each
estimate has a degree of uncertainty associated with sampling and measurement error, which can be expressed
as a standard error. The use of confidence intervals provides a way to make inferences about the population
means and proportions in a manner that reflects the uncertainty associated with the sample estimates. In this
report, confidence intervals are stated at 95% confidence level. In other words, the result for the corresponding
population would lie within the confidence interval in 95 out of 100 replications of the measurement on different
samples drawn from the same population.
Statistical significance
Differences considered to be statistically significant from either zero or between estimates are based on the 5% level
of significance, unless otherwise stated. In the figures, statistically significant estimates are denoted in a darker tone.

Reader’s Guide
Symbols for missing data and abbreviations
a Data are not applicable because the category does not apply.
c There are too few observations or no observation to provide reliable estimates (i.e. there are fewer than
30 individuals). Also denotes unstable odds ratios which may occur when probabilities are very close
to 0 or 1.
m Data are not available. The data are not submitted by the country or were collected but subsequently
removed from the publication for technical reasons.
w Data has been withdrawn at the request of the country concerned.
S.E. Standard Error
S.D. Standard Deviation
Score dif. Score-point difference between x and y
% dif. Difference in percentage points between x and y
GDP Gross Domestic Product
ISCED International Standard Classification of Education
ISCO International Standard Classification of Occupations
Country coverage
This publication features data on 20 OECD countries: Australia, Austria, Canada, the Czech Republic,
Denmark, Estonia, Finland, France, Germany, Ireland, Italy, Japan, Korea, the Netherlands, Norway, Poland, the
Slovak Republic, Spain, Sweden and the United States, and three OECD sub-national entities: Flanders (Belgium),
England (United Kingdom), and Northern Ireland (United Kingdom). In addition, two partner countries participated
in the survey: Cyprus* and the Russian Federation**.
Data estimates for England (UK) and Northern Ireland (UK) are presented separately as well as combined in the
data tables, but only as combined (i.e. England/N. Ireland [UK]) in the figures.
Data estimates for France, Italy and Spain are not included in this report as these countries did not participate
in the assessment of problem solving in technology-rich environments. However, ICT use-related data for these
countries, collected through the background questionnaire, and the results for the ICT core test are both available
in tables in Annex A.
The Survey of Adult Skills is conducted in nine additional countries: Chile, Greece, Indonesia, Israel, Lithuania,
New Zealand, Singapore, Slovenia and Turkey. Data collection took place in 2014 and the results will be released
in 2016. A third round of the survey, with additional countries, is planned for the 2015-19 period.
Rounding
Data estimates, including mean scores, proportions, odds ratios and standard errors, are generally rounded to one
decimal place. Therefore, even if the value (0.0) is shown for standard errors, this does necessarily imply that the
standard error is zero, but that it is smaller than 0.05.
Further documentation and resources
The details of the technical standards guiding the design and implementation of the Survey of Adult Skills (PIAAC)
can be found at www.oecd.org/site/piaac/. The first results from the Survey of Adult Skills can be found in the
report OECD Skills Outlook 2013: First Results from the Survey of Adult Skills (OECD, 2013a). Information regarding
the design, methodology and implementation of the Survey of Adult Skills can be found in summary form in the
Reader’s Companion to the survey (OECD, 2013b) and, in detail, in the Technical Report of the Survey of Adult
Skills (OECD, 2014) (www.oecd.org/site/piaac/).

Reader’s Guide
*Notes regarding Cyprus
Readers should note the following information provided by Turkey and by the Member States of the OECD and the
European Union regarding the status of Cyprus:
A. Note by Turkey
The information in this document with reference to “Cyprus” relates to the southern part of the Island. There
is no single authority representing both Turkish and Greek Cypriot people on the Island. Turkey recognizes the
Turkish Republic of Northern Cyprus (TRNC). Until a lasting and equitable solution is found within the context
of United Nations, Turkey shall preserve its position concerning the “Cyprus issue”.
B. Note by all the European Union Member States of the OECD and the European Union
The Republic of Cyprus is recognized by all members of the United Nations with the exception of Turkey. The
information in this document relates to the area under the effective control of the Government of the Republic
of Cyprus.
Throughout this report, including the main body, boxes, and annexes, references to Cyprus are accompanied by a
symbol pointing to a footnote that refers readers to notes A and B above.
**A note regarding the Russian Federation
Readers should note that the sample for the Russian Federation does not include the population of the Moscow
municipal area. The data published, therefore, do not represent the entire resident population aged 16-65 in
Russia but rather the population of Russia excluding the population residing in the Moscow municipal area. More
detailed information regarding the data from the Russian Federation as well as that of other countries can be found
in the Technical Report of the Survey of Adult Skills (OECD, 2014).
References
OECD (2014), Technical Report of the Survey of Adult Skills, www.oecd.org/site/piaac/_Technical%20Report_17OCT13.pdf,
pre-publication copy.
OECD (2013a), OECD Skills Outlook 2013: First Results from the Survey of Adult Skills, OECD Publishing, Paris, http://dx.doi.
org/10.1787/9789264204256-en.
OECD (2013b), The Survey of Adult Skills: Reader’s Companion, OECD Publishing, Paris, http://dx.doi.org/10.1787/
9789264204027-en.

1
Problem solving in technology-rich
environments and the Survey
of Adult Skills
The ability to manage information and solve problems using digital
devices, applications and networks has become an essential 21st-century
skill. This chapter provides the rationale for assessing adults’ ability to
solve problems in technology-rich environments in the Survey of Adult
Skills.

1
PROBLEM SOLVING IN TECHNOLOGY-RICH ENVIRONMENTS AND THE SURVEY OF ADULT SKILLS
As the demand for non-routine, high-skilled jobs grows, and information and communications technologies (ICT)
permeate every aspect of life, the capacity to manage information and solve problems using digital technology and
communication tools has become crucial. In this context, policy makers need to be able to determine adults’ proficiency
in using these technologies to solve common problems in their work and daily lives. This chapter describes the rationale
for assessing adults’ proficiency in problem solving in technology-rich environments – that is, their capacity to solve
problems using ICT – in the Survey of Adult Skills, a product of the OECD Programme for the International Assessment
of Adult Competencies (PIAAC).
The Importance of Problem-Solving Skills
Problem solving is an integral part of work and daily life. Problems are often defined as situations in which people do
not immediately know what to do to achieve their goals due to obstacles or challenges of some kind (OECD, 2012).
To solve problems, individuals must thus be able to access and process information, evaluate the consequences of
possible choices, and learn from previous steps. Problem solving tends to be required whenever people encounter
a new situation. As our home and work environments frequently change, our routine behaviours quickly become
outmoded, and it often becomes necessary to find new ways to achieve our goals. Given the pace of economic and
social change in contemporary society, most adults now need higher levels of problem-solving skills than were called
for in the past.
A seminal set of studies has analysed information on the activities carried out in different occupations and found a
systematic shift over time in the mix of tasks carried out across the workforce in several countries. These studies show
that the proportion of jobs requiring relatively non-routine cognitive skills has been increasing for several decades in
the United States, Germany and Japan, while the proportion of jobs requiring relatively routine tasks and skills has
been decreasing (Autor, Levy and Murnane, 2003; Spitz-Oener, 2006; Ikenaga and Kambayashi, 2010). More recent
analyses have shown that the declines in the proportion of jobs requiring relatively routine tasks and skills continued
in the United States during the first decade of this century (Levy and Murnane, 2013). The growing importance of
non-routine cognitive skills in the workforce means that a growing share of the workforce will be called upon to find
solutions to unforeseen problems. Similar conclusions can be drawn from the European Working Conditions Survey
(Eurofound, 2012).
On average across the countries shown in Figure 1.1, more than 80% of adults reported that they work in jobs that
require solving unforeseen problems. In Denmark, the Netherlands, Norway and Sweden, the rate exceeds 90%. By
contrast, in Austria, the Netherlands and Norway, less than 30% of workers reported that they are in jobs that largely
involve routine tasks. Problem-solving skills are clearly becoming important at work while routine tasks are becoming
less prevalent.
Problem solving using ICT
As ICT hardware and software both change at a breakneck pace, users of these technologies must be able to adjust
quickly to new ICT devices or programs or to ICT devices or programs that now function differently than before. As a
result, ICT users regularly need to solve problems as they carry out tasks using these technologies both at work and at
home.
The importance of ICT in modern life is often described in terms of the diffusion of access to the technology itself.
On average across OECD countries in 2011, 77% of households had access to computers compared to 46% in 2000
(Table B1.1 in Annex B) and 75% had access to the Internet at home compared to 28% in 2000 (Table B1.2). In
Denmark, Iceland, Korea, Luxembourg, the Netherlands, Norway and Sweden, more than 90% of households had
access to the Internet (Table B1.2). Adults are also increasingly accessing the Internet using portable devices such
as laptops, netbooks, tablet computers or smart phones, in addition to traditional desktop computers. For example,
more than 50% of individuals in Denmark, Norway, Sweden and the United Kingdom used a handheld device to
access the Internet in 2012 (Table B1.3). Many middle-income and developing countries are a decade or two behind
OECD countries in the process of gaining access to these technologies, but recent trends suggest that many of these
countries will approach current OECD-levels of ICT access in a decade or so (Table B1.4). Chapter 5 discusses the
role of government policy in promoting access to ICT and the Internet, including providing computers and digital
networks in public institutions.

1
• Figure 1.1 •
Jobs involving routine tasks or solving unforeseen problems
Percentage of workers aged 16-74
Denmark
Poland
Belgium
Sweden
Spain
Denmark
Poland
Belgium
Sweden
Spain
Czech Republic
Austria
Netherlands
Germany
Czech Republic
Norway Norway
Estonia Estonia
Austria
France France
Finland Finland
Ireland Ireland
Slovak Republic Slovak Republic
Italy Italy
Netherlands
Germany
United Kingdom United Kingdom
0 10 20 30 40 50 60 70 80 90 100
Solving unforeseen problems Routine tasks
%
Countries are ranked in descending order of the percentage of individuals in jobs that require solving unforeseen problems.
Source: European Working Conditions Survey (2010). See Table A1.1.
1 2http://dx.doi.org/10.1787/888933231444
Living with ICT
The near-universal access to ICT devices and applications is, in turn, driving a transformation in the way that people in
OECD countries live. Figure 1.2 shows how using the Internet to buy goods increased from 2005 to 2013 in a number of
countries. Additional examples of trends in using ICT for everyday tasks – such as banking and exchanging e-mails – are
shown in Tables B1.5 and B1.6 in Annex B. These trends demonstrate how ICT has become an integral part of everyday
life for many adults in most OECD countries.
The proportion of adults using ICT for these tasks has increased dramatically – by 20 to 40 percentage points in most
countries – from 2005 to 2013. The vast majority of adults in the Nordic countries (Denmark, Finland, Norway and
Sweden) reported that they use ICT to carry out everyday tasks: more than 80% used Internet banking in 2014 (Table B1.5)
and more than 70% made online purchases in 2013 (Table A1.2). If these growth rates continue, many other OECD
countries will move towards these near-universal levels of ICT use within the next decade.
As a consequence of using ICT for everyday tasks, offline purchases and practices have been transformed. Box 1.1
discusses some of the innovations that have taken place over the past decade in the travel sector as a growing proportion
of adults in OECD countries obtain travel information and make reservations through the Internet.
In addition, more and more people are using the Internet to apply for jobs. As information is becoming increasingly
digitised and shared on line, most job openings are now posted on line and many employers accept applications only
through special online platforms. As a result, for many adults in OECD countries, the ability to use such platforms has
become a required skill for landing a job.

1
• Figure 1.2 •
Evolution of online purchases
Percentage of 25-64 year-olds, 2005 and 2013
Poland
Slovak Republic
Austria
Italy
Finland
Poland
Slovak Republic
Austria
Italy
Finland
France³
Netherlands
Estonia¹
Ireland
France³
Spain Spain
Czech Republic² Czech Republic²
Netherlands
Germany Germany
Norway Norway
Sweden Sweden
United Kingdom United Kingdom
Denmark Denmark
Estonia¹
Ireland
Belgium Belgium
0 10 20 30 40 50 60 70 80 90 100
2005 2013
%
1. Year of reference 2009 instead of 2005.
Note: Within the 12 months prior to the Eurostat Community Survey.
Countries are ranked in ascending order of the percentage of individuals who made purchases in the 12 months prior to the Community Survey on
ICT usage in households and by individuals.
Source: Eurostat, Community Survey on ICT usage in households and by individuals. See Table A1.2.
1 2http://dx.doi.org/10.1787/888933231457
Box 1.1 Transformation in making travel reservations
Information and communication technologies (ICT) have transformed the way we live. One of the more visible
changes is in the travel industry. Nowadays, it is hard to imagine booking travel without comparing flight prices
and hotel room rates on line. However, online flight bookings were not available outside airline terminals until the
mid-1970s.1 Only a few domestic airlines allowed licensed travel agents to access the reservation system at that
time (McKenney and Copeland, 1995).
Airlines and hotel companies realised that approaching consumers directly, through the Internet, could reduce their
fees to travel agents and Computer Reservation Systems operators. As a result, since 1997, many airlines and other
travel companies gradually started to sell airline tickets directly to travellers. Travel agencies also started to develop
their own travel websites with online flight booking options. For example, in 1996, CheapTickets was founded in
the United Kingdom, offering airfare-pricing comparisons and partnering with airlines to offer low Internet rates.
Microsoft launched the Expedia online travel booking site the same year. In the years since, many other online
travel agencies have emerged, including Orbitz, Opodo, Travelocity and Voyages-sncf (Hockenson, 2012).
Consumers no longer need to call or travel to an offline travel agency to make travel reservations but can easily go
on line and book their own travel. Since 2010, more travel arrangements are booked on line than off line, and in
2012, 60% of all travel reservations were made on line. In 2010, 79% of all hotel bookings were either booked on
line or influenced by the Internet (Mullin, 2013).
Consumer spending on online travel has grown rapidly in recent years, reflecting continued increases in total
travel spending and the growing portion of online bookings. In 2012, online travel sales reached USD 524 billion
globally. Online travel spending is growing by 17% per year (Rossini, 2013).
...

1
Various ICT, problem-solving, literacy and numeracy skills are required to book airline tickets, reserve hotel
rooms or purchase package tours. These travel transactions usually involve navigating through many different
sites, evaluating the information presented, clicking on boxes, making payments on line and checking booking
confirmations via e-mail. These activities are similar to the types of tasks included in the problem solving in
technology-rich environments assessment.
With the latest advances in technology, it has become easier to make shopping and travel reservations with
smartphones and other mobile devices. Consumers can receive travel alerts and suggestions, store their boarding
card on their smartphones, book their own seats, and check in on line using their smartphone. Some 30% of
individuals around the world reported that they use mobile apps to find hotel deals, and 29% of travellers have
used mobile apps to find cheaper flights (Rossini, 2013).
Note:
1. The online travel evolution-infographic available at www.staywyse.org/2012/07/02/the-online-travel-evolution-infographic/
[Accessed 1 March 2015].
Figure 1.3 shows the degree to which unemployed adults in Europe use the Internet to search or apply for jobs. As
the figure shows, there was a substantial increase in the use of the Internet for this purpose between 2005 and 2013.
During this eight-year period, Austria, Ireland, the Netherlands and Norway saw an increase of more than 40 percentage
points in the use of the Internet to search for jobs or send job applications. More than 80% of unemployed adults in the
Netherlands, Norway and Sweden searched for jobs on line or submitted job applications via the Internet. The Survey of
Adult Skills reflects this new reality by including a task in the problem solving in technology-rich environments domain
related to accessing and evaluating job-search information in a simulated web environment (see Annex Box 2.2).
• Figure 1.3 •
Evolution of using the Internet to search or apply for a job
Percentage of unemployed individuals aged 16-74, 2005 and 2013
Italy
Belgium¹
Denmark
Poland
France¹
Italy
Belgium¹
Denmark
Poland
France¹
United Kingdom²
Finland
Czech Republic
Spain²
United Kingdom²
Ireland Ireland
Finland
Austria Austria
Estonia¹ Estonia¹
Norway Norway
Netherlands Netherlands
Sweden Sweden
Czech Republic
Spain²
Germany¹ Germany¹
0 10 20 30 40 50 60 70 80 90 100
2005 2013
%
Countries are ranked in ascending order of the percentage of unemployed individuals who used the Internet to look for a job or sent a job application within
the three months prior to the Community Survey on ICT usage in households and by individuals.
Source: Eurostat, Community Survey on ICT usage in households and by individuals. See Table A1.3.
1 2http://dx.doi.org/10.1787/888933231461

1
Working with ICT
Digital technologies have also changed business and work practices. For example, as shown in Table B1.7 in Annex B,
many enterprises send and receive business invoices on line. ICT applications are transforming work in many industries,
and employees in many occupations must be able to use them.
Intensity in the use of ICT differs across different sectors of the economy. As shown in Figure 1.4, only about 15% of
workers employed in agriculture across European countries use ICT. By contrast, more than 90% of workers in the
financial sector use ICT frequently, as do more than 70% of workers in public administration/defence and education.
Many of the sectors with high levels of ICT use, such as financial services and health care, are also those that have
increased their share of employment over the past several decades (OECD, 2013). Therefore, having an adequate level
of ICT skills to handle various tasks at work is likely to become even more prized by employers in the future.
Using ICT to interact with public authorities
The increase in access to and use of ICT by individuals and businesses has been accompanied by an increase in the
online provision of public services across many OECD countries. As shown in Figure 1.5, between 2008 and 2013
there was a substantial increase in the percentage of adults interacting with public authorities through digital channels.
For example, over the past four years, Denmark saw an increase of 36 percentage points in the proportion of adults
interacting with public authorities through ICT.
• Figure 1.4 •
Using technology, by sector of work
Percentage of workers reporting frequent use of ICT*, EU27 average
Agriculture
Construction
Transport
Industry
Wholesale, retail, food and accommodation
Other services
Health
Public administration and defence
Education
0 10 20 30 40 50 60 70 80 90 100
No technologyMachineryICT and machineryICT
%
Financial services
* Use is considered frequent if the technology is used more than 75% of the time.
Sectors are ranked in ascending order of the percentage of workers who reported using ICT frequently at work.
Source: European Working Conditions Survey (2010). See Table A1.4.
1 2http://dx.doi.org/10.1787/888933231479
Public services provided on line are more convenient for users, which usually means that more people can access those
services, and the services are less costly to both users and providers. For these reasons, many countries are looking for
ways to provide more public services on line and are investing substantial resources in developing them. Of course,
online services often require the user to find and interpret information and, as later chapters of this report make clear,
many adults still do not have adequate skills for accessing such services. It is thus critical that governments ensure that
public services are equally accessible to those who do not yet have access to computers or who lack the skills to use
them. Chapter 5 discusses the issues related to adopting e-government services, including those to consider before
designing related policies.

1
Challenges in working with ICT
Working with ICT involves much more than providing access to the technologies themselves. The differences between
access and use are shown in the figures above, where the adoption of ICT hardware – computers, Internet connections,
and mobile subscriptions – is substantially larger than the adoption of ICT as the means of carrying out the various
tasks described in Figures 1.2, 1.3 and 1.5. There is ample literature on the diffusion of technology that examines the
complexity of fully integrating new methods and techniques into work and everyday life (Rogers, 2003). A number of
factors determine the pace and extent of diffusion, including not only the characteristics of the innovations themselves,
but also the ways that information about innovation is communicated and the obstacles encountered when incorporating
the innovations into current work practices and social systems.
Using ICT adds another layer of complexity for users who are more accustomed to performing tasks using more traditional
methods. For most adults in OECD countries, using a pencil and paper, calling someone on the telephone, or visiting a store
or office involves a set of skills that they have developed and perfected over a number of decades. These skills have become
almost automatic: they are applied appropriately with almost no conscious thought or effort. As a result, users of these older
techniques can focus on the details of the task they are trying to accomplish – what words to use, how to respond to a difficult
conversation, which products to buy – rather than on how to manipulate the physical equipment they use to complete the task.
• Figure 1.5 •
Evolution of using the Internet to interact with public authorities
Percentage of 16-74 year-olds, 2008 and 2013
Czech Republic
United Kingdom
Canada²
Italy
Germany
Czech Republic
United Kingdom
Canada²
Italy
Germany
Estonia
Belgium
Poland
Spain
Estonia
Australia¹ Australia¹
Belgium
New Zealand New Zealand
Austria Austria
France France
Finland Finland
Norway Norway
Poland
Spain
Ireland Ireland
0 10 20 30 40 50 60 70 80 90 100
2008 2013
%
Sweden Sweden
Netherlands Netherlands
Denmark Denmark
Countries are ranked in ascending order of the percentage of adults who used the Internet to interact with public authorities in 2013.
Note: Within the 12 months prior to the surveys, for private purposes. Derived variable on use of e-government services. Individuals used the Internet for
at least one of the following: to obtain services from public authorities’ websites; to download official forms; and/or to send completed forms. Data for
Canada and New Zealand refer only to obtaining services from public authorities’ wedsites but does not include other activities such as townlegding or
completing official forms.
Source: Eurostat, Community Survey on ICT usage in households and by individuals; OECD ICT database. See Table A1.5.
1 2http://dx.doi.org/10.1787/888933231480

1
By contrast, using ICT to accomplish the same tasks places an additional burden on users who are not yet proficient
in using these technologies. As a result, it often becomes more difficult to carry out the task – at least for some time –
because users must consciously learn how to make the technology function as they intend, in addition to figuring out
the substantive details of the task. Many adults who have only recently begun using ICT have had the frustrating – and
sometimes embarrassing – experience of accidentally deleting the draft of a sensitive document or accidentally sending
the draft of a sensitive e-mail too soon.
References
Autor, D.H., F. Levy and R.J. Murnane (2003), “The skill content of recent technological change: An empirical exploration”, The
Quarterly Journal of Economics, Vol. 118, pp. 1278-1333.
Eurofound (2012), Fifth European Working Conditions Survey, Publications Office of the European Union, Luxembourg.
Hockenson, L. (2012), The Evolution of Online Travel [infographic], Mashable, Social Travel Series, http://mashable.com/2012/02/21/
online-travel-infographic/.
Ikenaga, T. and R. Kambayashi (2010), “Long-term trends in the polarization of the Japanese labor market: The increase of non-routine
task input and its valuation in the labor market”, Hitotsubashi University Institute of Economic Research Working Paper.
Levy, F. and R.J. Murnane (2013), Dancing with Robots: Human Skills for Computerized Work, Third Way, http://content.thirdway.org/
publications/714/Dancing-With-Robots.pdf [accessed 16 May 2014].
McKenney, J. and D. Copeland (1995), Waves of Change: Business Evolution through Information Technology, Harvard Business School
Publishing, Boston.
Mullin, M. (2013), Online and Offline Travel Agents in the Age of Digital Travel, TourismLink, www.tourismlink.eu/2013/03/
onlineandoffline-travel-agents-in-the-age-of-digital-travel/.
org/10.1787/9789264204256-en.
OECD (2012), Literacy, Numeracy and Problem Solving in Technology-Rich Environments: Framework for the OECD Survey of Adult
Skills, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264128859-en.
Rogers, E. M. (2003), Diffusion of Innovations, Free Press, New York.
Rossini, A. (2013), “Sustained growth but tougher competition”, WTM Business 2013, pp. 88-89.
Spitz-Oener, A. (2006), “Technical change, job tasks, and rising educational demands: Looking outside the wage structure”, Journal of
Labor Economics, Vol. 24, pp. 235-270.

2
Proficiency in problem
solving in technology-rich
environments
This chapter describes the main features of the assessment of problem
solving in technology-rich environments included in the Survey of Adult
Skills. It also presents the results of the adult survey and information
on how frequently adults use ICT devices and applications in their daily
lives. The results show a close relationship, across countries, between
proficiency in problem solving in technology-rich environments and the
degree of access to and use of ICT.

2
PROFICIENCY IN PROBLEM SOLVING IN TECHNOLOGY-RICH ENVIRONMENTS
The domain of problem solving in technology-rich environments captures the intersection between the set of cognitive
capacities required to solve problems and the use of information and communication technologies (ICT). Proficiency in
this skill reflects the capacity to use ICT devices and applications to solve the types of problems adults commonly face
as ICT users in modern societies. The domain assesses adults’ ability to use “digital technology, communication tools,
and networks to acquire and evaluate information, communicate with others and perform practical tasks” (OECD 2012,
p. 47). In order to display proficiency in this domain, adults must have the basic computer skills needed to undertake an
assessment on a computer: the capacity to type, manipulate a mouse, drag and drop content, and highlight text.
While the definition of the domain encompasses the full range of digital devices, interfaces and applications, the
assessment of problem solving in technology-rich environments in the first cycle of the Survey of Adult Skills, a product
of the OECD Programme for the International Assessment of Adult Competencies (PIAAC) is restricted to an environment
involving computers and computer networks. The tasks in this first assessment involve “solv[ing] problems for personal,
work or civic purposes by setting up appropriate goals and plans, and accessing and making use of information through
computers and computer networks” (OECD 2012, p. 47).The tasks require respondents to access, interpret, and integrate
information from multiple sources in order to construct a solution to a problem.
Of the 24 participating countries and sub-national regions, Cyprus1, France, Italy and Spain did not participate in the
assessment of problem solving in technology-rich environments. Since a measure of proficiency in this domain is not
available for these countries, the text, figures and the averages focus on the results of countries that participated in this
domain. However, some information for these countries, relevant to this report, is available from other sections of the
survey, including information from the background questionnaire on computer experience and on the use of ICT devices
and applications, both at and outside of work, and information on adults’ basic level of ICT skills, as assessed through
the ICT core test. This information for these countries can be found in the tables in the Annex.
Key findings
• On average, 8% of adults indicate that they had no prior experience with computers.
• Across countries, an average of one in three adults performs at the higher levels of problem solving, ranging from
19% in Poland to 44% in Sweden.
• In the Nordic countries and the Netherlands, over 80% of adults use e-mail at least once a month and over 70%
use the Internet with similar frequency to understand issues and conduct transactions. By comparison, around
60% of adults or less in Korea, Poland and the Slovak Republic use e-mail and the Internet (to understand issues)
at least once a month, and less than 40% of adults in Poland and the Slovak Republic use the Internet to conduct
transactions at least once a month.
• Differences in the levels of Internet access and ICT use explain much of the variation in proficiency in problem
solving in technology-rich environments across countries.
Fourteen tasks, presented in two assessment modules, were used to assess adults’ proficiency in this skill. The results are
presented on a 500-point scale that is divided into four proficiency levels that describe the difficulty of the tasks and the
specific capabilities of the adults who can perform them. Table A2.1 in the Annex lists the 14 tasks in increasing order
of difficulty, clustered into proficiency Levels 1 through 3. The fourth proficiency level, Below Level 1, is used for those
adults who cannot reliably perform the tasks at Level 1.
Tasks below Level 1 have clear goals, few steps and familiar environments. Adults who score below Level 1 in proficiency
can successfully complete fewer than one in six Level 1 tasks. Adults at this level have passed the ICT core, which means
that they can use basic computer functions, such as typing, manipulating a mouse, dragging and dropping content, and
highlighting text.
At Level 1, adults can complete tasks in which the goal is explicitly stated and for which a small number of operations are
performed in a single familiar environment. The tasks that are rated at this level involve locating an item in a spreadsheet
and communicating the result by e-mail, using e-mail to send information to several people, and categorising e-mail
messages into existing folders.

2
At Level 2, adults can complete problems that have explicit criteria for success, a small number of applications, several
steps and operators, and occasional unexpected outcomes that need to be addressed. The tasks that are rated at this
level involve organising information in a spreadsheet, categorising e-mail messages into new folders, evaluating search
engine results according to a set of criteria, completing a multi-step consumer request using a website and e-mail, and
evaluating multiple websites to identify the most trustworthy site.
At Level 3, adults can complete tasks involving multiple applications, a large number of steps, occasional impasses,
and the discovery and use of ad hoc commands in a novel environment. The tasks that are rated at this level involve
evaluating search engine results with a set of criteria, solving a scheduling problem by combining information from an
Internet application and several e-mail messages, determining the proper folder destination for categorising a subset of
e-mail messages, and transforming information in an e-mail message into a spreadsheet and performing computations
with it.
Further information about the overall design and administration of the Survey of Adult Skills is provided on page 15 of
this report and in chapter 3 of The Survey of Adult Skills: Reader’s Companion (OECD, 2013b). A sample task that was
used during field testing is described in Box 2.2.
Information on adults who lack basic ICT skills
Some adults were not able to demonstrate their proficiency in problem solving in technology-rich environments because
they lacked the basic computer skills necessary to sit the assessment. Given its nature, the assessment must be delivered
on a computer. Unlike the assessments of literacy and numeracy, respondents could not complete the assessment using
a paper test booklet. Thus, estimates of the proficiency in this domain are available only for those adults who completed
the assessment on computer.
There are three main reasons why some respondents did not complete the assessment on computer and, thus, did not
have a score in problem solving using ICT. First, some adults indicated in the background questionnaire that they had
never used a computer. Second, among the adults who had used a computer, some did not pass the ICT core test,
which was designed to assess whether respondents had sufficient skill in the use of computers and computer networks
(including the ability to use a mouse, type, scroll through text, highlight text, use drag and drop functionality, and use
pull-down menus) to complete the assessment on a computer. Third, a number of respondents opted to complete the
assessment in its paper-based format rather than on a computer without first taking the ICT core test.
Opting out of the computer-based assessment may reflect either respondents’ lack of familiarity with computers, their
unwillingness to use a computer for an assessment, or different field work practices across countries. The technical
standards guiding the design and implementation of the survey (PIAAC, 2011) offered countries no guidance on the
procedure to be followed in the event that a respondent expressed a preference to complete the assessment using pencil
and paper without first taking the ICT core test. As a result, it is possible that practices in managing this situation varied
among countries and among interviewers within countries. The existence of the “opt-out” group (for more information
about this group, see Box 2.1) thus adds some uncertainty to both the estimates of the proportions of adults with very
poor computer skills (i.e. those who could not meet the minimum requirements for completing the test on computer)
and the proportion of adults at the different levels of proficiency in problem solving in technology-rich environments.
Thus, the Survey of Adult Skills provides two different pieces of information about the ability of adults to manage
information using ICT.The first is the proportion of adults who have or do not have sufficient familiarity with computers to
use them to perform information-processing tasks. The second is the level of proficiency in solving problems commonly
encountered in work and everyday life in a technology-rich world. The various pathways through the assessment and the
proportions of adults taking these pathways are presented in Figure 2.1.

2
• Figure 2.1 •
Pathways to completing the Survey of Adult Skills
Background questionnaire Missing
Missing
Computer-based assessment core
ICT test (stage 1)
Paper-based assessment core
4 literacy and 4 numeracy tasks
Full
paper-based
assessment
Literacy
(20 tasks)
Full
paper-based
assessment
Numeracy
(20 tasks)
Computer-based assessment core
3 literacy and 3 numeracy tasks (stage 2)
Numeracy
Stage 1 (9 tasks)
Stage 2 (11 tasks)
Literacy
Stage 1 (9 tasks)
Stage 2 (11 tasks)
Problem solving
in technology-rich
environments
Reading
components
Some computer experienceNo prior computer experience
Pass
Pass
Pass
Fail
Fail
Fail
“Opted out” of the
computer-based
assessment
Literacy
Stage 1 (9 tasks)
Stage 2 (11 tasks)
Numeracy
Stage 1 (9 tasks)
Stage 2 (11 tasks)
Problem solving
in technology-rich
environments
1 2http://dx.doi.org/10.1787/888933231498
Box 2.1 Adults who “opted out” of taking the computer-based assessment
Some respondents decided to take the paper-and-pencil version of the assessment rather than taking the computer-
based assessment on their own initiative. These individuals also did not take a simple test of their ability to use the
basic functionality required to take the full computer-based assessment (the ICT core test). Information about their
level of computer proficiency is therefore unknown, as is their ability to solve problems using ICT devices, since
this assessment was only computer-based. Nevertheless, a range of information collected through the background
questionnaire provides some indication about the characteristics of those who opted out of the computer-based
assessment, as well as information suggesting differences in field practices in certain countries related to opting out.
As shown in Figure “a” in Box 2.10 of the first international report (OECD, 2013a), respondents who opted out of
the computer-based assessment are more likely to be older (45+), have lower educational attainment, and work in
semi-skilled blue-collar or white-collar occupations, and they are less likely to use ICT in everyday life. This group
shares similar characteristics with the adults who failed the ICT core test, though they are even more likely to be
older and even less likely to use ICT in everyday life than the adults who failed the ICT core test. This suggests that
lack of familiarity with computers might have influenced their decision to take the assessment on paper, even if
they might have had the skills to take the computer-based assessment.
In some countries, the proportion of adults opting out of the computer-based assessment is substantially larger than it is
in other countries. As shown in Figure “a” below, more than 15% of adults opted out of the computer-based assessment
in Estonia, Ireland, Japan and Poland. In some of these countries, an unexpectedly large proportion of adults opted
out of the computer-based assessment from the subgroups of the population that, in other countries, generally have
low rates of opting out. This is particularly true in Poland, where 28% of adults who scored at Level 4 or 5 in literacy,
18% of adults who frequently use e-mail outside of work, 19% of adults with tertiary education, and 12% of young
adults opted out of the computer-based assessment. Ireland and Japan also show similar patterns. These results suggest
that in these countries, the field practices used to encourage adults to take the computer-based assessment may have
...

2
functioned differently than in other countries. As a result, the estimates of proficiency in solving problems in technology-
rich environments may be biased in these countries because some adults who could have taken the computer-based
assessment chose to take the paper-and-pencil version instead.
• Figure 2.a •
Percentage of adults who opted out of taking the computer-based assessment,
by various characteristics
Poland
Ireland
Japan
Estonia
Australia
Slovak Republic
Czech Republic
Austria
Finland
Norway
Denmark
United States
Canada
Germany
Sweden
Korea
Flanders (Belgium)
Netherlands
England/N. Ireland (UK)
Average
Russian Federation¹
Total
Youth aged
16-24
Tertiary
education
High use
of e-mail
(at least monthly)
Level 4/5
in literacy
0 10 20 30 0 10 20 30 0 10 20 30 0 10 20 30 0 10 20 30 %
1. See note at the end of this chapter.
Countries are ranked in descending order of the percentage of adults who opted out of taking the computer-based assessment.
Source: Survey of Adults Skills (PIAAC) (2012), Table B2.1 in Annex B.
1 2http://dx.doi.org/10.1787/888933231556

2
Proficiency across countries
Given the variation, across countries, in the proportion of adults who were able and willing to complete the assessment
in problem solving in technology-rich environments, results of the assessment are presented in terms of the proportions
of adults who perform at the four levels of proficiency rather than by mean scores. There is no information on proficiency
for three groups of adults: those who have no computer experience; those who have some computer experience but
“opted out” of taking the computer-based assessment; and those who agreed to complete the computer-based assessment
but failed the ICT core test that assesses basic computer skills.
Figure 2.2 provides an overview of adults’ proficiency in problem solving in technology-rich environments and the
proportion of adults without scores in this domain. Countries are ranked by the proportion of adults who are proficient
at Level 2 or 3. The Nordic countries and the Netherlands stand out as having the largest proportions of adults who
perform at these levels. Estonia, Ireland, Poland and the Slovak Republic have the smallest proportions. Even in the best-
performing countries, less than half of the adult population has skills at these levels.
Figure 2.3 shows the proportions of adults attaining Level 2 or 3 across countries, indicating where the differences
between countries are statistically significant. The proportion of adults at these levels is significantly larger in Sweden
than in any other country, and is significantly smaller in Poland than in any other country.
Nearly one in four adults across participating countries was not able or willing to take the assessment on a computer.
Even in the Nordic countries, one in seven adults did not take the assessment on a computer.
On average, 8% of adults indicate that they had no prior experience with computers. The Nordic countries, along
with Australia, Canada, the Netherlands, the United Kingdom and the United States, show the smallest proportions of
adults with no computer experience, ranging from 1% to 5%. Korea, Poland and the Slovak Republic have much larger
proportions of adults with no computer experience, ranging from 15.5% to 22%.
Some 4.9% of adults, on average, had poor computer skills and failed the ICT core test. Japan and Korea have the
largest proportions of the population in this category (11% and 9%, respectively), while the Czech Republic and the
Slovak Republic had the smallest proportion of adults who failed the ICT core test (both 2.2%).
On average, 9.9% of adults opted out and did not participate in the assessment of problem solving in technology-rich
environments. The opt-out rate was more than 14% in Estonia, Ireland, Japan and Poland and was less than 6% in
England/N. Ireland (UK), Flanders (Belgium), Korea, the Netherlands and Sweden.
Differences in frequency of ICT use
In addition to assessing proficiency in problem solving in technology-rich environments, the Survey of Adult Skills
collected a range of information about how adults use ICT devices and applications. Information was sought on the
frequency with which respondents used common applications (e-mail, the Internet, word processing and spreadsheets)
or engaged in certain activities, such as programming or participating in real-time interactions, such as chat sessions,
both at and outside of work. This chapter focuses on using ICT in daily life outside of work, covering both respondents
who work and those who do not.2 The analysis focuses on the use of e-mail, the Internet (either to understand issues or
to conduct transactions), spreadsheets and word processing because they are closely related to the types of tasks that are
included in the assessment of problem solving in technology-rich environments.
Figure 2.4 shows the average frequency with which adults use3 e-mail, the Internet (both to understand issues and
to conduct transactions), spreadsheets and word processing in their daily lives outside of work across participating
countries.4 Not surprisingly, the two most frequently occurring practices are using e-mail and using the Internet to
understand issues, with over two-thirds of respondents across participating OECD countries using these applications
at least once a month. On average, almost half of respondents across participating OECD countries reported they use
e-mail daily in their private life (Table A2.4a). Adults use these technologies less frequently for the other activities.
More than one in two reported they use the Internet to conduct transactions at least once a month. Roughly two in
five respondents use ICT for word processing in their daily lives at least once a month, and around one in five use
spreadsheets that often.
In some countries, monthly use of e-mail and the Internet is approaching universality. In the Nordic countries and the
Netherlands, over 80% of adults use e-mail at least once a month and over 70% use the Internet, to understand issues
and conduct transactions, with similar frequency (Tables A2.4a, b and c). In contrast, in Japan less than 50% of adults

2
use e-mail or use the Internet to understand issues, and less than 30% use the Internet to conduct transactions at least
once a month (Tables A2.4a, b and c). Korea, Poland and the Slovak Republic also show infrequent use: around 60%
of adults or less use e-mail and the Internet (to understand issues) at least once a month, and less than 40% of adults in
Poland and the Slovak Republic use the Internet to conduct transactions at least once a month (Tables A2.4a, b and c).
• Figure 2.2 •
Proficiency in problem solving in technology-rich environments
Sweden
Average
100 60 4080 20 0 20 40 8060 100 %
Opted out of the computer-based assessment
Failed ICT core
No computer experience
Level 3
Level 2
Level 1
Below level 1
Finland
Netherlands
Norway
Denmark
Australia
Canada
Germany
England/
N. Ireland (UK)
Japan
Czech Republic
Austria
United States
Korea
Estonia
Russian Federation1
Slovak Republic
Ireland
Poland
Flanders (Belgium)
Sweden
Average
Finland
Netherlands
Norway
Denmark
Australia
Canada
Germany
England/
N. Ireland (UK)
Japan
Czech Republic
Austria
United States
Korea
Estonia
Russian Federation1
Slovak Republic
Ireland
Poland
Flanders (Belgium)
Countries are ranked in descending order of the percentage of adults scoring at Level 2 or 3 in problem solving in technology-rich environments.
Source: Survey of Adult Skills (PIAAC) (2012), Table A2.2.
1 2http://dx.doi.org/10.1787/888933231500

2
• Figure 2.3 •
Country comparison of proficiency in problem solving in technology-rich environments
Percentage of adults scoring at Level 2 or 3
Significantly above the average
Not significantly different from the average
Significantly below the average
% Comparison country Countries whose % is NOT significantly different from the comparison country
44 Sweden
42 Finland Netherlands, Norway
42 Netherlands Finland, Norway
41 Norway Finland, Netherlands
39 Denmark Australia
38 Australia Canada, Denmark, Germany
37 Canada Australia, Germany, England/N. Ireland (UK)
36 Germany Australia, Canada, Japan, Flanders (Belgium), England/N. Ireland (UK)
35 England/N. Ireland (UK) Canada, Czech Republic, Germany, Japan, Flanders (Belgium)
35 Japan Austria, Czech Republic, Germany, Flanders (Belgium), England/N. Ireland (UK)
35 Flanders (Belgium) Austria, Czech Republic, Germany, Japan, England/N. Ireland (UK)
34 Average Austria, Czech Republic, Japan, Flanders (Belgium), England/N. Ireland (UK)
33 Czech Republic Austria, Japan, Korea, United States, Flanders (Belgium), England/N. Ireland (UK)
32 Austria Czech Republic, Japan, Korea, United States, Flanders (Belgium)
31 United States Austria, Czech Republic, Korea
30 Korea Austria, Czech Republic, United States, Russian Federation¹
28 Estonia Slovak Republic, Russian Federation¹
26 Russian Federation¹ Estonia, Ireland, Korea, Slovak Republic
26 Slovak Republic Estonia, Ireland, Russian Federation¹
25 Ireland Slovak Republic, Russian Federation¹
19 Poland
Countries are ranked in descending order of the percentage of adults scoring at Level 2 or 3 in problem solving in technology-rich environments.
1 2http://dx.doi.org/10.1787/888933231513
The estimates from the Survey of Adult Skills regarding ICT use for e-mail and Internet transactions are in line with data
from other sources, such as Eurostat. Figure 2.6 compares data from the survey and from Eurostat on the frequency with
which adults in the EU countries that participated in the Survey of Adult Skills use e-mail and the Internet to conduct
transactions.
• Figure 2.4 •
Using information technologies in everyday life
Percentage of users of ICT applications in everyday life at least once a month (country average*)
Use e-mail in everyday life
Use Internet to better understand issues
related to everyday life
Use Internet for conducting transactions
in everyday life
Use a word processor in everyday life
Use spreadsheet software in everyday life
0 20 40 60 80 100 %
* Country average: average of 19 participating OECD countries and entities.
Source: Survey of Adult Skills (PIAAC)(2012), Tables A2.4a, b, c, d and e.
1 2http://dx.doi.org/10.1787/888933231525

2
Proficiency and ICT access and use
While the assessment of problem solving in technology-rich environments measures more than the skill in using ICT
devices and applications, one would expect a close relationship between proficiency in this domain and access to
and use of ICT. Access to ICT devices and networks makes it possible for adults to use them, and frequent use of ICT is
likely to help in developing proficiency in the domain. At the same time, greater proficiency in these skills is likely to
encourage more frequent use of ICT, which, in turn, is likely to prompt investments to increase access. Chapter 5 of this
report offers some policy pointers to consider in increasing access to ICT for the general public.
Figure 2.5 looks at the relationship between proficiency in problem solving in technology-rich environments and ICT
access and use at the country level. The first panel compares the proportion of adults who score at proficiency Level 2 or
3 to the proportion of households with Internet access, by country. The comparison suggests that Internet access explains
about two-fifths of the variation in proficiency across countries. The second panel then compares the proportion of
adults who score at proficiency Level 2 or 3 to the proportion of adults who use e-mail at least once a month. It shows
that monthly use of e-mail explains about three-fifths of the variation in proficiency across countries. When considering
ICT access and e-mail use together, these variables explain 70% of the variation in proficiency across countries. The
measures of access and use are closely correlated with country performance in problem solving in technology-rich
environments, even though the assessment measures much more than adults’ familiarity with computers.
• Figure 2.5 •
Relationship between proficiency in problem solving in technology-rich environments
and access to or use of ICT
PercentageofadultsscoringatLevel2or3
inproblemsolving
intechnology-richenvironments
70 8050 60 90 100
Percentage of households with Internet access
R² = 0.37100
80
60
40
20
0
PercentageofadultsscoringatLevel2or3
inproblemsolving
intechnology-richenvironments
70 8050 60 90 100
Percentage of adults who use e-mail at least once a month
R² = 0.63100
80
60
40
20
0
Source: Survey of Adult Skills (PIAAC) (2012) and OECD, ICT Database and Eurostat, Community Survey on ICT usage in housholds and by individuals,
November 2011. See Tables A2.1 and A2.5.
1 2http://dx.doi.org/10.1787/888933231538

2
• Figure 2.6 •
Relationship between ICT use in the Survey of Adult Skills
and in the Eurostat Community Survey
Percentageofadultswhousee-mailatleast
onceamonth(SurveyofAdultSkills)
40 6020 80 100
Percentage of individuals who sent or received e-mails
within the three months prior to the Eurostat survey
R² = 0.88100
80
60
40
20
PercentageofadultswhousetheInternetforconducting
transactionsorbankingatleastonceamonth
(SurveyofAdultSkills)
40 6020 80 100
Percentage of individuals who used online banking
within the three months prior to the Eurostat survey
R² = 0.95100
80
60
40
20
PercentageofadultswhousetheInternettobetter
understandissuesatleastonceamonth
(SurveyofAdultSkills)
Percentage of individuals who used the Internet for seeking health-related
information within the three months prior to the Eurostat survey
R² = 0.62
40 6020 80 100
100
80
60
40
20
Source: Survey of Adult Skills (PIAAC) (2012), Eurostat Community Survey on ICT usage in households and by individuals. See Tables B1.5, B1.6 and B2.2
in Annex B and Tables A2.4a, b and c.
1 2http://dx.doi.org/10.1787/888933231542

2
Box 2.2 Sample task in problem solving in technology-rich environments
An example of a problem-solving item is provided below. This item involves a scenario in which the respondent
assumes the role of a job-seeker. Respondents access and evaluate information relating to job search in a
simulated web environment. This environment includes tools and functionalities similar to those found in real-life
applications. Users are able to:
• click on links on both the results page and associated web pages;
• navigate, using the back and forward arrows or the Home icon; and
• bookmark web pages and view or change those bookmarks.
The first test figure presented above is the results page of the search-engine application, which lists five employment
agency websites. To complete the task successfully, respondents have to search through the pages of the listed
websites to identify whether registration or the payment of a fee is required in order to gain further information
about available jobs. Respondents can click on the links on the search page to be directed to the websites identified.
For example, by clicking on the “Work Links” link, the respondent is directed to the home page of “Work Links”.
...

2
In order to discover whether access to the information on available jobs requires registration with the organisation
or payment of a fee, the respondent must click the “Learn More” button which opens the following page. The
respondent must then return to the search results page to continue evaluating the sites in terms of the specified
criteria, using the back arrows without bookmarking the page (correct answer) or having bookmarked the page
(incorrect answer).
Notes
1. See notes regarding Cyprus below.
2. The discussion in Chapter 4 on proficiency in problem solving in technology-rich environments at work examines responses to the
questions related to the use of ICT at work.
3. Respondents who have never used a computer were not asked about the frequency with which they use different ICT applications.
The analysis assumes that those respondents who have never used a computer have also never used the different ICT applications.
4. Country-specific figures are available in Tables A2.4a, b, c, d and e.
Notes regarding Cyprus

2
A note regarding the Russian Federation
More detailed information regarding the data from the Russian Federation as well as that of other countries can be found in the
References
OECD (2014), Technical Report of the Survey of Adult Skills, www.oecd.org/site/piaac/_Technical%20Report_17OCT13.pdf,
pre-publication copy.
OECD (2013a), OECD Skills Outlook 2013: First Results from the Survey of Adult Skills, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264204256-en.
OECD (2013b), The Survey of Adult Skills: Reader’s Companion, OECD Publishing, Paris, http://dx.doi.
org/10.1787/9789264204027-en.
OECD (2012), Literacy, Numeracy and Problem Solving in Technology-Rich Environments: Framework for the OECD
Survey of Adult Skills, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264128859-en.
PIAAC (2011), PIAAC Technical Standards and Guidelines, OECD Programme for the International Assessment of Adult
Competencies [PIAAC].

3
Differences within countries
in proficiency in problem solving
in technology-rich environments
This chapter explores the ways in which proficiency in problem solving
in technology-rich environments varies within countries across various
socio-demographic groups. It looks at differences in proficiency related
to age, education, gender, parents’ education, immigrant and language
background, and participation in adult education and training. In
addition, the chapter examines the association among proficiency in
these skills, the use of ICT, and literacy proficiency.

3
DIFFERENCES WITHIN COUNTRIES IN PROFICIENCY IN PROBLEM SOLVING IN TECHNOLOGY-RICH ENVIRONMENTS
With the widespread diffusion of information and communication technologies (ICT) in all areas of life, the ability to
manage information in digital environments and solve problems that involve the use of digital devices, applications and
networks is becoming essential for adults of all ages. This chapter examines the relationships between different socio-
demographic characteristics and proficiency in problem solving in technology-rich environments, as measured by the 2012
Survey of Adult Skills, a product of the OECD Programme for the International Assessment of Adult Competencies (PIAAC).
The analyses help to identify the groups that are most likely to encounter difficulties in using ICT to solve problems. This
information can then be used to inform government policies that aim to develop these specific skills in particular segments
of the population. In addition, some of the characteristics examined – such as those related to education, participation
in adult education and training, and ICT use – provide insights into the types of activities that are likely to lead to better
performance in problem solving using ICT. Chapter 5 explores the policy implications of these different relationships.
Of the eight characteristics examined, six are strongly related to the probability of being highly proficient in problem
solving in technology-rich environments (Figure 3.1). In particular, being highly proficient in literacy, being younger,
having a parent with tertiary qualifications, having tertiary qualifications oneself, being a regular user of ICT, and
participating in adult education and training are all strongly associated with the probability of performing at high levels
in the problem-solving assessment. Men are found to have a small advantage over women in these skills. The observed
differences in proficiency related to immigrant and language background are not significant across OECD countries;
however, there are significant differences within some countries.
Key findings
• Literacy proficiency and age have the strongest relationships to proficiency in problem solving in technology-
rich environments. Educational attainment and ICT use are strongly related to proficiency, after accounting for
other factors.
• Gender is weakly related to proficiency in problem solving in technology-rich environments, while immigrant and
language background do not have a significant relationship with proficiency in technology-rich environments,
after accounting for other factors.
• Age and educational attainment both have a strong relationship with whether or not an adult has experience
using a computer.
When adjustments are made to take account of the impact of other factors, the relationships between many of the characteristics
and performance in this domain weaken considerably.1 However, age and literacy proficiency are still associated with large
differences in proficiency. Even when other characteristics are taken into account, a person scoring at Level 4 or 5 on the
literacy scale of the Survey of Adult Skills is 69 percentage points more likely to be highly proficient in problem solving
in technology-rich environments than someone who scores at Level 2 on the literacy scale. Similarly, a 16-24 year-old is
28 percentage points more likely than a 55-65 year-old to be perform at a high level in the problem-solving domain.
Each of the characteristics, except gender and immigrant and language background, is also associated with the probability
of having no computer experience (Figure 3.2).2 However, when other socio-demographic characteristics and literacy
proficiency are taken into account, only age and educational attainment are strongly related to the probability that an adult
has no experience in using computers. After accounting for other variables, literacy is not strongly related to computer use.
Proficiency in problem solving in technology-rich environments, and
computer experience, related to various socio-demographic characteristics
Differences related to age
The personal computer and the Internet have been widely used only since the 1990s. Consequently, different cohorts of
individuals were first exposed to these technologies at very different ages. These cohorts first developed skills in using
these technologies under different conditions (if at all), and tend to have somewhat different relationships with the
technologies. In most of the countries that participated in the Survey of Adult Skills, 16-24 year-olds can be considered
to be “digital natives”, in that they were brought up in an environment in which digital technologies were in widespread
use in homes and in school. At the other extreme, most adults aged 55-65 were first exposed to these technologies in
their 30s, at the earliest. Given that familiarity with ICT is a precondition for displaying proficiency in problem solving in
technology-rich environments, it would be expected that there are strong age-related differences in proficiency in these
skills, and that the differences would be greatest in countries in which diffusion of digital technologies has been slowest.

3
• Figure 3.1 •
Differences in problem solving in technology-rich environments proficiency between various groups
Percentage differences between groups of adults who score at Level 2 or 3 in problem solving in technology-rich environments,
before and after accounting for various characteristics
Age (16-24 year-olds minus 55-65 year-olds)
Educational attainment
(Tertiary minus below upper secondary)
Gender (Men minus women)
Immigrant and language background
(Native-born/language minus foreign-born/language)
Parents’ educational attainment (At least one parent attained
tertiary minus neither parent attained upper secondary)
Participation in adult education and training
(Participation minus non-participation)
0 20 40 60 80 Percentage points
AdjustedUnadjusted
E-mail use (At least monthly users minus
less than monthly users and non-users)
Literacy proﬁciency
(Scoring at Level 4/5 minus scoring at Level 2)
High proﬁciency (Levels 2 and 3)
Note: Adjusted results include controls for age, educational attainment, gender, parents’ educational attainment, immigrant and language background,
participation in adult education and training, e-mail use, and literacy proficiency. Statistically significant differences are marked in a darker tone. Results
for each country are available in Table B3.3 in Annex B.
Source: Survey of Adult Skills (PIAAC) (2012), Table A3.1
1 2http://dx.doi.org/10.1787/888933231566
• Figure 3.2 •
Differences in computer experience between various groups
Percentage differences between various groups of adults who have no computer experience,
before and after accounting for various characteristics
Age (55-65 year-olds minus 16-24 year-olds)
(Below upper secondary minus tertiary)
Gender (Women minus men)
(Foreign-born/language minus native-born/language)
Parents’ educational attainment (Neither parent attained
upper secondary minus at least one parent attained tertiary)
(Non-participation minus participation)
0 5 10 15 20 25 30 Percentage points
AdjustedUnadjusted
(Scoring at Level 2 minus scoring at Level 4/5)
Note: Adjusted results include controls for age, educational attainment, gender, parents’ educational attainment, immigrant and language background,
participation in adult education and training (AET), and literacy proficiency. Statistically significant differences are marked in a darker tone. Results for
each country are available in Table B3.5 in Annex B.
1 2http://dx.doi.org/10.1787/888933231577
As expected, there is a strong correlation between age and proficiency in problem solving in technology-rich environments
across participating countries. At the same time, the strength of the correlation varies considerably across countries. On
average, 51% of 16-24 year-olds, but only 12% of 55-65 year-olds, perform at Level 2 or 3 in the domain, a difference of
39 percentage points (Figure 3.3). The gap between the youngest and oldest age groups ranges from 18 percentage points
in the United States to 59 percentage points in Korea. Between countries, there is also greater variation in proficiency
among the youngest adults than among the oldest. For example, the proportion of 16-24 year-olds who score at Level 2
or 3 ranges from 38% (the United States) to 63% (Korea), while the proportion of 55-65 year-olds who perform at those
levels ranges from only 3% (Poland) to 20% (the United States).

3
Denmark, Finland, the Netherlands, Norway and Sweden have larger proportions of adults who score at Level 2 or 3 in
problem solving in technology-rich environments, with larger proportions of adults of all age groups who score at these
levels compared to the average. This suggests that most adults in these countries generally had better opportunities to
develop these skills, regardless of their age. By contrast, in some other countries, some of the age groups have relatively
smaller proportions of adults who score at Level 2 or 3, which pulls down the country average. For example, despite
the fact that Korea has the largest proportion of young adults who perform at Level 2 or 3 in the domain (63%), Korea
has a smaller-than-average proportion of adults who perform at those levels. This largely reflects the fact that only a tiny
proportion (4%) of 55-65 year-old Koreans perform at Level 2 or 3 (the second smallest proportion after that observed in
Poland). By contrast, the United States has the largest proportion of 55-65 year-olds who score at Level 2 or 3, but the
smallest proportion of 16-24 year-olds who score at those levels.
Computer experience is also related to age. On average, less than 1% of 16-24 year-olds, but 22% of 55-65 year-olds,
have no experience with computers (Figure 3.3). The gap between the two age groups ranges from only 5 percentage
points in Norway and Sweden to over 50 percentage points in Korea. The variation across countries is much larger
among members of the oldest group than among members of the youngest group. The chance that a 16-24 year-old has
no computer experience is less than 5% in all countries, whereas the probability that a 55-65 year-old has no computer
experience ranges from 5% in Sweden to 52% in Korea.
In most countries, only a small proportion of the youngest cohort does not have computer experience, except for the
Slovak Republic, where 4.8% of 16-24 year-olds lack computer experience compared to the average of 0.8% across
participating OECD countries. However, large proportions of the oldest age group have no computer experience. Across
countries, except Denmark, the Netherlands, Norway and Sweden, more than 10% of adults in oldest age group lack
computer experience. In Korea, more than one in two 55-65 year-olds do not have computer experience, nor do more
than 45% of adults that age in Poland and the Slovak Republic.
• Figure 3.3 •
Problem-solving proficiency and computer experience, by age
Percentage of adults who score at Level 2 or 3 in problem solving in technology-rich environments or have no computer experience
Sweden
Finland
Netherlands
Norway
Denmark
Australia
Canada
Germany
Japan
Flanders (Belgium)
United States
Czech Republic
Austria
Average
Korea
Estonia
Slovak Republic
Ireland
Poland
55-65 year-olds16-24 year-olds
High levels of proﬁciency (Level 2 or 3) No computer experience
%% 100 10020 2040 4060 6080 8000
Countries are ranked in descending order of the percentage of adults aged 16-65 scoring at Level 2 or 3 in problem solving in technology-rich environments.
1 2http://dx.doi.org/10.1787/888933231586

3
Differences related to educational attainment
Given that many types of skills, including problem-solving skills, are developed in formal education, it is reasonable
to expect that higher levels of education will be associated with higher levels of proficiency in problem solving in
technology-rich environments. However, a positive association between education and proficiency in these skills does
not mean that formal education is directly responsible for the higher levels of proficiency observed. It is also likely
that adults with higher levels of education have other experiences, such as work in particular occupations or training
opportunities later on, that have a more direct impact on proficiency in this domain.
On average, an adult with tertiary education is 33 percentage points more likely than an adult with less than secondary
education to perform at Level 2 or 3 in the assessment of problem solving in technology-rich environments (Figure 3.4).
However, there are large variations in this difference across countries, ranging from less than 20 percentage points in
Estonia to over 40 percentage points in the Netherlands and the United Kingdom.
Educational attainment is also correlated with computer experience. On average, adults with less formal education
are more likely to lack experience with computers than those with more education. Only 1% of adults with tertiary
education lack experience with computers compared to 21% of those with less than secondary education.The difference
between high- and low-educated adults in the probability that they have no experience with computers ranges from
4 percentage points in Norway to 49 percentage points in the Slovak Republic. In every country, few adults with tertiary
education lack computer experience. The largest differences between countries are thus found in the proportion of
adults with less than secondary education who have no experience with computers. The countries with fewer of these
adults are generally also the countries with larger proportions of adults who perform at Level 2 or 3 in problem solving
in technology-rich environments.
• Figure 3.4 •
Problem-solving proficiency and computer experience, by educational attainment
Sweden
Finland
Netherlands
Norway
Denmark
Australia
Canada
Germany
Japan
Flanders (Belgium)
United States
Czech Republic
Austria
Average
Korea
Estonia
Slovak Republic
Ireland
Poland
Lower than upper secondaryTertiary
%% 100 10020 2040 4060 6080 8000
1 2http://dx.doi.org/10.1787/888933231590

3
Differences related to adult education and training
Adult education and training, like compulsory education, can provide opportunities to develop proficiency in problem
solving in technology-rich environments. For example, many adults are likely to have had at least some training in
the use of word-processing software or spreadsheets that would then have an impact on their performance in the
problem solving in technology-rich environments assessment, although the type of training would largely depend on
adults’ occupations and individual needs. It is also likely that people who are more proficient in these skills will avail
themselves of learning opportunities through adult education and training. The Survey of Adult Skills found that, on
average across participating OECD countries, 52% of respondents had participated in adult education and training in
the year prior to the survey.3
Not surprisingly, recent participation in adult education and training activities is associated with greater proficiency
in problem solving in technology-rich environments. Across OECD countries, 42% of adults who participated in adult
education and training during the previous year were proficient at Level 2 or 3 in this domain, compared to only 18% of
adults who had not participated in adult education and training during that period (Table B3.6).
Adult education and training is also associated with computer experience. Only 3% of adults who had recently
participated in adult education and training activities lack computer experience compared to 16% of those who had not
recently participated in such activities (Table B3.6). Across countries, only a small proportion of adults who had recently
participated in adult education and training lack computer experience, from near zero in Sweden to 7% in Korea and
the Slovak Republic. There is a much wider variation among countries in the proportion of adults who had not recently
participated in adult education and training and who have no computer experience: from 4% in Norway to 34% in the
Slovak Republic.
Differences related to gender
Surveys commonly find that men use computers somewhat more frequently than women do. For example, Eurostat
found that, in 2011, 77% of men aged 16-74 used a computer in the 12 months prior to the survey compared to 73% of
women that age.4 Given that proficiency in problem solving in technology-rich environments requires basic computer
skills, it would not be surprising if there were some differences between men’s and women’s performance in the domain
that are similar to the modest differences in men’s and women’s rates of computer use. In the PISA 2012 problem-solving
assessment, which was delivered exclusively in computer-based format, 15-year-old boys had a slight advantage (of
7 score points) over girls (OECD, 2013b).
Indeed, in the 2012 Survey of Adult Skills, men perform slightly better than women in problem solving in technology-
rich environments. On average across OECD countries, the proportion of men who are proficient at Level 2 or 3 in this
domain is 5 percentage points bigger than that of women (Figure 3.5). In all participating countries, a larger share of men
than women performs at these levels, but the differences are not statistically significant in all cases. The largest gender
difference (11 percentage points) is observed in Japan. Interestingly, in countries that are most proficient in these skills,
men’s performance advantage over women is larger than average. Among young adults aged 16-24, there is virtually no
difference, on average, in the proportions of men and women who are proficient at Level 2 or 3 in problem solving in
technology-rich environments (Table A3.5).
Men and women who participated in the 2012 Survey of Adult Skills reported similar levels of experience with
computers.5 On average across OECD countries, the proportion of women who lack computer experience is slightly
larger (0.4 percentage points) than the proportion of men who do (Figure 3.5). In roughly half of the participating
countries, men are more likely than women to have no computer experience, while the reverse is true in the remainder
of the countries. In Austria, the Czech Republic, Germany, Japan and Korea, more women than men reported that they
have no computer experience, though in none of those countries is the gap larger than 5 percentage points. In Estonia,
Ireland and Poland, men were more likely than women to report that they have no computer experience, but again the
difference is small (between 2 and 4 percentage points). There is almost no gender difference, in any country, in the
likelihood that a 16-24 year-old has no experience in using a computer (Table A3.5).

3
• Figure 3.5 •
Problem-solving proficiency and computer experience, by gender
Sweden
Finland
Netherlands
Norway
Denmark
Australia
Canada
Germany
Japan
Flanders (Belgium)
United States
Czech Republic
Austria
Average
Korea
Estonia
Slovak Republic
Ireland
Poland
WomenMen
%% 100 10020 2040 4060 6080 8000
1 2http://dx.doi.org/10.1787/888933231605
Differences related to socio-economic status
Given that socio-economic status has a significant impact on many life outcomes, policy makers need to understand
the relationship between socio-economic status and skills development and consider whether that relationship reflects
inequities in opportunities that could be addressed by policy. The Survey of Adult Skills uses parents’ education as an
indicator of the socio-economic status of respondents. In the literacy and numeracy domains, the survey revealed a
statistically significant difference of about 40 score points between adults with at least one parent who had attained tertiary
education and adults with neither parent having attained upper secondary education (OECD 2013a, Table A3.6[L]).
There is a strong correlation between parents’ education and the probability that an adult performs at Level 2 or 3 in problem
solving in technology-rich environments. On average across OECD countries, the share of adults who are proficient at
these levels is 38 percentage points larger among those with at least one parent who had attained tertiary education than
it is among adults with neither parent having attained upper secondary education (Table B3.7). The differences in these
proportions range from 30 percentage points in Australia to 52 percentage points in the Czech Republic.
There is also a strong correlation between parents’ education and computer experience. On average, adults with at least
one parent who had attained tertiary education are 17 percentage points less likely to lack computer experience than
adults with neither parent having attained upper secondary education (Table B3.7).The size of this gap varies substantially
across countries, from 3 percentage points in Norway and Sweden to 50 percentage points in the Slovak Republic. Across
all countries, few adults with at least one parent who attained tertiary education lack computer experience; so most
of the between-country variation in computer experience associated with parents’ education comes from disparities in
experience with computers among adults with neither parent having attained upper secondary education.
Differences related to immigrant and language background
In most of the countries that participated in the Survey of Adult Skills, a significant share of the population is of foreign origin;
in many cases, immigrants represent over 10% of the total population of these countries. Immigrants often face special

3
challenges in developing information-processing skills in the language(s) of their country of residence. On average, immigrants
who did not speak the language of their host country in their childhood have lower proficiency in literacy than native-born,
native-language adults (OECD 2013a, Table A3.15 [L]). Policy makers need to understand how well – or poorly – immigrants
can manage information in digital environments, in the language(s) of their country of residence, so that sufficient assistance
is offered to enable immigrants to integrate more smoothly into the labour market and into society more broadly.
Information about immigrant and language background is combined in the analysis of their relationship with proficiency in
problem solving in technology-rich environments. In all countries, most adults were born in-country (“native-born”) and most
grew up speaking the language(s) in which the survey was delivered (“native language”). Across participating OECD countries,
86% of adults fall into the category “native-born, native language” (OECD 2013a, Table B3.11). The next-largest group is
composed of adults who migrated into the country (“foreign-born”) and did not grow up speaking the language(s) in which the
survey was delivered (“foreign language”). On average, 7% of adults fall into this category, “foreign-born, foreign language”.
The remainder of adults can be classified into two other categories: adults born in-country who did not grow up speaking
the language(s) of the survey (“native-born, foreign language”), and immigrants who grew up speaking the language(s) of the
survey (“foreign-born, native language”). These groups represent 2% and 4% of the adult population, respectively, across
participating OECD countries. There is substantial variation in these proportions across countries, however. For example, the
size of the foreign-born, foreign-language population ranges from near zero in Poland and Japan to 17% in Canada.
Immigrant and language background is correlated with the probability of performing at Level 2 or 3 in the problem
solving in technology-rich environments assessment, and this correlation is significant. Some 36% of native-born, native-
language adults are proficient at Level 2 or 3 in the domain compared to 17% of foreign-born, foreign-language adults
(Figure 3.6). The difference in the proportions of native-born, native-language adults and foreign-born, foreign-language
adults who perform at those levels ranges from 5 percentage points in Ireland to 31 percentage points in Sweden. There
is much greater between-country variation in the proportion of native-born, native-language adults who are proficient
at Level 2 or 3 than there is in the proportion of foreign-born, foreign-language adults who perform at these levels.
For example, foreign-born, foreign-language adults in Ireland and Sweden have very similar chances of performing at
Level 2 or 3 in the domain – 20% and 18%, respectively – but the chances that native-born, native-language adults in
the two countries perform at those levels are very different – 25% and 49%, respectively.
• Figure 3.6 •
Problem-solving proficiency and computer experience, by immigrant and language status
Sweden
Finland
Netherlands
Norway
Denmark
Australia
Canada
Germany
Japan
Flanders (Belgium)
United States
Czech Republic
Austria
Average
Korea
Estonia
Slovak Republic
Ireland
Poland
Foreign-born/foreign-languageNative-born/native-language
%% 100 10020 2040 4060 6080 8000
Notes: Estimates based on low sample sizes are not shown. Estimates for the Russian Federation are missing due to the lack of language variables.
1 2http://dx.doi.org/10.1787/888933231610

3
Immigrant and language background is also weakly associated with computer experience. On average, native-born,
native-language adults (8%) are less likely than foreign-born, foreign-language adults (13%) to lack computer experience
(Table A3.6). In Estonia, the Slovak Republic and the United States, the gap between these two groups in the probability
that an adult lacks computer experience is over 17 percentage points. In contrast, in Ireland, native-born, native-language
adults are more likely to lack computer experience than foreign-born, foreign-language adults.
Differences related to ICT use
The frequency with which adults use ICT is likely to be closely related to proficiency in problem solving in technology-rich
environments, both because more frequent use of ICT is likely to improve proficiency in this domain, and because people
with greater proficiency are likely to use ICT more often. In the cross-country analyses in Chapter 2, frequency of ICT use
(measured here as the frequency with which adults use e-mail in their daily lives) is strongly correlated with proficiency in
problem solving in technology-rich environments; thus it is reasonable to expect a similar relation to hold within countries.
The more frequently adults use e-mail, the better their performance in the domain. The probability of performing at
Level 2 or 3 in problem solving in technology-rich environments is 36 percentage points greater among adults who use
e-mail at least once a month than for those who use e-mail less often or not at all (Table B3.8). The difference ranges from
a low of 29 percentage points in Poland to a high of 42 percentage points in Finland and the Netherlands.
Differences related to literacy proficiency
As the tasks included in the assessment of problem solving in technology-rich environments involve understanding
and interpreting written texts, a reasonably strong relationship between proficiency in literacy and proficiency in the
problem-solving domain is expected6 – and is, in fact, observed in the survey. On average across OECD countries, 83%
of adults who are highly proficient in literacy (Level 4 or 5 in the assessment) are also highly proficient (Level 2 or 3)
in problem solving in technology-rich environments (Figure 3.7). However, the proportion of adults at these levels of
proficiency varies widely across countries, from 57% in Poland to 94% in Sweden. In contrast, only 11% of adults who
attain Level 2 in literacy proficiency (on average, one in three adults perform at this level) are highly proficient (Level 2
or 3) in the problem-solving domain, and in no country does this share exceed 15%.
• Figure 3.7 •
Problem-solving proficiency and computer experience, by level of literacy proficiency
Percentage of adults scoring at Level 2 or 3 in problem solving in technology-rich environments or have no computer experience
Sweden
Finland
Netherlands
Norway
Denmark
Australia
Canada
Germany
Japan
Flanders (Belgium)
United States
Czech Republic
Austria
Average
Korea
Estonia
Slovak Republic
Ireland
Poland
Level 2 in literacyLevel 4 or 5 in literacy
%% 100 10020 2040 4060 6080 8000
1 2http://dx.doi.org/10.1787/888933231629

3
Literacy proficiency is also related to computer experience. On average, only 1% of adults who perform at Level 4 or
5 in the literacy assessment lack computer experience, compared with 10% of adults proficient at Level 2 in literacy
(Figure 3.7). There is greater between-country variation in computer experience among adults who are less proficient in
literacy than among adults who are more proficient. Few adults who perform at Level 4 or 5 in the literacy assessment lack
computer experience, with the exception of those in the Slovak Republic (6%). In contrast, the proportion of adults who
perform at Level 2 in literacy who have no computer experience ranges from 2% in Sweden to 26% in the Slovak Republic.
Differences in proficiency related to specific characteristics,
after accounting for other variables
Most of the characteristics discussed above have a close relationship with the probability of performing at Level 2 or 3
in problem solving in technology-rich environments and the probability of having no computer experience. But these
characteristics are often related to one another (e.g. older adults have lower educational attainment, on average in most
countries); thus it is important to know how each of the characteristics is associated with proficiency in problem solving
in technology-rich environments when the other characteristics are held constant.
This section details the results when logistic regressions are used to calculate the probability of performing at Level 2
or 3 in problem solving in technology-rich environments if an adult has a certain characteristic, after accounting for the
other variables under consideration. These regressions produce odds ratios (see Box 3.1 for a discussion of odds ratios)
that reflect the relative increase in the probability that a particular group, say 55-65 year-olds, will perform at Level 2 or
3 in the domain compared to a reference group with different demographic characteristics, say 16-24 year-olds.
Because of the close relationship between proficiency in problem solving in technology-rich environments and the
frequency of ICT use, as well as the high correlation of proficiency among the three domains (literacy, numeracy and
problem solving in technology-rich environments) covered in the Survey of Adult Skills, the regressions are conducted in
stages, with three versions of analysis. Version 1 examines the relationship between proficiency and socio-demographic
characteristics, without including information on frequency of ICT use and literacy proficiency.Version 2 adds frequency
of ICT use (e-mail) as an additional explanatory variable to distinguish between the relationships with proficiency in
problem solving in technology-rich environments from relationships with the frequency of computer use. Version 3
adds literacy proficiency to the regression to distinguish between relationships with proficiency in the problem-solving
domain and relationships with literacy proficiency. To distinguish between literacy proficiency and general cognitive
ability, Version 3 also includes analyses that use proficiency in numeracy rather than in literacy.
The logistic regressions are performed for each country, and the resulting country coefficients are then averaged across
all participating OECD countries to produce OECD average coefficients. Since there are relatively few statistically
significant differences between the individual estimates and the OECD average, the OECD averages are used in the
following discussion. Figure 3.8 summarises the results of the three different stages of the analysis.
Opportunities to develop skills
The cognitive skills needed to solve problems and ICT skills are acquired and developed in both formal education and
in adult education and training activities. As expected, educational attainment and participation in adult education and
training during the 12 months prior to the survey are both found to be independently related to proficiency in problem
solving in technology-rich environments, even after accounting for other factors.
The probability of performing at Level 2 or 3 in the problem-solving assessment is 39 percentage points higher for
adults with tertiary education than it is for adults with less than upper secondary education, after accounting for
socio-demographic characteristics (Version 1), somewhat larger than the difference of 33 percentage points that
was observed before accounting for the other factors. The difference increases because controlling for age takes into
account the large proportion of young adults with low education – and thus corrects for the way low educational
attainment among young adults reduces the observed difference in proficiency in problem solving in technology-rich
environments that is associated with education. Adding frequency of ICT use (Version 2) to the regression brings the
difference back to 33 percentage points. When proficiency in literacy is added (Version 3), the adjusted difference
drops substantially to 13 percentage points. If proficiency in numeracy is added instead of proficiency in literacy, the
reduction is similar. Thus much of the relationship between educational attainment and proficiency in the problem-
solving domain is explained by the higher cognitive proficiency of better-educated adults, as measured by the literacy
or numeracy assessments.

3
After accounting for socio-demographic characteristics (Version 1), the probability of performing at Level 2 or 3 in
problem solving in technology-rich environments is 12 percentage points higher for adults who have participated in
adult education and training activities in the 12 months prior to the survey than it is for adults who have not recently
participated in those activities – half the difference (24 percentage points) observed before taking other socio-economic
characteristics into account. Adding frequency of e-mail use to the regression (Version 2) reduces this difference to 9
percentage points, and adding literacy proficiency (Version 3) reduces the difference to 7 percentage points.
• Figure 3.8 •
How problem-solving proficiency and lack of computer experience are affected
by various characteristics
Differences in the percentage of adults scoring at Level 2 or 3 in problem solving in technology-rich environments
or those without computer experience, before and after accounting for various characteristics
Age
(16-24 year-olds minus 55-65 year-olds)
(Tertiary minus below upper secondary)
Gender
(Men minus women)
Parents’ educational attainment
(At least one parent attained tertiary minus
neither parent attained upper secondary)
(Native-born/language minus foreign-born/language)
(Participation minus non-participation)
E-mail use
(At least monthly users minus less
than monthly users and non-users)
(Scoring at Level 4/5 minus scoring at Level 2)
Version 3: Version 2 + Literacy proﬁciencyVersion 1: Socio-demographic controls
Version 2: Version 1 + E-mail useUnadjusted
High proﬁciency (Level 2 or 3) No computer experience
Percentage pointsPercentage points
80 30102030 1050 4070 60 2000
n.s.
n.s.
n.s.
n.s.
n.s.
n.s: not significantly different from zero.
Note: Version 1 adjusts for socio-demographic characteristics (age, educational attainment, gender, parents’ educational attainment and immigrant and
language background). Version 2 adds frequency of ICT use (e-mail) as an adjustment to Version 1. Version 3 adds literacy proficiency to the regression of
Version 2 to adjust for cognitive ability.
Results for each country are available in Tables B3.1, 2, 3, 4 and 5 in Annex B.
Source : Survey of Adult Skills (PIAAC)(2012), Tables 3.1 and 3.2.
1 2http://dx.doi.org/10.1787/888933231637
Background characteristics
The analyses include four background characteristics that are not specifically linked to opportunities for skills
development: age, gender, parents’ level of education, and immigrant status and language background.
Of these four characteristics, age has the strongest relationship with proficiency in problem solving in technology-rich
environments, a relationship that is only slightly affected when other factors are taken into account. In Version 3 of the
regression, adults aged 16-24 are 28 percentage points more likely than 55-65 year-olds to perform at Level 2 or 3 in the
problem-solving assessment. The difference was 39 percentage points before taking other factors into account.
The probability that men, rather than women, perform at Level 2 or 3 in the assessment of problem solving in technology-
rich environments increases by two percentage points after other factors are taken into account: from a 5 percentage-

3
point difference before taking other factors into account to a 7 percentage-point difference (in all three versions).7 This
is because more women have tertiary education than men, and accounting for education widens the gender gap by
correcting for the extra benefit women have from their higher level of education.
The probability that adults with highly educated parents perform at Level 2 or 3 in the problem-solving domain is
7 percentage points greater than that for adults whose parents have low educational attainment, after accounting for socio-
demographic variables, e-mail use and literacy proficiency. This is substantially less than the difference of 39 percentage
points before accounting for these other factors. Much of the advantage of having better-educated parents disappears after
other socio-demographic factors are taken into account (Version 1) and, to a lesser extent, when literacy proficiency is also
taken into account (Version 3).8 Adding numeracy instead of literacy proficiency in Version 3 produces a similar result.
Before accounting for other factors, the difference in probability that a native-born, native-language adult performs at
Level 2 or 3 in problem solving in technology-rich environments compared with a foreign-born, foreign-language adult is
20 percentage points; after taking those other factors into account (Version 1), the difference increases to 29 percentage
points.9 This is because foreign-born, foreign-language adults are relatively younger and more educated than native-
born, native-language adults. Taking age and education into account adjusts for those advantages for foreign-born,
foreign-language adults and thus widens the gap between them and native-born, native-language adults in proficiency
in the problem-solving domain. After accounting for literacy proficiency in addition to socio-demographic factors and
e-mail use (Version 3), the advantage associated with native-born, native-language adults shrinks to 16 percentage points
and is no longer significant. If numeracy proficiency is considered instead of literacy proficiency, the result is similar
(14 percentage points and not significant). This means that the disparity in proficiency in problem solving in technology-
rich environments between native-born, native-language adults and foreign-born, foreign-language adults is largely
explained by differences in their general cognitive proficiency in the language of their country of residence as assessed
through either the literacy or numeracy assessment in the Survey of Adult Skills.
ICT use
A minimum level of familiarity and comfort with computers and common computer applications is required to display
proficiency in problem solving in technology-rich environments. Given that the difficulty of the tasks in the problem-
solving assessment reflects both the cognitive demands placed on the respondents and more complex uses of technology,
it is expected that there would be a relationship between the frequency with which common computer applications are
used and proficiency in problem solving in technology-rich environments. In line with expectations, adults who use
e-mail at least once a month have a 15 percentage point greater probability of scoring at Level 2 or 3 in the problem-
solving domain than less regular users, after taking into account other socio-demographic characteristics and literacy
proficiency (Version 3). This suggests that there is a mutually reinforcing relationship between the capacity to solve
problems in digital environments and using computer applications, as represented here by e-mail.
Literacy proficiency
After taking account of other factors (Version 3), the probability of performing at Level 2 or 3 in problem solving in
technology-rich environments is 69 percentage points higher for adults who are highly proficient in literacy (performing
at Level 4 or 5 in the literacy assessment) than it is for adults with lower literacy proficiency (performing at Level 2). This
difference is almost as large as that observed before other factors are taken into account (72 percentage points). Using
numeracy proficiency in place of literacy proficiency, the difference between the two groups is similar. This suggests that
the relationship between literacy proficiency and proficiency in problem solving reflects a relationship between general
cognitive proficiency and problem solving using ICT, rather than a relationship specific to literacy proficiency.
The close relationship between general cognitive proficiency and the capacity to solve problems in digital environments is
not surprising. The upper levels of performance on both the literacy and the numeracy assessments in the Survey of Adult
Skills involve cognitive tasks that include an element of problem solving. Tasks at Levels 4 and 5 in literacy involve multi-step
operations to interpret and synthesise multiple texts, including evaluating subtle evidence to accomplish the tasks. Similarly,
tasks at Levels 4 and 5 in numeracy involve complex contexts, multiple steps, choosing relevant problem-solving strategies,
and communicating explanations of the solutions. The results confirm that adults who can perform such tasks in literacy and
numeracy are often able to perform the kinds of tasks, using digital tools and applications, that are assessed in the survey.10
In summary, literacy proficiency and age have the strongest independent relationships to proficiency in problem solving
in technology-rich environments, after accounting for other factors. Education and ICT use have moderately strong
relationships.

3
Differences in experience with computers related to specific characteristics,
after accounting for other variables
A similar analysis was conducted to examine the relationships among background characteristics, educational and
labour market factors, literacy proficiency and the probability that an adult has no computer experience. The results
differ to some extent from those observed for proficiency in problem solving in technology-rich environments. Age and
educational attainment both have strong relationships with the probability of whether or not an adult has experience
in using a computer. After taking other factors into account, younger adults are less likely than older adults to have no
prior computer experience, as are adults with higher levels of educational attainment. For example, after taking other
socio-demographic factors and literacy proficiency into account, a 16-24 year-old is less likely to have no computer
experience, by 25 percentage points, than an adult aged 55-65. In addition to age and educational attainment, only
parents’ education and recent participation in adult education and training had large and statistically significant
relationships with the probability of having no computer experience. Interestingly, numeracy proficiency has a significant
relationship with the lack of computer experience after taking other factors into account. This contrasts with the analyses
of proficiency in problem solving in technology-rich environments, where literacy and numeracy have similar effects.
Box 3.1 Using odds ratios when comparing a group to a reference group
Odds ratios reflect the relative likelihood of an event occurring for a particular group relative to a reference group.
An odds ratio of 1 represents equal chances of an event occurring for the group vis-à-vis the reference group.
Coefficients with a value below 1 indicate that there is less chance of an event occurring for the particular group
compared to the reference group, and coefficients greater than 1 represent greater chances. The odds ratios are
calculated from logistic regressions that take a number of other factors into account.
The definition of the odds ratio is used to calculate an adjusted percentage point difference associated with
each characteristic, using the proficiency in problem solving in technology-rich environments proportion for the
corresponding reference category.
For example, for the relationship of age with higher-level proficiency in problem solving in technology-rich
environments, the reference category is adults aged 55-65. For this reference category, the proportion of adults
with proficiency in Levels 2 or 3 is 11.681%, which corresponds to odds of
0.11681
= 0.13226
1 − 0.11681
Version 3 of the model results in an average coefficient of 1.6214 across OECD countries among adults aged 16-24,
which corresponds to an odds ratio of
e1.6214 = 5.0602
The odds ratio of 5.0602 implies that the odds associated with the contrast group – adults aged 16-24 – when the
other factors are held constant will be the following:
0.13226 * 5.0602 = 0.66926
Odds of 0.66926 for the contrast group can be transformed into the corresponding probability p as follows:
p 0.66926
0.66926 = ⇒ p = ⇒ p = 0.40093
1 − p 1 + 0.66926
As a result, in Version 3 of the model, the adjusted difference in the proportion of 16-24 year-old adults with
proficiency Level 2 or 3 compared to adults aged 55 to 65 is the difference between 11.681% and 40.093%, or
28.412 percentage points.

3
Notes
1. The adjustments include a set of socio-demographic characteristics, along with ICT (e-mail) use and literacy proficiency.
2. ICT use is omitted from Figure 3.2 because the questions related to ICT use were not asked of respondents with no computer
experience.
3. OECD 2013a, Table A5.9 (L). The analysis combines separate measures of job-related and non-job-related adult education and
training, and includes both formal and non-formal types of education and training.
4. http://ec.europa.eu/eurostat/data/database?node_code=isoc_ci_cfp_cu, Series on Individuals – computer use.
5. The contrast with the Eurostat figures cited earlier may reflect differences in the countries represented.
6. Because of the high correlation between literacy and numeracy, the correlation between numeracy and problem solving using ICT
is similar.
7. In some versions of the models, the relationship between proficiency and gender is significantly smaller than the OECD average in
Australia, Canada and the Slovak Republic, and is not significantly different from zero.
8. In all versions of the models, the relationship between proficiency and parents’ education is significantly smaller than the OECD
average in Denmark, Japan and the Netherlands; in Version 3, the relationship is not significantly different from zero in these countries.
9. In some versions of the models, the relationship between proficiency and immigrant and language status is significantly smaller than
the OECD average in Estonia, and is not significantly different from zero.
10. The Adult Literacy and Life Skills Survey (ALL) also assessed problem-solving skills, although the construct for problem solving did
not focus specifically on problem solving in technology-rich environments. ALL found a relationship between problem-solving skills
and literacy, but did not report on whether there was a similar relationship between problem solving and numeracy (OECD/Statistics
Canada, 2011, Chapter 5).
References
org/10.1787/9789264204256-en.
OECD (2013b), PISA 2012 Results: Excellence through Equity (Volume II): Giving Every Student the Chance to Succeed, PISA, OECD
Publishing, Paris, http://dx.doi.org/10.1787/9789264201132-en.
OECD/Statistics Canada (2011), Literacy for Life: Further Results from the Adult Literacy and Life Skills Survey, OECD Publishing, Paris,

4
Proficiency in problem solving
in technology-rich environments,
the use of skills
and labour market outcomes
This chapter examines the relationship among proficiency in problem
solving in technology-rich environments, the use of ICT at work and
labour market outcomes. The analysis first considers the proficiency of
the labour force in using ICT to solve problems and reviews data from
the Survey of Adult Skills about the frequency with which adults use ICT
and solve problems at work, and whether adults believe that their ICT
skills are sufficient for work. The chapter then discusses the relationship
between proficiency in problem solving in technology-rich environments
and labour force participation, unemployment, wages and labour
productivity.

4
Proficiency in Problem solving in technology-rich environments, the use of skills and labour market Outcomes
How proficient are workers and non-workers in problem solving using information and communication technologies
(ICT)? To what extent are workers in different countries using ICT and problem-solving skills at work? Do these adults
believe that they have sufficient ICT skills to do their jobs? Are higher proficiency in problem solving using ICT and
more frequent use of ICT associated with higher rates of participation in the labour market, lower unemployment, higher
wages and higher labour productivity? This chapter examines the relationship between proficiency in problem solving
in technology-rich environments, the use of ICT at work, and labour market outcomes.
Key findings
• Workers are more likely than non-workers to be highly proficient in problem solving in technology-rich
environments, and workers in skilled occupations are more likely to be highly proficient than workers in
elementary occupations.
• In most countries, few workers are concerned that they lack the computer skills needed to do their jobs well, and
few workers say that a lack of computer skills has affected their chances of getting a job, promotion or pay raise.
• Proficiency in problem solving in technology-rich environments and use of ICT (e-mail) are associated with
higher rates of labour force participation and higher wages, even after accounting for other factors. Adults with
no computer experience are less likely to participate in the labour force and are paid less.
• The relationship between proficiency in problem solving in technology-rich environments and wages is more
closely related to skills use than the relationship between wages and either literacy or numeracy proficiency.
A profile of workers’ skills in problem solving and using ICT
Current and recent workers’ proficiency in problem solving in technology-rich
environments
In most countries, workers who were employed at the time of the Survey of Adult Skills (a product of the OECD
Programme for the International Assessment of Adult Competencies, or PIAAC) or who had worked in the 12 months
prior to the survey were more likely than non-workers1 to perform at Level 2 or 3 in the assessment of problem solving
in technology-rich environments, and less likely than non-workers to lack computer experience. On average, 37% of
current and recent workers are proficient at Level 2 or 3 in the domain. The proportion ranges between 21% in Poland
and 47% in Sweden. On average, few current and recent workers (6%) lack computer experience. The proportion is
around 1% in the Nordic countries and 2% in Australia and the Netherlands, and rises to 8% in Japan, 14% in Poland
and Korea, and 16% in the Slovak Republic.
Compared to the 37% of current and recent workers who perform at the higher levels of proficiency in problem solving in
technology-rich environments, only 24% of non-workers attain the same levels of proficiency in the assessment, a difference
of 14 percentage points (Figure 4.1). The difference in the probability of performing at those levels between adults who
have worked in the past year and those who have not, reaches a high of 26 percentage points in the Netherlands. In Korea,
the gap is not significantly different from zero. Computer experience is also related to participation in the labour force.
On average, the difference in having experience using computers between adults who had worked in the year prior to the
survey and those who had not is 11 percentage points. In Estonia, the difference reaches 20 percentage points.
Proficiency in problem solving in technology-rich environments related to occupation
Different occupations require different skills; they also provide different opportunities to exercise and develop skills.
For both reasons, there is likely to be an association between occupation and proficiency in problem solving using ICT.
Across OECD participating countries and across those respondents who provided information about their occupation,
39% are in skilled occupations, 28% are in semi-skilled, white-collar occupations, 21% are in semi-skilled, blue-collar
occupations, and 9% are in elementary occupations2 (Table B4.14).
Differences in proficiency related to occupation are examined by comparing adults employed in skilled and elementary
occupations. Adults in these two broad occupational groups would be expected to be at the top and the bottom,
respectively, of the distribution of cognitive skills. Across OECD countries, 50% of adults in skilled occupations are
proficient at Level 2 or 3 on the problem solving in technology-rich environments scale compared to only 20% of adults
in elementary occupations, a difference of 30 percentage points (Table B4.1). This difference ranges from 21 percentage
points in Poland to 40 percentage points in the United Kingdom.

4
• Figure 4.1 •
Problem-solving proficiency and computer experience, by employment status
Percentage of adults scoring at Level 2 or 3 in problem solving in technology-rich environments or having no computer experience,
for workers* and non-workers
Sweden
Finland
Netherlands
Norway
Denmark
Australia
Canada
Germany
Japan
Flanders (Belgium)
United States
Czech Republic
Austria
Average
Korea
Estonia
Slovak Republic
Ireland
Poland
Non-workerWorker
High levels of proﬁciency (Levels 2 and 3) No computer experience
%% 100 10020 2040 4060 6080 8000
* Workers are defined as adults who were employed when the survey was conducted or whose most recent work experience occurred during the
12 months prior to the survey.
1 2http://dx.doi.org/10.1787/888933231644
Countries with higher proficiency in this domain, in general, tend to exhibit larger differences in proficiency between
occupations. For example, in Sweden, which has the highest proportion of adults who are proficient at Level 2 or 3
in problem solving in technology-rich environments, the probability of scoring at Level 2 or 3 is 61% for adults in
skilled occupations and 27% for adults in elementary occupations, a difference of 34 percentage points. By contrast,
in Poland, which has the smallest proportion of adults who are proficient at Level 2 or 3, the probability is 33%
for adults in skilled occupations and 12% for adults in elementary occupations, a difference of only 21 percentage
points.
In many countries there are also large differences in computer experience related to occupation. Across the OECD
countries that participated in the Survey of Adult Skills, only 1% of adults in skilled occupations lack computer
experience compared to 17% of adults in elementary occupations, a difference of 16 percentage points (Table B4.1).
This difference ranges from less than 5 percentage points in the Nordic countries and Australia, to 44 percentage points
in the Slovak Republic. The variation across countries in the magnitude of this difference is primarily due to the variation
in the computer experience of adults in elementary occupations, because almost no adults in skilled occupations lack
computer experience.
Frequency of ICT use at work
The Survey of Adult Skills includes a set of questions about the frequency of ICT use at work.These questions are identical
to those that are asked about the frequency of ICT use in everyday life, as discussed in Chapter 2. As in Chapter 2, the
analysis in this chapter focuses on the questions related to the use of e-mail, the Internet for understanding issues or
conducting transactions, and the use of spreadsheets and word processing.

4
About 70% of workers use computers3 at work while about 28% do not use a computer at work, on average across
participating countries. In Norway and Sweden, more than 80% of workers reported using computer at work, while
more than 40% of workers in Italy, Poland, the Slovak Republic and Spain said that they do not use a computer at work.
Among the ICT applications discussed in the survey, e-mail is the most frequently used at work (Figure 4.2). Almost half
of workers use e-mail every day at work, which is close to the proportion of adults who use e-mail daily outside of work
(Tables A2.4a and A4.2a). In addition, a third of workers use the Internet daily to understand issues, and half use it at least
once a month for the same purpose (Figure 4.2, Table A4.2b). As with using e-mail and the Internet for understanding
issues outside of work, the greatest frequency of use is found in the Nordic countries and the Netherlands, with the
proportion of workers using these technologies at least once a month approaching 70% for e-mail and surpassing 60%
for the Internet. In contrast, in Poland, only 43% of workers use e-mail and the Internet frequently for understanding
issues.
Adults use the Internet to conduct transactions at work much less frequently. Across OECD countries, 24% of workers
use the Internet for transactions at least once a month, compared to 57% of adults who use the Internet for this purpose
outside of work (Figures 4.2 and 2.4). This is not surprising, since many workers are not in jobs where they are authorised
to make transactions at work, which are defined in the survey as tasks that involve buying, selling or banking. In contrast,
most adults have some responsibility for banking and purchases in their daily lives, and Internet services for carrying out
such tasks are broadly available.
• Figure 4.2 •
Using information technologies at work
Percentage of adults who use information technology applications at work at least once a month (country average*)
Use a computer at work
Use e-mail at work
Use Internet at work to better
understand issues related to work
Use a wordprocessor at work
Use spreadsheet software at work
Use Internet at work for conducting transactions
0 20 40 60 80 100 %
* Country average: average of 19 participating OECD countries and entities.
Source: Survey of Adult Skills (PIAAC) (2012), Tables A4.2a, b, c, d and e.
1 2http://dx.doi.org/10.1787/888933231659
Across OECD countries, 40% of adults use spreadsheets at work compared to 21% of adults who use them outside of
work at least once a month. One in five workers, on average across OECD countries, reported using a spreadsheet every
day. Some 49% of workers said that they use word processing at least once a month. In the Netherlands, almost 60% of
workers use word processing at least that often.
Information on the use of different ICT applications both at work and outside of work is also available for employed
adults. Many workers use ICT with similar frequency both at and outside of work (Tables B4.2 through B4.6). Among
those workers for whom the pattern of ICT use differs between the two spheres, most use e-mail and the Internet more
frequently outside of work than at work. When it comes to using spreadsheets and word processing, the opposite pattern
is observed: these are used more frequently at work than outside of work. Japan shows particularly large proportions
of workers who use ICT frequently at work but infrequently outside of work for all the applications considered, except
transactions on the Internet.

4
Problem solving at work
The Survey of Adult Skills asks respondents how often they encounter situations in their job that involve “more complex
problems that take at least 30 minutes to find a good solution”. Overall, 34% of workers report that they engage in
complex problem solving at least once a month (Table B4.7).
Workers who undertake complex problem solving at least once a month are more likely than other workers to perform
at higher levels in the assessment of problem solving in technology-rich environments. Some 45% of workers who
engage in complex problem solving that frequently are proficient at Level 2 or 3 in the domain, compared to 28% of
workers who engage in complex problem solving less than once a month or never (Figure 4.3). Although few workers
lack computer experience in general, a relationship can still be found between complex problem solving at work and
computer experience, with only 3% of workers lacking computer experience if they engage in complex problem solving
at work at least once a month compared to 9% of workers who engage in complex problem solving less than once a
month or never.
• Figure 4.3 •
Problem-solving proficiency and computer experience, by frequency of complex problem solving
Percentage of workers scoring at Level 2 or 3 in problem solving in technology-rich environments or having no computer experience
Sweden
Finland
Netherlands
Norway
Denmark
Australia
Canada
Germany
Japan
Flanders (Belgium)
United States
Czech Republic
Austria
Average
Korea
Estonia
Slovak Republic
Ireland
Poland
Solve complex problems less than monthly or neverSolve complex problems at least monthly
High levels of proﬁciency (Levels 2 and 3) No computer experience
%% 100 10020 2040 4060 6080 8000
Note: Complex problems are defined as those that take at least 30 minutes to find a good solution.
1 2http://dx.doi.org/10.1787/888933231662
Adequacy of ICT skills for work
The survey’s background questionnaire includes two questions related to the adequacy of ICT skills for work. These are
asked of all workers who have used a computer in their current or previous job. The first asks whether the respondent
has “the computer skills needed to do [his/her] job well” and the second asks whether “a lack of computer skills
affected your chances of being hired for a job or getting a promotion or pay raise”. Both of these questions involve
self-reports and subjective judgements, which might be influenced by cultural factors. However, the second question
suggests some objective criteria to consider (job-related outcomes) when determining the effects of having limited
computer skills.

4
In most countries, relatively few workers believe they lack the computer skills needed to do their jobs well (Figure 4.4).
On average, only 7% of workers report lacking the necessary computer skills, with that share ranging from 2% in the
Czech Republic to 26% in Japan.
Similarly, few workers (5% on average across OECD countries) believe that a lack of computer skills has affected their
chances of being hired, promoted or paid more (Figure 4.4). This proportion ranges from 2% in Korea to 16% in Japan;
and again, the proportion of workers who believe this is more than twice as large in Japan as in any other country.
• Figure 4.4 •
Workers who reported insufficient computer skills
Percentage of workers* who reported that they lack the computer skills to do their job well or that their lack
of computer skills has affected their chances of getting a job, promotion or pay raise
Japan
United States
Australia
Canada
Estonia
Poland
Average
Norway
Ireland
Finland
Denmark
Sweden
Flanders (Belgium)
Austria
Slovak Republic
Netherlands
Germany
Czech Republic
Korea
Lack of the computer skills to do the job well
Lack of computer skills has affected the chances
of getting a job/promotion/pay raise
%% 30 25 305 510 1015 1520 20 2500
* Workers are defined as adults who were employed when the survey was conducted or whose most recent work experience occurred during the
12 months prior to the survey.
Countries are ranked in descending order of the percentage of workers who reported that their lack of computer skills has affected their chances of getting
a job, promotion or pay raise.
Source: Survey of Adult Skills (PIAAC)(2012), Tables A4.4a and b.
1 2http://dx.doi.org/10.1787/888933231674
Although closely related, the two questions cover different aspects of the adequacy of respondents’ skills. Indeed,
some workers may have adequate computer skills for their current job precisely because their lack of computer skills
prevented them from moving to another job requiring more advanced computer skills or because a failure to be
hired, promoted or paid more in the past prompted them to improve their computer skills. On average, only 19% of
adults who report that their employment has been affected at some point by their lack of computer skills feel that they
lack the computer skills they need for their current job (Figure 4.5). A smaller percentage (7%) of the workers whose
employment has not been affected by their lack of computer skills feels that they do, in fact, lack the computer skills
they need for their current job.
Older workers are more likely to feel they lack the computer skills needed to do their job well, with 10% of 55-65 year-
olds expressing this concern compared to 2% of 16-24 year-olds. (Table B4.8). This finding is consistent with the
generally lower proficiency in problem solving in technology-rich environments that is observed among older adults
(see Chapter 3). In contrast, there is little variation by age in the perception that a lack of computer skills has affected the
chances of being hired or promoted or getting a pay raise (Table B4.9).

4
• Figure 4.5 •
Workers who reported insufficient computer skills, by the effect on employment
Percentage of workers (working at the time of the survey or had worked in the 12 months prior to it) who reported
that they lack the computer skills to do their job well
Japan
Korea
Norway
Finland
Denmark
Sweden
Average
Estonia
Flanders (Belgium)
Australia
Poland
Netherlands
Canada
Ireland
United States
Germany
Austria
Slovak Republic
Czech Republic
Lack the computer skills to do the job well among
those whose computer skills have affected
the chances of getting job/promotion/pay raise
Lack the computer skills to do the job well among
those whose computer skills have not affected
the chances of getting job/promotion/pay raise
%% 50 5010 1020 2030 3040 4000
Countries are ranked in descending order of the percentage of workers who reported a lack of computer skills to do the job well among those whose
computer skills have not affected the chances of getting a job/promotion/pay raise.
1 2http://dx.doi.org/10.1787/888933231682
Concern about having adequate computer skills also varies by the level of proficiency in problem solving in technology-
rich environments. On average, 5% of adults who perform at proficiency Level 2 or 3 in the assessment believe that
they lack the computer skills needed for their jobs, compared to 8% of adults who score below Level 1 or who did
not take the assessment on the computer (Table B4.10). However, there is little association between proficiency in the
domain and the perception that a lack of computer skills has affected the chances of being hired, promoted or paid more
(Table B4.11).
Relationships among adults’ problem-solving and ICT skills, frequency
of ICT use and various economic outcomes
The following sections examine how proficiency in problem solving in technology-rich environments, frequency of
ICT use, frequency of problem solving, and the level of adequacy of ICT skills for work are related to labour market
outcomes. The discussion in this first section focuses on the relationship of each of these variables with labour market
outcomes before accounting for other variables. The following sections examine the relationships after taking account of
other factors that are related to the outcomes.
Relationship with labour force participation
On average across OECD countries, 80% of adults aged 25-65 participate in the labour force.4 Some 90% of adults who
are proficient at Level 2 or 3 in the assessment of problem solving in technology-rich environments participate in the
labour force compared to 84% of those who are proficient at Level 1 and 76% of those who are proficient below Level 1
(Figure 4.6 and Table A4.6). There is notable variation among countries in the difference in labour force participation

4
rates between adults performing at Level 2 or 3 and those who perform below Level 1: from 5 percentage points in
Korea to 25 percentage points in the Netherlands. In most of the countries that are highly proficient in problem solving
in technology-rich environments (Denmark, the Netherlands, Norway and Sweden), the gap in the rates of labour
force participation between adults performing at Level 2 or 3 and those performing below Level 1 is relatively large. In
these countries, adults who score below Level 1 have lower rates of labour force participation while those with high
proficiency have higher rates of labour force participation compared to the OECD average.
The labour force participation rates of adults who failed the ICT core test (73%) or opted out of the computer assessment
(69%) are, on average, lower than that of adults who took the computer assessment. Only 47% of adults with no
computer experience participate in the labour market. All OECD countries show a wide gap between the labour force
participation rates for adults with no computer experience and the overall population, ranging from 12 percentage points
in Korea to 53 percentage points in Norway. The labour market seems to prefer workers who have some familiarity with
a computer. At the same time, those who are employed would also have more opportunities to develop or maintain
their skills in problem solving using ICT so the relationship between problem solving proficiency and labour force
participation like goes in both directions.
• Figure 4.6 •
Labour force participation, by problem-solving proficiency
Adults aged 25-65
Korea
Estonia
United States
Slovak Republic
Japan
Czech Republic
Germany
Australia
Austria
Canada
Poland
Average
Sweden
Norway
Ireland
Flanders (Belgium)
Denmark
Finland
Netherlands
Korea
Estonia
United States
Slovak Republic
Japan
Czech Republic
Germany
Australia
Austria
Canada
Poland
Average
Sweden
Norway
Ireland
Flanders (Belgium)
Denmark
Finland
Netherlands
Participation rate, by level
Participation rate
20 806040 100
Opted out
Level 1
Failed ICT Core
Below level 1
Level 2/3
Countries are ranked in ascending order of the difference in participation rates (Level 2/3 minus Below Level 1).
1 2http://dx.doi.org/10.1787/888933231693
Frequency of ICT use is also related to labour force participation. On average, 85% of 25-65 year-olds who use e-mail
at least once a month outside of work participate in the labour force, compared to only 66% of adults who use e-mail
less often or never (Figure 4.7). This difference ranges from 7 percentage points in Japan to 27 percentage points in
Finland.

4
• Figure 4.7 •
Labour force participation, by e-mail use in everyday life
Adults aged 25-65
Japan
Korea
United States
Germany
Sweden
Canada
Ireland
Australia
Average
Austria
Denmark
Norway
Czech Republic
Flanders (Belgium)
Slovak Republic
Netherlands
Estonia
Poland
Finland
Japan
Korea
United States
Germany
Sweden
Canada
Ireland
Australia
Average
Austria
Denmark
Norway
Czech Republic
Flanders (Belgium)
Slovak Republic
Netherlands
Estonia
Poland
Finland
Participation rate
Participation rate
100806040
Infrequent use of e-mail Frequent use of e-mail
Note: Frequent use of e-mail means using e-mail at least once a month.
Countries are ranked in ascending order of the unadjusted difference in participation rates (frequent minus infrequent use of e-mail).
1 2http://dx.doi.org/10.1787/888933231708
Relationship with unemployment
Across OECD countries, proficiency in problem solving in technology-rich environments is negatively correlated with
unemployment: adults who have the capacity to take the assessment have a lower rate of unemployment (4.6%) than
the average for all labour force participants (5.3%). Some 3.6% of labour force participants who perform at Level 2
or 3, 5.1% of those who perform at Level 1, and 6.2% of those who are proficient below Level 1 are unemployed
(Figure 4.8). By contrast, 7.8% of labour force participants who fail the ICT core test and 8.3% of participants who
have no computer experience are unemployed. A number of countries, including Estonia and the Slovak Republic have
particularly high levels of unemployment among adults who have no computer experience. The average unemployment
rate among adults who opt out of the computer assessment is 6.8%, close to the average for all labour force participants.
However, this pattern is not observed in a few countries. For example, in Korea, unemployment rates are generally low,
regardless of adults’ level of proficiency in problem solving in technology-rich environments. However, unemployment
rates among adults who perform at Level 2 or 3 are slightly higher than those among adults who perform at lower levels
of proficiency.
The overall unemployment rate is highly influenced by the economic conditions in each country, and it is likely that
economic conditions affect the unemployment rate differently for workers at different proficiency levels. Therefore,
when comparing unemployment rate results across countries it is important to remember that in 2011-2012, when the
data for the Survey of Adult Skills were collected, the countries participating in the survey were affected to different
degrees by the economic crisis.

4
• Figure 4.8 •
Unemployment rate, by problem-solving proficiency
Adults aged 25-65
Netherlands
Ireland
Slovak Republic
United States
Australia
Sweden
Austria
Estonia
Poland
Average
Japan
Germany
Canada
Flanders (Belgium)
Norway
Czech Republic
Denmark
Finland
Korea
Netherlands
Ireland
Slovak Republic
United States
Australia
Sweden
Austria
Estonia
Poland
Average
Japan
Germany
Canada
Flanders (Belgium)
Norway
Czech Republic
Denmark
Finland
Korea
Unemployment rate
Unemployment rate
0 15105 20
Opted out
Level 1
Failed ICT Core
Below level 1
Level 2/3
Countries are ranked in ascending order of the difference in unemployment rates (Level 2/3 minus Below Level 1).
1 2http://dx.doi.org/10.1787/888933231714
Frequency of ICT use is also somewhat related to unemployment. On average, 4.9% of labour force participants
aged 25-65 who use e-mail at least once a month in everyday life are unemployed, compared to 6.2% of labour force
participants who use e-mail less often or never (Figure 4.9). In some countries with relatively low unemployment rates,
this relationship is reversed: unemployment rates are higher among adults who use e-mail more frequently.
Relationship with wages
In all participating countries, higher levels of proficiency in problem solving in technology-rich environments are
associated with higher wages. On average across OECD countries, hourly wages for workers who perform at proficiency
Level 2 or 3 are 26% higher than mean hourly wages for workers who perform below Level 1 (Figure 4.10). This premium
ranges from 9% in Korea to 56% in the United States. Hourly wages for workers at proficiency Level 1 are 11% higher
than those of workers who perform below Level 1. Computer experience is also associated with wages. Hourly wages for
workers with no computer experience are 18% lower than those of workers with Below Level 1 proficiency, and range
from 9% in Sweden to 34% in Estonia. On average across OECD countries, the hourly wages for workers who failed the
ICT core test or who opted out of the computer assessment are close to those of workers who perform below Level 1 in
the assessment.
Frequency of ICT use has a strong relationship with wages. On average across OECD countries, hourly wages for workers
who use e-mail at work at least once a month are 51% higher than those of workers who do not use e-mail at work that
frequently (Figure 4.11). This difference in wages ranges from 24% in Sweden to 85% in the United States.

4
• Figure 4.9 •
Unemployment rate, by e-mail use in everyday life
Adults aged 25-65
Slovak Republic
Poland
Estonia
Czech Republic
Germany
Average
Ireland
Netherlands
Denmark
Canada
Sweden
Australia
Austria
United States
Flanders (Belgium)
Japan
Norway
Finland
Korea
Slovak Republic
Poland
Estonia
Czech Republic
Germany
Average
Ireland
Netherlands
Denmark
Canada
Sweden
Australia
Austria
United States
Flanders (Belgium)
Japan
Norway
Finland
Korea
Unemployment rate
Unemployment rate
0 15105
Infrequent use of e-mail Frequent use of e-mail
Note: Frequent use of e-mail means using e-mail at least once a month.
Countries are ranked in ascending order of the difference in unemployment rates (frequent minus infrequent use of e-mail).
1 2http://dx.doi.org/10.1787/888933231728
• Figure 4.10 •
Wage premium, by problem-solving proficiency
Percentage difference in mean hourly wages relative to Below Level 1, by problem solving in technology-rich environments levels
United States
Slovak Republic
Estonia
Poland
Czech Republic
Germany
Ireland
Austria
Average
Canada
Netherlands
Norway
Australia
Japan
Sweden
Finland
Flanders (Belgium)
Denmark
Korea
United States
Slovak Republic
Estonia
Poland
Czech Republic
Germany
Ireland
Austria
Average
Canada
Netherlands
Norway
Australia
Japan
Sweden
Finland
Flanders (Belgium)
Denmark
Korea
Wage premium (compared to Below Level 1)
Wage premium
-0.6 -0.4 -0.2 0.40.20 0.6 0.8 1.0
Opted out
Level 1
Failed ICT Core
Level 2/3
Countries are ranked in descending order of the wage premium for Level 2/3.
1 2http://dx.doi.org/10.1787/888933231738

4
• Figure 4.11 •
Wage premium associated with e-mail use at work
Percentage difference in mean hourly wages between frequent* and less frequent use of e-mail at work
United States
Netherlands
Canada
Germany
Japan
Ireland
Slovak Republic
Poland
Average
Korea
Australia
Austria
Czech Republic
Estonia
Flanders (Belgium)
Norway
Denmark
Finland
Sweden
United States
Netherlands
Canada
Germany
Japan
Ireland
Slovak Republic
Poland
Average
Korea
Australia
Austria
Czech Republic
Estonia
Flanders (Belgium)
Norway
Denmark
Finland
Sweden
Wage premium
Wage premium
0.0 0.70.6 0.80.50.40.30.20.1 0.9
* Frequent use refers to use of e-mail at least once a month; less-frequent use refers to use of e-mail less than once a month or never.
Note: All differences are statistically significant.
Countries are ranked in descending order of the wage premium for workers using e-mail at work frequently.*
1 2http://dx.doi.org/10.1787/888933231745
Engaging in complex problem solving at work is also associated with higher wages. On average across OECD countries,
hourly wages for workers who engage in complex problem solving at work at least once a month are 34% higher than
those of workers who do not engage in this activity that frequently (Figure 4.12). This difference in wages ranges from
19% in Flanders (Belgium) to 53% in England/N. Ireland (UK).
Across participating countries, believing that one lacks the computer skills necessary to do one’s job does not have a
clear relationship with wages. On average across OECD countries, there is no wage penalty for workers who believe
that they lack the computer skills necessary for their jobs (Table A4.13). Consistent with expectations, workers who
use computers but believe they lack the necessary computer skills for their jobs are paid at least 10% less than
workers who believe they have the necessary skills in the Czech Republic, the Slovak Republic and Japan where
statistically significant differences are found. In Norway, the opposite is observed as workers who believe that they
lack the computer skills to do their jobs are paid 6% more than workers who say they have the skills necessary to
do their jobs.
A clearer relationship is found between wages and having employment difficulties due to inadequate computer skills.
On average across OECD countries, workers who report that their limited computer skills have caused difficulties
in being hired, promoted or paid more are paid 10% less than workers who have not encountered such difficulties
(Figure 4.13). In England/N.Ireland (UK), Germany and Ireland workers who report having employment difficulties due
to limited computer skills are paid 15% less than workers who report having encountered no such difficulties from a
lack of computer skills.

4
• Figure 4.12 •
Wage premium associated with regular use of complex problem-solving skills
Percentage difference in mean hourly wages between frequent* use of complex problem-solving skills and less frequent use
of those skills at work
Germany
United States
Slovak Republic
Canada
Japan
Netherlands
Austria
Ireland
Australia
Average
Estonia
Poland
Korea
Denmark
Czech Republic
Norway
Finland
Sweden
Flanders (Belgium)
Germany
United States
Slovak Republic
Canada
Japan
Netherlands
Austria
Ireland
Australia
Average
Estonia
Poland
Korea
Denmark
Czech Republic
Norway
Finland
Sweden
Flanders (Belgium)
Wage premium
Wage premium
0.0 0.4 0.50.3 0.60.20.1
* Frequent use refers to the use of complex problem-solving skills at least once a month; less-frequent use refers to the use of complex problem-solving
skills less than once a month or never.
Note: All differences are statistically significant. Complex problems are defined as those that take at least 30 minutes to find a good solution.
Countries are ranked in descending order of the wage difference between workers who frequently use complex problem-solving skills and workers who
use those skills less often or never.
1 2http://dx.doi.org/10.1787/888933231750
Relationships among adults’ problem-solving and ICT skills, frequency of ICT
use and various economic outcomes, after accounting for other factors
As the preceding sections show, there are clear associations between the various measures related to proficiency in
problem solving in technology-rich environments and ICT use and labour market outcomes. However, it is also well-
documented that such outcomes also tend to be affected by workers’ socio-demographic characteristics, such as age,
educational attainment and work experience. To adjust for the effect of these other factors, the analyses in this section
take account of the following characteristics of workers: age, educational attainment, gender, marital status, immigrant
status, and work experience.
In order to identify the relationships between proficiency in problem solving in technology-rich environments and the
use of ICT and economic outcomes, after accounting for the influence of other factors, the relationships are modelled
in several stages. Version 1 analyses proficiency in problem solving in technology-rich environments and membership
in the different groups of adults who did not take the assessment on the computer as a function of socio-demographic
characteristics alone. Version 2 takes account of proficiency in literacy and numeracy, as measured in the Survey of
Adult Skills, in order to distinguish proficiency in problem solving using ICT from other types of cognitive proficiency.
Version 3 adds the frequency of e-mail use to distinguish proficiency in problem solving using ICT from simple use
of ICT.5 For the wage regression, Version 3 also adds the other factors related to problem solving in technology-rich
environments: how frequently adults solve complex problems at work, and the two measures related to the adequacy of
computer skills for work. Version 4 adds measures of skills use that are not related to problem solving in technology-rich
environments – specifically, measures of the use of reading, writing and numeracy skills6 – to distinguish the use of ICT
skills from the use of skills in general. Finally, for the wage regression, Version 5 also accounts for occupation.

4
• Figure 4.13 •
Wage premium associated with reported employment difficulties due to lack of computer skills
Percentage difference in mean hourly wages between adults who reported employment difficulties due to lack
of computer skills and adults who reported no effect on their employment
Germany
Ireland
Austria
Korea
Estonia
United States
Sweden
Canada
Average
Flanders (Belgium)
Norway
Australia
Netherlands
Slovak Republic
Finland
Denmark
Japan
Czech Republic
Poland
Germany
Ireland
Austria
Korea
Estonia
United States
Sweden
Canada
Average
Flanders (Belgium)
Norway
Australia
Netherlands
Slovak Republic
Finland
Denmark
Japan
Czech Republic
Poland
Wage premium
Wage premium
-0.2 0.10.0-0.1 0.2
Note: Statistically significant differences are marked in a darker tone.
Countries are ranked in descending order of the wage premium associated with a lack of computer skills causing employment difficulties, compared to a
lack of computer skills having no effect on employment.
1 2http://dx.doi.org/10.1787/888933231764
The regressions are estimated for each country and the resulting country coefficients are averaged across all participating
OECD countries to produce OECD average coefficients. As in Chapter 3, the discussion focuses on the OECD average
results because there are relatively few statistically significant differences between the individual country estimates and
the OECD average.
Relationships with labour force participation, after accounting for other factors
Proficiency in problem solving in technology-rich environments is positively related to greater labour force participation
when socio-demographic factors are accounted for (Version 1), although the relationship is weaker than that observed
before taking these factors into account. After taking socio-demographic factors into account, the labour force
participation rate of adults who are proficient at Level 2 or 3 is 9 percentage points higher than that of adults who
are proficient below Level 1, and the participation rate of adults who are proficient at Level 1 is 4 percentage points
higher (Figure 4.14).7 However, these relationships are weakened further when proficiency in literacy and numeracy
are also taken into account (Version 2), although only the coefficient on numeracy is significant. This suggests that a
large part of the relationship between proficiency in the domain and labour force participation before taking account
of socio-demographic factors and literacy and numeracy proficiency reflects an association with numeracy proficiency
rather than problem solving in technology-rich environments. When adjusted for proficiency in literacy and numeracy,
the labour force participation rate of adults who are proficient at Level 2 or 3 in the domain is 5 percentage points
higher than that among adults who are proficient below Level 1, and there is no significant difference for adults who
are proficient at Level 1. The results for the analyses that add frequency of ICT use and the use of other types of skills
(Versions 3 and 4) are similar to the results for Version 2.8
There are also significant differences in labour force participation associated with whether or not respondents took
the assessment on the computer, after accounting for other factors. The largest effect is for adults with no computer

4
experience, whose labour force participation rate is 14-16 percentage points lower than that of adults proficient below
Level 1, after taking account of other factors. Results are similar in all four versions of the analysis. Adults who failed the
ICT core test have labour force participation rates that are 3-4 percentage points lower than adults who are proficient
below Level 1, after accounting for other factors, and adults who opted out of the computer assessment have participation
rates that are 4-5 percentage points lower.9
In the versions of the model that include ICT use, there are also significant differences in labour force participation
between adults who use e-mail at least once a month and adults who use e-mail less often or never. Adults who use
e-mail at least once a month have a participation rate that is 2-6 percentage points higher than adults who do not in most
countries, after other factors are accounted for (Versions 3-4). Flanders (Belgium), Japan and Sweden show a relationship
between ICT use and labour force participation that is significantly different from the OECD average and is usually not
significantly different from zero.
• Figure 4.14 •
How labour force participation is affected by problem-solving proficiency
and lack of computer experience
Differences in the rate of labour force participation between various groups, before and after accounting for various characteristics
Level 2/3 (minus Below Level 1)
Level 1 (minus Below Level 1)
Opted out (minus Below Level 1)
Failed ICT core (minus Below Level 1)
No computer experience (minus Below Level 1)
E-mail use in everyday life (at least once a month
use minus less than once a month use or never)
-30 -20 -10 0 10 20
Percentage points
Unadjusted
Version 2: Version 1+ literacy and numeracy
Version 3: Version 2 + E-mail use in everyday life
Version 1: Socio-demographic Version 4: Version 3 + reading/writing/numeracy
use in everyday life
Difference in the rate of labour force participation
n.s.
n.s.
n.s.
n.s.
n.s: not significant.
Note: Version 1 adjusts for socio-demographic characteristics (age, gender, foreign-born status, years of education and marital status). Version 2 adds
literacy and numeracy proficiency to the regression ofVersion 1.Version 3 adds frequency of ICT use (e-mail) in everyday life as an adjustment toVersion 2.
Version 4 adds use of reading/writing/numeracy skills in everyday life as an additional adjustment to Version 3.
Source: Survey of Adult Skills (PIAAC) (2012), Tables A4.6, A4.7 and A4.14.
1 2http://dx.doi.org/10.1787/888933231775
Relationships with unemployment, after accounting for other factors
After accounting for other relevant factors, the relationships between proficiency in problem solving in technology-rich
environments, ICT use and unemployment are no longer significant (Figure 4.15). Adults who are proficient at Level 2
or 3 in the domain have an unemployment rate that is significantly lower than that of adults who are proficient below

4
Level 1 only in the analysis that does not include proficiency in literacy and numeracy (Version 1). When literacy and
numeracy are taken into account, being proficient at Level 2 or 3 no longer has a significant relation with unemployment,
whereas the relationships with literacy and numeracy are significant (Versions 2-4).10 This suggests that a large part of
the relationship between proficiency in problem solving in technology-rich environments and unemployment, before
taking other factors into account, reflects an association with cognitive proficiency, in general, rather than proficiency
in this domain. Adults who are proficient at Level 1 and adults who did not take the assessment on the computer do not
have significantly different unemployment rates in any version of the analysis. In addition, e-mail use is associated with a
higher rate of unemployment when other types of skills use are not included (Version 3), but that relationship disappears
after also accounting for the use of reading, writing and numeracy skills outside of work (Version 4).
• Figure 4.15 •
How unemployment rates are affected by problem-solving proficiency
and lack of computer experience
Differences in the rate of unemployment between various groups, before and after accounting for various characteristics
E-mail use in everyday life (at least once a month
use minus less than once a month use or never)
-3 -2 -1 0 1 2 3
Percentage points
Unadjusted
Version 3: Version 2 + E-mail use in everyday life
Version 1: Socio-demographic Version 4: Version 3 + reading/writing/numeracy
use in everyday life
Difference in the rate of unemployment
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
Note: Version 1 adjusts for socio-demographic characteristics (age, gender, foreign-born status, years of education and marital status). Version 2 adds
literacy and numeracy proficiency to the regression ofVersion 1.Version 3 adds frequency of ICT use (e-mail) in everyday life as an adjustment toVersion 2.
Version 4 adds use of reading/writing/numeracy skills in everyday life as an additional adjustment to Version 3.
Source: Survey of Adult Skills (PIAAC) (2012), Tables A4.7, A4.8 and A4.15.
1 2http://dx.doi.org/10.1787/888933231788
Relationship with wages, after accounting for other factors
After accounting for socio-demographic characteristics (Version 1), the relationship between proficiency in problem
solving in technology-rich environments and wages weakens (Figure 4.16): workers proficient at Level 2 or 3 in the domain
are paid 18% more than workers below Level 1, and workers proficient at Level 1 are paid 8% more (before accounting
for socio-demographic factors, the differences in wages are 26% and 11%, respectively). When literacy and numeracy
proficiency are also taken into account (Version 2), the two adjusted wage premiums shrink to 8% and 4%; and when use
of ICT, problem solving at work, and adequacy of computer skills are also taken into account (Version 3) they decrease

4
further to 4% and 1%. The wage premium for workers proficient at Level 1 is not significant once ICT use, problem solving
at work and computer adequacy are also accounted for (Version 3). The wage premium for workers proficient at Level 2
or 3 is no longer significant once the use of other skills is accounted for (Version 4), while the wage premiums associated
with literacy and numeracy proficiency are still statistically significant for the OECD average.11 The results of the analysis
indicate that the relationship between proficiency in problem solving in technology-rich environments and wages, before
accounting for these other factors, reflects general cognitive proficiency (particularly numeracy) and the various types of
skills use, rather than a relationship with proficiency in problem solving using ICT itself.12
• Figure 4.16 •
How wages are affected by problem-solving proficiency and lack of computer experience
Percentage differences in wages between various groups, before and after accounting for various characteristics
Computer workers without computer skills to do the job well
(vs computer workers with computer skills)
Computer workers whose skills have affected employment
(vs computer workers whose skills have not affected employment)
Frequency of problem solving
(Frequent vs infrequent complex problem-solvers)
Work e-mail use
(Regular users vs infrequent users)
-0.3 -0.2 -0.1 0.0 0.1 0.2 0.60.50.40.3
Percentage points
Problem-solvingproﬁciency
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
Unadjusted
Version 3: Version 2 + e-mail use, adequacy of ICT skills and complex
problem-solving frequency at workVersion 1: Socio-demographic
Version 4: Version 3 + reading/writing/numeracy use in everyday life
Version 5: Version 4 + occupation
Note: Version 1 adjusts for socio-demographic characteristics (age, gender, foreign-born status, years of education, marital status and years of experience).
Version 2 adds literacy and numeracy proficiency to the regression ofVersion 1.Version 3 adds the frequency of ICT use (e-mail) at work, the two adequacy
measures of computer skills for work and the frequency of complex problem solving at work as an adjustment to Version 2. Version 4 adds use of reading/
writing/numeracy skills at work as an additional adjustment to Version 3. Version 5 adds occupation as an additional adjustment to Version 4.
Source: Survey of Adult Skills (PIAAC) (2012), Tables A4.10, A4.11, A4.12, A4.13 and A4.16.
1 2http://dx.doi.org/10.1787/888933231799

4
Belonging to two of the categories of workers who did not take the computer assessment has a significant negative
relationship with wages, after other factors are taken into account. The wages of workers who opted out of the computer
assessment are 3-7% lower than those of workers who perform below Level 1, with the negative wage effect similar
across all versions of the model. The wages of workers with no computer experience are 12-13% lower than those
of workers with proficiency below Level 1, before ICT use and the other variables related to problem solving in
technology-rich environments are taken into account (Versions 1-2) and 4-6% lower after those variables are accounted
for (Versions 3-5).13 There is no significant difference between the wages of workers who failed the ICT core test and
workers who perform below Level 1 on the assessment.14
When ICT (e-mail) use is added to the analysis (Version 3), it is associated with a wage premium of 15%, which is substantially
smaller than the difference of 51% observed before taking other factors into account. Also accounting for the use of reading,
writing and numeracy skills (Version 4) reduces the adjusted wage premium for e-mail use to 10%.15 Engaging in complex
problem solving at work is associated with a wage premium of 8% (Version 3), which is reduced to 6% after taking account of
the use of reading, writing and numeracy skills (Version 4).16 These wage premiums for solving complex problems at work are
thus substantially less than the difference of 34% that was observed before taking other factors into account.
The two measures of adequacy of computer skills show some relationship with wages when other factors are considered.
With all of the factors taken into account, the wages of workers who believe they lack the necessary computer skills for
their job are 2% lower than those of workers who believe they do have the necessary skills (Version 5), although there
is not a significant effect in the analyses without taking into account the use of reading, writing and numeracy skills
or occupation (Versions 3-4).17 The wages of workers who have had employment difficulties because of their limited
computer skills are 6% lower than those of workers who have not had such difficulties (Versions 3-5).18
Overall, the wage analysis shows several relationships between computer use and wages, including negative wage effects
for workers who have no computer experience or who opt out of the computer assessment, and positive wage effects for
workers who use e-mail at least once a month. Solving complex problems at work also has a positive relationship with
wages after other factors are taken into account. Proficiency in problem solving in technology-rich environments does
not show a relationship with wages that is distinct from general cognitive proficiency as measured by the literacy and
numeracy assessments.
Relationship with labour productivity
Across countries, there is a relationship between average labour productivity and a country’s average proficiency in
problem solving in technology-rich environments and using e-mail frequently (Figures 4.17 and 4.18).19 The proportion
of workers who are proficient at Level 2 or 3 explains 41% of the variation in labour productivity, while the proportion
of workers who use e-mail at work at least once a month explains 48% of that variation. When proficiency in problem
solving in technology-rich environments and e-mail use are used together to explain cross-country differences in labour
productivity, the addition of proficiency in the domain does not help to explain the variation any more than e-mail use
alone does, since e-mail use, itself, explains much of the variation of proficiency in problem solving in technology-rich
environments.These simple correlations at the country level do not imply a direct causal relationship between proficiency
in the domain, ICT use and labour productivity. Proficiency in problem solving in technology-rich environments and ICT
use are only used as proxies of a complex set of factors reflecting the mix of occupations, industries and work practices
that are themselves significant determinants of aggregate labour productivity. Still, these relationships do exist at the
country level. In contrast, country averages of proficiency in literacy and numeracy are not correlated with average
labour productivity, although there is a correlation with the use of reading skills.
The complex relationship between problem solving using ICT and labour
market outcomes
The analyses above suggest that computer use is closely associated with labour market outcomes. Adults who lack
computer experience are less likely to participate in the labour force and are paid lower wages than those who have
experience with computers. In addition, adults who use e-mail at least once a month at home are more likely to
participate in the labour force; and those who use e-mail at least once a month at work are paid higher wages. These
relationships remain significant even after accounting for the use of other types of information-processing skills. Although
it is unclear whether frequent computer use results in better work outcomes or vice versa – since computer experience
is now required for many jobs, but many jobs also provide adults with opportunities to gain computer experience – the
results show a clear link between work and computer use.

4
• Figure 4.17 •
Labour productivity and high performance in problem solving in technology-rich environments
In GDP per hour worked, percentage of workers scoring at Level 2 or 3 in problem solving in technology-rich environments
LabourproductivityGDPperhourworked
USD,currentprices,currentPPPs(2012)
30 3520 25 40 45 50
Percentage of workers at Levels 2 or 3 in problem solving in technology-rich environments
R² = 0.4190
80
70
60
50
40
30
20
Norway
United States
GermanyIreland
Austria
Denmark
Netherlands
Sweden
Finland
Australia
Canada
Czech Republic
Estonia KoreaPoland
Slovak Republic
Japan
Source: Survey of Adult Skills (PIAAC) (2012), Table A4.1 and OECD.Stat.
1 2http://dx.doi.org/10.1787/888933231803
• Figure 4.18 •
Labour productivity and frequent use of e-mail
In GDP per hour worked, percentage of adults who use e-mail at least once a month at work
LabourproductivityGDPperhourworked
USD,currentprices,currentPPPs(2012)
55 6035 40 45 50 65 70 75
Percentage of adults using email at work at least monthly
R² = 0.4890
80
70
60
50
40
30
20
Norway
United States
GermanyIreland
Austria
Denmark Netherlands
Sweden
Finland
Australia
Canada
Czech Republic
EstoniaKoreaPoland
Slovak Republic
Japan
Source: Survey of Adult Skills (PIAAC)(2012), Table A4.2a and OECD.Stat.
1 2http://dx.doi.org/10.1787/888933231813

4
The relationship between proficiency in problem solving using ICT and work is more complex.The relationships between
higher proficiency in this skill and all three labour market outcomes are significant after accounting for only socio-
demographic factors. However, when proficiency in literacy and numeracy are accounted for as well, there is no longer
a significant relationship with unemployment, and when the use of non-ICT skills are accounted for, the relationship
with wages is no longer significant either.20
The analyses reinforce the finding from Chapter 3 that there are important areas of commonality across the three different
proficiency measures. What matters for labour market outcomes, in part, is cognitive proficiency, in general, more
than the different areas of cognitive proficiency, as measured in the three different assessments – literacy, numeracy
and problem solving in technology-rich environments – in the Survey of Adult Skills. In addition, the higher levels of
proficiency in literacy and numeracy include an element of problem solving that is somewhat similar to the kind of
problem solving assessed in the survey.
The analyses also suggest that proficiency in problem solving using ICT has a closer relationship with the use of related skills
than either literacy or numeracy proficiency does. When considering labour force participation and wages, accounting for
skills use reduces the strength and significance of the relationships with proficiency in problem solving in technology-rich
environments, for both the use of ICT skills and the use of reading, writing and numeracy skills. In contrast, the associations
with proficiency in literacy and numeracy are not affected by accounting for skills use. A contrast between proficiency
in problem solving in technology-rich environments and proficiency in literacy and numeracy is also seen in the cross-
country correlations with labour productivity, where proficiency in problem solving using ICT is correlated with labour
productivity, but proficiency in literacy and numeracy are not. In this way, proficiency in problem solving in technology-
rich environments is similar to the skills-use variables – both ICT skills and reading skills – which are correlated with labour
productivity.These relationships with skills use are an important way that proficiency in problem solving in technology-rich
environments differs from proficiency in literacy and numeracy.
The relationship between proficiency in problem solving in technology-rich environments and the use of skills may
reflect the way that adults developed this proficiency. Proficiency in these skills includes both the cognitive skills
necessary to solve problems and the ability to use digital devices and functionality to access and manage information.
Unlike proficiency in literacy and numeracy, which reflect years of development in formal education, many adults have
developed ICT skills largely on their own at work and at home, with informal help from family, friends and colleagues.
Since the demand for these skills in the labour market arose relatively recently, many adults have not had the opportunity
to develop them during formal education. As a result, the part of proficiency in this domain that is related specifically
to ICT skills is likely to be closely linked to opportunities and requirements for the use of these skills. And given the fact
that, for most adults, ICT skills are largely self-taught, it is precisely those adults with higher cognitive proficiency in
general who have had the capacity to develop proficiency in problem solving using ICT on their own, outside of formal
education. Over time, this relationship between general cognitive proficiency and skills use may weaken if more adults
acquire proficiency in the domain during their formal education – and that, in turn, may be necessary as proficiency in
problem solving in technology-rich environments becomes increasingly important, both at and outside of work.
Notes
1. “Non-workers” refers to adults who were not working at the time of the survey, or who have not worked in the 12 months prior to it.
2. Table B4.14 in Annex B. Skilled occupations include managers (ISCO 1); professionals (ISCO 2); and technicians and associate
professions (ISCO 3). Semi-skilled white-collar occupations include clerical support workers (ISCO 4); and service and sales workers
(ISCO 5). Semi-skilled blue-collar occupations include skilled agricultural, forestry and fishery workers (ISCO 6); craft and related trades
workers (ISCO 7); and plant and machine operators and assemblers (ISCO 8). Elementary occupations (ISCO 9) include cleaners,
labourers, and similar unskilled occupations.
3. A “computer” included a mainframe, desktop or laptop computer, or any other device, such as a cell phone or tablet, that can be
used to send or receive e-mail messages, process data or text, or find things on the Internet.
4. The analysis excludes adults below 25 years of age since many young adults are not yet in the labour force but still in school.
5. The results are similar for regressions that use a more comprehensive ICT use index that aggregates across the different ICT use
questions.
6. These measures are for skills use outside of work for the analyses of labour force participation and unemployment, and for skills use
at work for the analysis of wages.

4
7. There are few significant country differences in the size of these adjusted relationships.
8. The overall pattern of results is the same if only literacy or numeracy alone is used in Version 2, instead of both used together.
In addition, if the frequency of ICT use and the use of other types of skills is added to the model before literacy and numeracy, the
relationship between proficiency and labour force participation is still substantially weakened by the addition of literacy and numeracy,
not by the addition of the various measures of skills use.
9. For all four versions of the model, the relationship between failing the ICT core and labour force participation is significantly weaker
than the OECD average in the Czech Republic and Ireland, and is not significantly different from zero in either country. For all four
versions of the model, the relationship between opting out and labour force participation is significantly weaker than the OECD average
in the Czech Republic and is not significantly different from zero. In general, the overall pattern of results is the same if only literacy or
numeracy alone is used in Version 2, or if the various skills-use variables are added to the model before literacy and numeracy.
10. This result is not substantially affected by using literacy or numeracy alone in Version 2 instead of both together, or by adding the
various skills-use variables to the model before literacy and numeracy. For Denmark, in Versions 2-4, the relationship between Level 2
or 3 and unemployment is significantly different from the OECD average and positive, with the unemployment rate among workers who
are proficient at Level 2 or 3 higher than that among workers who perform below Level 1.
11. The overall pattern of results inVersions 2-5 is not substantially affected by using literacy or numeracy alone instead of both together,
except that the small remaining relationships between proficiency and wages in Versions 4 and 5 are still statistically significant when
only literacy or numeracy are used separately.
12. Hanushek et al. (2013) also find that the inclusion of literacy and numeracy in a wage analysis substantially reduces the strength
the relationship with proficiency in problem solving in technology-rich environments, and that the relationships between literacy and
numeracy and wages are stronger than the relationship between proficiency in problem solving in technology-rich environments and
wages. Their analysis does not consider the additional effect of skill use on these relationships.
13. In some versions of the models for the Czech Republic and Sweden, the wage penalty for having no computer experience is
significantly smaller than the OECD average and not significantly different than zero. In some versions of the models, Ireland has a
reversed relationship, with workers who have no computer experience receiving a significant wage benefit compared to workers who
perform below Level 1.
14. The relationship between failing the ICT core and wages is significantly different in some countries than the OECD average. In some
versions of the model, the Slovak Republic or Sweden are significantly different than the OECD and show a significant wage benefit,
with workers who fail the ICT core receiving higher wages than those who perform below Level 1. In some versions of the model,
Estonia and Korea are significantly different than the OECD and show a significant wage penalty, with workers who fail the ICT core
receiving lower wages that those who perform below Level 1.
15. In Version 4, Sweden has a wage benefit associated with email use that is significantly smaller than the OECD average and is not
significantly different than zero; in Version 5, this is true for Finland and Norway, in addition to Sweden.
16. In some versions of the model, Flanders (Belgium), Japan and Ireland have a wage benefit from engaging in complex problem
solving at work that is significantly smaller than the OECD average and is not significantly different than zero.
17. In all versions of the model, Canada has a relationship between workers’ beliefs that they lack the necessary computer skills for their
job and wages that is significantly different than the OECD average and in the opposite direction: on average, workers in Canada who
believe they lack the necessary computer skills receive higher wages than similar workers who do not believe they lack the necessary
computer skills.
18. Denmark and the Slovak Republic are significantly different than the OECD average in some versions of the analysis and do not
show a significant wage penalty from employment difficulties related to limited computer skills.
19. Note that the measure of labour productivity used (GDP per hour worked) does not reflect the contribution of other productive
factors, unlike the analyses of wages.
20. For both unemployment and wages, there are significant relationships with either numeracy alone or with both literacy and
numeracy. So the lack of significance with respect to proficiency in problem solving in technology-rich environments is not simply a
reflection of the multicollinearity resulting from the use of several highly correlated measures of proficiency.
Reference
Hanushek, E., G. Schwerdt, S. Wiederhold and L. Woessmann (2013), “Returns to Skills Around the World: Evidence from PIAAC,”
OECD Education Working Papers, No. 101, OECD Publishing, Paris, http://dx.doi.org/10.1787/5k3tsjqmvtq2-en.

5
Some pointers for policy
In all countries, there are many adults who are not proficient in solving
problems using ICT; in most, some groups of adults are more likely
than others to struggle with these skills. This chapter suggests how
governments can help their citizens to develop these skills and what
governments should consider when designing e-government services.
The chapter also presents several case studies of countries in which large
proportions of the population are skilled in problem solving using ICT.

5
SOME POINTERS FOR POLICY
Given the widespread and growing presence of information and communication technology (ICT) in all areas of social
and economic life, as described in Chapter 1, it is important for adults to be able to manage information in digital
environments both at work and in daily life. The findings presented in Chapter 4 confirm the importance of these skills
by showing how proficiency in problem solving using ICT is related to such economic outcomes as employment and
earnings, while also showing that these relationships are sensitive to general cognitive proficiency and opportunities to
use skills, both at work and at home. Policy makers, businesses, and education providers thus need to be aware of adults’
proficiency in these 21st-century skills and to consider how they can help adults who have not yet developed these skills.
One of the major findings of this study is that there are many adults in all countries that participated in the 2012 Survey of
Adult Skills, a product of the OECD Programme for the International Assessment of Adult Competencies (PIAAC), who do
not possess sufficient skills in managing information in digital environments and are not comfortable using ICT to solve the
kinds of problems that they are likely to encounter at work or in everyday life (see Chapter 2). This could slow the uptake of
digital technologies at work, limit the utility of electronic platforms that deliver services, whether public (e.g. e-government,
e-education) or private (e-commerce), and create inequalities in access to the digital world.
While the large number of adults with low proficiency in these skills is worrying, many adults, in all countries, have
acquired greater proficiency in these skills over the past decade or two. It is only comparatively recently that the general
public has been regularly exposed to technology-rich environments and expected to become proficient in problem
solving using ICT. In historical terms, the acquisition of these skills by so many people, in such a short time is remarkable,
even if considerable inequalities still exist in both access to digital technologies and proficiency in using them.
Adults with low proficiency in problem solving using ICT
In all countries, low proficiency in problem solving in technology-rich environments is concentrated in certain groups of
the population. Adults who are aged 55-65 years, adults with less than upper secondary education, adults with neither
parent having attained upper secondary education, foreign-born adults who did not grow up speaking the language(s)
in which the Survey of Adult Skills was delivered, and adults with low proficiency in literacy are particularly at risk of
performing poorly in the problem-solving assessment.
The proportion of adults without any computer experience is of particular concern. Overall, 8% of adults in the OECD
countries that participated in the survey have no computer experience. Again, certain groups are much more likely
than others to lack computer experience. For example, 22% of adults aged 55-65, 21% of adults with less than upper
secondary education, 19% of adults with neither parent having attained upper secondary education, and 13% of
foreign-born, foreign-language adults have no computer experience. Lack of computer experience is associated with
substantially lower labour force participation and wages, even after accounting for other relevant factors.
The fact that a relatively large proportion of adults either has low proficiency in problem solving in technology-rich
environments or lacks familiarity with ICT and computers poses significant challenges to governments. Governments
need to ensure broader access to digital technologies and networks and provide opportunities for adults with no or low
skills in this domain to develop their proficiency. Governments also need to consider the level of their population’s skills
when developing initiatives to deliver services and information through digital technologies and networks. For example,
initiatives designed to make the Internet the default medium of access to and interaction with public administrations may
run the risk of excluding certain subgroups of the population unless alternative access points are provided and websites
are designed to be used by adults with low literacy, numeracy or ICT skills.
Some countries may face special challenges that need to be addressed in particular ways. For example, countries with
large immigrant populations – such as Canada and Sweden – may have a particularly large portion of their population
with limited proficiency in problem solving in technology-rich environments that is foreign-born with foreign language.
For such countries, it may be important to develop policies to increase proficiency in problem solving in technology-rich
environments that reflect the special circumstances of their specific immigrant populations.
The importance of access to and use of ICT and problem-solving skills
at work
Increasing access to ICT
In order to develop the skills in managing information in technology-rich environments that are measured by the Survey
of Adult Skills, adults must first have access to computers and the Internet. It is striking that a simple measure of access to
the Internet explains one third of the variation in proficiency in problem solving in technology-rich environments across

5
countries (see Chapter 2). Ensuring that all citizens have access to ICT is a necessary, though of course not sufficient,
condition for ensuring that these skills are developed across the population. Thus governments should adopt policies that
maximise access to ICT and connectivity to information networks.
Government policy can play an active role in promoting access to ICT and the Internet, as has been seen throughout the
introduction of broadband technologies. For example, over the past decade OECD countries have adopted policies that
structure the market for broadband service, including policies to remove barriers to entry by competing firms and to provide
tax incentives to suppliers for new investments. The regulatory framework that governs the provision of telecommunications
services is a key determinant of access to digital networks through its influence on the price and quality of the ICT services
that are available to the public and the affordability of ICT access. In addition, governments have encouraged the adoption of
broadband through programmes to increase awareness of the technology and policies to provide incentives to specific groups
of users, such as disabled people, unemployed individuals, rural residents and new PC owners. Such policies are likely to
have led to substantial increases in the rate of broadband takeup (OECD, 2008). For example, the government of Canada
undertook a number of projects and initiatives to increase ICT access for Canadians in rural and remote communities.1
Governments could also expand access by making computers and digital networks available in public institutions, such
as existing government offices that interact with the public, including libraries, post offices, medical and social services,
tax offices, and schools and universities. These institutions already use ICT in their operations, and they often provide
ways for citizens to use their services on line or with computer kiosks. For example, Figure 1.5 in Chapter 1 shows an
estimate of the proportion of adults who use the Internet to interact with public authorities in some way. Government
institutions could build on this by identifying adults who do not access services using ICT and providing assistance for
them to do so; and government agencies that interact with the public could take a more active role in encouraging and
supporting the adults who are not yet comfortable using ICT.
This approach of government actively providing access to ICT and encouraging the use of it is similar to the role that
some governments have played in making ICT available in compulsory education and encouraging teachers to use
the technology to improve instruction. Box 5.1 describes the role that the government has played in Korea to provide
ICT access in the public schools. The Korea case underlines the importance of providing both technology access and
appropriate support to encourage its use, since access is necessary but not sufficient to encourage the development of
proficiency in problem solving using ICT.
Policies to encourage greater use of ICT and problem-solving skills
When it comes to developing ICT skills, use is as important as access. As discussed in Chapter 3, there is a clear relationship
between ICT use and proficiency in problem solving in technology-rich environments, both across and within countries.The
association of proficiency in this domain with frequency of ICT use reinforces the common observation that many people
acquire proficiency in these skills informally, through trial and error and with the help of family, friends and colleagues. Part
of the relationship between ICT use and proficiency in problem solving using ICT stems from the opportunities to develop
skills that regular ICT use affords. Across all countries, the proportion of adults who use e-mail regularly is roughly double
the proportion of adults who perform at high levels in problem solving in technology-rich environments. Regular use of
ICT both at and outside of work is likely to improve proficiency in these skills by providing more opportunities to solve
problems using the technology. Governments’ use of e-mail and Internet websites to communicate with citizens is likely to
encourage citizens who are less comfortable using ICT to develop their skills in this area.
But using ICT even daily will not necessarily improve an adult’s ability to solve problems in technology-rich environments:
higher-order cognitive skills are also required. As discussed in Chapter 4, workers who are confronted with complex
problems to solve at least once a month are more likely than other workers to be highly proficient in problem solving
using ICT. The Finnish working life 2020 programme2, the workplace innovation fund in Ireland, and the workplace
productivity project in New Zealand (Buchanan et al., 2010) all envisage a redesign of the working environment so that
workers can use their skills more.
Developing proficiency in problem solving using ICT in formal education
The analyses in Chapter 3 show that proficiency in problem solving in technology-rich environments is related to
education. Even after accounting for other factors, an individual with tertiary education is 13 percentage points more
likely to perform at Level 2 or 3 in the assessment than an adult who lacks upper secondary education. In addition, an
adult who recently participated in adult education and training is 7 percentage points more likely to perform at those
levels in the assessment than an adult who had not recently participated in adult education and training.

5
Yet formal education may not be the primary context in which these skills are developed. Education may lead to later
opportunities to develop proficiency in problem solving using ICT, or the level of education an adult attains may reflect
certain personal characteristics that also tend to be associated with greater proficiency in those skills. Still, formal education
helps to develop more sophisticated approaches to solving problems, including the capacity to assess the quality of
information gathered from different sources and synthesize that information into a coherent whole. Educational settings are
also likely to develop proficiency in the more difficult aspects of computer programmes – such as the spreadsheet and word
processing programmes that are a focus of the assessment of problem solving in technology-rich environments.
The PISA 2012 report on problem solving (OECD, 2014) discusses some possible approaches to improving 15-year-old
students’ skills in problem solving, including encouraging teachers and students to reflect on solution strategies when
dealing with subject-specific problems in the classroom. When teachers ask students to describe the steps they took
to solve a problem, they encourage students’ metacognition, which, in turn, improves general problem-solving skills.
Problem-solving skills cannot be taught in a traditional classroom setting alone where a set of rules-based solutions
are taught. As Levy (2010) argues, when solutions are taught in classes, it is difficult to improve students’ ability to
solve unforeseen problems in real life. Exposure to diverse real-world problems and contexts seems to be essential for
developing problem-solving skills. Countries can also do more to improve students’ access to ICT at school. Across
OECD countries, PISA reports that only two in three 15-year-olds attend schools where there is adequate access to
computers for instruction (OECD 2013, Vol. IV, Figure IV.3.8).
Adult education and training is another promising route for developing proficiency in problem solving in technology-
rich environments. Among other benefits, adult education and training courses are usually much more accessible to
adults: they are generally offered in more flexible schedules and are specifically targeted to address the interests and
needs of their students. For example, adult learning courses can be targeted to help adults who have low proficiency
in these skills, while formal education tends to reach primarily younger adults who may already be very proficient. In
addition, adult education and training can be used to reach specific populations, such as older adults, immigrants or
adults with less formal education, who may already be receiving some support with targeted government programmes.
Box 5.2 describes examples of adult education programmes offered in the Nordic countries – countries that show some
of the highest levels of proficiency in problem solving in technology-rich environments, particularly among older adults.
In addition, on-the-job training provided by employers, either in formal settings, such as training sessions or workshops,
or in informal settings, such as learning from supervisors or peers, is a good way to help employees to develop various
work-related skills as well as proficiency in problem solving using ICT. During on-the-job training, cognitive skills,
including problem-solving skills and ICT skills, can be both developed and used to do the job better, which can also be
beneficial for employers.
e-Government and proficiency in problem solving using ICT
For over a decade, many governments have been providing citizens with access to government services through e-mail
and the Internet. The move toward e-government has been prompted by the dual goals of decreasing cost and increasing
service (OECD, 2009). Using ICT can allow government agencies to function more efficiently internally while also
providing more coherent external interactions with the public. For example, between 2008 and 2013, Denmark showed
a remarkable increase in the use of the Internet for interacting with public authorities: in 2008, 49% of Danish adults
used e-government services; in 2013, 85% of adults did (see Figure 1.5 in Chapter 1).
However, in many countries, progress in expanding e-government has been limited by the public’s slow uptake. Results
from the Survey of Adult Skills provide one explanation for the slow pace of adoption: many adults do not have sufficient
proficiency in computer skills to feel confident in using e-government services.
An OECD report on the adoption of e-government services recommends that these services need to be more focused on user
needs in order to be successful (OECD, 2009). Among other things, the report recommends the use of a simple organisation
of e-government websites and common architectures across all content areas for navigation and search within websites. Such
changes would make it easier for people with low proficiency in computer skills to use e-government websites. Without such
effort, government services can create a digital divide among the citizens. Government policies need to be carefully designed
to bridge the gap between those with access to and the ability to use the services and those without such capacity.
Once a sufficient level of proficiency is reached among the population, governments can then begin to require
e-government use, which strongly encourages all adults to develop at least minimal levels of proficiency in problem
solving using ICT. Denmark has taken this approach with respect to some e-government services, including mandatory

5
registration of unemployed adults on a public website for job-seekers and mandatory use of electronic transfers for all
government payments (OECD, 2009, Box 3.32). This approach is only feasible in a country whose citizens have high
levels of proficiency in computer skills.
High-performing countries
The Nordic countries and the Netherlands show particularly high levels of proficiency in problem solving in technology-
rich environments, with many adults performing at Level 2 and 3 in the assessment and few adults who have no computer
experience. The high average performance in these countries is a reflection of the better results among the population
subgroups that tend to perform poorly in other countries. For example, fewer older or less-educated adults in these
countries have no computer experience, and more adults who have less-educated parents or who work in elementary
occupations perform at Level 2 or 3. The high average performance in the Nordic countries and the Netherlands tends
to reflect high performance across the full population, not just among particular groups.
The high levels of performance in problem solving in technology-rich environments in these countries is paired with
high levels of ICT use. Over 80% of adults in these countries use e-mail frequently, with most doing so daily. At the
same time, most of these countries show larger-than-average numbers of workers who have had difficulties in getting a
job or a promotion because of their limited computer skills. This suggests that, in these societies, there is a widespread
expectation that everyone will have some level of proficiency in these skills.
To some extent, the high performance in the Nordic countries and the Netherlands may be associated with achieving high
levels of access to computers and the Internet earlier than occurred in other countries. In 2005, 76% of the households
across these five countries had access to a computer at home – a proportion 17 percentage points larger than the OECD
average; and 69% of the households in the five countries had access to the Internet – a proportion 20 percentage points
larger than the OECD average.3 In addition, greater equity of opportunities in the access to formal education and adult
education and training, both at and outside of work, might have contributed to their high performance. When it comes
to developing skills in Nordic countries, socio-economic status matters little or not at all.
Box 5.1 Korea: The largest proportion of highly proficient young adults
Among all OECD countries, Korea has the largest proportion of 16-24 year-olds who scored at Level 2 or 3 (63.4%)
and the smallest proportion of young adults who scored below Level 1 (2.6%) in problem solving in technology-
rich environments in the 2012 Survey of Adult Skills. In a related finding, the OECD Programme for International
Student Assessment (PISA) shows that 15-year-old students in Korea are highly proficient in digital reading skills,
including evaluating information on the Internet, assessing its credibility, and navigating webpages. In fact, Korean
students performed significantly better in digital reading than in print reading, (as did students in Australia, Iceland,
Macao-China, New Zealand and Sweden) (OECD, 2011). In addition, 15-year-old Korean students also had the
highest performance in PISA’s computer-based creative problem-solving assessment among the 44 countries and
economies that participated in that assessment (OECD, 2014).
Considering that a high level of cognitive skills and frequent use of ICT are linked to high performance in problem
solving in technology-rich environments (see Chapter 3), it is not surprising to find that young Korean adults are
highly proficient in these skills. These young adults also performed very well in both literacy and numeracy in the
Survey of Adult Skills. Technology is pervasive in both public and private settings (for example, high-speed Internet
connections are available in subways and trains), so a certain level of ICT skills is required to conduct everyday
tasks. In universities, it is common to find students using their mobile devices to reserve library seats, mark their
attendance in classes, and check their grades.1
According to the Korea Internet and Security Agency (KISA, 2013), 99% of junior high and high school students use
the Internet more than once a day, spending an average of about two hours per day on line. Most Korean students use
computers and the Internet outside of school rather than at school, with only half of students reporting that they use
the Internet at school. Some 68% of 15-year-olds reported that they do not have time to use the Internet at school,
according to PISA 2012 results. Most Korean students reported that they use the Internet to search for information,
communicate with friends, and access educational content. More students access the Internet through mobile
devices, such as smartphones, tablet PCs and laptops. In fact, ownership of smart devices tripled among Korean
youth between 2011 and 2012, rising from 21% to 65% of young people who own such devices. As of 2013, about
85% of junior high and high school students owned smartphones, according to the Korean Ministry of Education.
...

5
The Korean government continues to invest in ICT in schools. In 2011, the Korean government launched the “Smart
Education” initiative, which aims to make digital versions of textbooks and assessments, increase the number of online
classes, promote the use of Internet Protocol Television in class, allow easy and free access to a variety of educational
materials, improve school infrastructure and standard platforms for a Smart Education cloud system, and strengthen
teacher competencies with training courses and smart devices (Ministry of Education, Science and Technology, 2011).
ICT is frequently used at the tertiary level of education. In 2001, the Ministry of Education and Human Resource
Development enacted the “Cyber University Foundation Law”, which spawned the creation of 17 cyber universities
by 2004 and another four by 2012. In addition, there are nine cyber graduate schools across the country, as of 2013,
offering distance and e-learning degree courses, such as MBAs, education and information-security programmes.2
Notes:
1. “In South Korea, All of Life is Mobile”, The New York Times, www.nytimes.com/2009/05/25/technology/25iht-mobile.html?pagewanted=all_r=0,
[accessed 26 November 2014].
2. Cyber University Statistics, available at www.cuinfo.net/home/eudc/statistics.sub.action?gnb=55, [accessed 9 September 2014].
Box 5.2 The Nordic Countries: High proficiency, particularly among older adults
Denmark, Finland, Norway and Sweden have the largest proportion of adults aged 16-65 who scored at Level 2 or
3 in problem solving in technology-rich environments, and the smallest proportion of adults who have no computer
experience or basic ICT skills among all the OECD countries that participated in the Survey of Adult Skills.The Nordic
countries have highly sophisticated ICT infrastructures in place that make it easy to access the Internet anywhere. In
2011, more than 85% of adults in Denmark, Finland, Norway and Sweden had access to the computer (Table B1.1),
and more than 85% of adults in those countries had access to the Internet. For example, almost 92% of Swedish
adults have access to a computer and about 91% have access to the Internet at home.
Participation in adult education and training is above 60%, on average across Nordic countries, with high rates
even among the least-skilled adults. ICT has been used as a tool to support and complement formal education,
giving learners access to educational resources anywhere, any time. Some 35 universities and university colleges
in Sweden offer distance higher education courses.1 Similarly, Norway offers online adult education and training
through the NKI Distance Education and through Norwaynet with IT for Open Learning (NITOL).2
There have been several policy efforts to increase participation in adult learning and training for disadvantaged
groups in Nordic countries. In Finland, study vouchers (Opintoseteli) are provided to cover the costs of developing
ICT skills among retirees, immigrants and unemployed adults. These groups can use vouchers to pay for any
courses in Adult Education Centres.3
The high average performance in problem solving in technology-rich environments that is observed among the Nordic
countries is a reflection of the high performance of older adults in these countries. This high proficiency among older
adults seems to be associated with high employment rates among these age groups. As the findings in this report suggest,
using ICT skills and other cognitive skills at work helps to maintain and develop these skills. For example, Norway has
one of the highest employment rates and the lowest unemployment rate among older adults among all OECD countries.
The Norwegian government works with business to establish policies that create comfortable working conditions for
older adults while reforming the pension system to provide stronger economic incentives for older people to remain
employed. When older adults stay longer in the labour force, they can learn new skills through colleagues or work-
based training. According to an employers’ survey conducted in 2011, 29% of Norwegian companies with 10 or more
employees reported that they offer training and career-development opportunities to older employees (Eironline, 2013).
In addition to high-performing older adults, less-educated adults and low-skilled workers with no computer experience
in the Nordic countries also performed relatively well in the assessment. In Denmark, adult vocational training
programmes (arbejdsmarkedsuddannelser or AMU) provide vocational training for both low-skilled and skilled
workers, as well as unemployed adults, immigrants and refugees. The programmes aim to improve vocational and
other skills, including ICT, literacy and numeracy skills. In 2006, 617 000 adults participated in these programmes.4
Notes:
1. Eurostat, extracted September 2014, Community Survey on ICT usage in households and by individuals, http://epp.eurostat.ec.europa.eu/tgm/
table.do?tab=tableinit=1plugin=1language=enpcode=tin00134
2. NITOL, available at www2.tisip.no/nitol/english/nitol.html [accessed 9 September 2014].
3. TrainingVouchers, available at www.hel.fi/www/sto/fi/opiskelu/maahanmuuttajat-immigrants/opintosetelit [accessed 9 September 2014].
4. Adult vocational training in Denmark, available at http://eng.uvm.dk/Education/Adult-Education-and-Continuing-Training/Adult-vocational-
training-in-Denmark [accessed 9 September 2014].

5
Notes
1. Statistics Canada (2008) found that significantly fewer Canadians in remote and rural areas have access to the Internet. As a response,
federal, provincial and territorial governments of Canada have undertaken a number of projects and initiatives to increase the use of
ICT in rural and remote communities. For example, Connecting Canadians, a plan to bring high-speed Internet to 280 000 Canadian
households as part of Digital Canada 150 (a comprehensive approach to ensure that all Canadian citizens can benefit from the digital
age) was launched in the summer of 2014. The government of Canada will be investing up to CAD 305 million over five years to extend
access to high-speed Internet (five megabits per second) to 98% of Canadian households, mainly in rural and remote communities.
www.ic.gc.ca/eic/site/028.nsf/eng/50009.html.
2. Working Life 2020 as part of Liideri programme, available at www.tekes.fi/en/programmes-and-services/tekes-programmes/liideri/.
3. OECD Key ICT Indicators, available at www.oecd.org/internet/broadband/oecdkeyictindicators.htm [accessed 1 August 2014].
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Ministry of Education, Science and Technology (2011), 스마트교육 추진 전략 실행계획 (Action plan for Korea’s Smart Education
Initiative), www.moe.go.kr/web/110501/ko/board/view.do?bbsId=348boardSeq=23930.
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OECD (2013), PISA 2012 Results: What Makes Schools Successful? (Volume IV): Resources, Policies and Practices, PISA, OECD
OECD (2011), PISA 2009 Results: Students on Line: Digital Technologies and Performance (Volume VI), PISA, OECD Publishing, Paris,
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org/10.1787/9789264059412-en.
OECD (2008), Broadband Growth and Policies in OECD Countries, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264046764-en.
Statistics Canada (2008), How Canadians’ Use of the Internet Affects Social Life and Civic Participation, Connectedness Series, No. 16,
Statistics Canada.
Wood, S. (2004), Fully on-the-job Training: Experiences and Steps Ahead, National Centre forVocational Education Research, Adelaide.

Annex A
TABLES OF RESULTS
All tables in Annex A are available on line.
•• Chapter 1 tables . . . . . . . . . . . . . . . . . . . 89
•• Chapter 2 tables . . . . . . . . . . . . . . . . . . . 92
•• Chapter 3 tables . . . . . . . . . . . . . . . . . . . 101
•• Chapter 4 tables . . . . . . . . . . . . . . . . . . . 110

Annex A: Tables of results

Tables of results: Annex A
[Part 1/1]
Table A1.1
Percentage of workers aged 16-74 who are in jobs that require solving unforeseen problems
or conducting routine tasks
Solving unforeseen problems Routine tasks
Austria 81.8 27.5
Belgium 83.6 44.7
Czech Republic 83.4 57.3
Denmark 92.9 39.5
Estonia 89.9 59.7
Finland 80.8 48.9
France 81.3 48.0
Germany 84.6 31.3
Ireland 77.9 53.0
Italy 74.0 42.0
Netherlands 93.5 24.4
Norway 91.5 25.3
Poland 84.9 43.0
Slovak Republic 75.2 43.6
Spain 82.8 58.3
Sweden 95.2 31.4
United Kingdom 84.5 59.4
Average 84.6 43.4
Source: European Working Conditions Survey, 2010.
1 2 http://dx.doi.org/10.1787/888933231824
[Part 1/1]
Table A1.2 Percentage of 25-64 year-olds who made online purchases, 2005 and 2013
2005 2013
Austria 26 58
Belgium 18 51
Czech Republic¹ 13 36
Denmark 51 81
Estonia³ 18 24
Finland 40 71
France2
36 62
Germany 47 74
Ireland 22 49
Italy 7 22
Netherlands 46 72
Norway 58 77
Poland 6 33
Slovak Republic 9 45
Spain 13 35
Sweden 54 77
United Kingdom 47 80
Average 30 56
Notes:
1. Year of reference 2006.
Source: Eurostat, Community Survey on ICT usage in households and by individuals.

[Part 1/1]
Table A1.3
Percentage of unemployed individuals aged 16-74 who used the Internet to look for a job or send
a job application
2005 2013
Austria 29 71
Belgium¹ 27 51
Czech Republic 10 40
Denmark 48 62
Estonia¹ 37 76
Finland 42 69
France¹ 35 67
Germany¹ 52 58
Ireland 2 48
Italy 15 41
Netherlands 32 81
Norway 38 80
Poland 8 33
Spain² 24 52
Sweden 78 90
United Kingdom² 46 64
Average 32 60
Notes:
[Part 1/1]
Table A1.4 Percentage of workers reporting frequent use* of technology, by sector of work, EU 27 average
ICT ICT and machinery Machinery No technology
Financial services 81 10 2 7
Education 67 4 2 27
Public administration
and defence 66 10 8 16
Health 55 10 5 30
Other services 52 10 9 30
Wholesale, retail, food
and accommodation 37 10 14 38
Industry 28 19 38 15
Transport 26 15 25 34
Construction 17 13 52 18
Agriculture 7 8 41 44
* Use is considered frequent if the technology is used more than 75% of the time.
Source: European Working Conditions Survey, 2010.

[Part 1/1]
Table A1.5 Percentage of individuals aged 16-74 who used the Internet to interact with public authorities
2008 2013
Australia² 38 m
Austria 51 54
Belgium 26 50
Canada¹ 46 m
Czech Republic 19 29
Denmark 49 85
Estonia 37 48
Finland 62 69
France 48 60
Germany 44 49
Ireland 34 45
Italy 20 21
Netherlands 61 79
New Zealand m 51
Norway 72 76
Poland 22 23
Spain 32 44
Sweden 59 78
Average 42 52
Notes:
Note: Within the 12 months prior to the surveys, for private purposes. Derived variable on use of e-government services. Individuals used the Internet for at least one of the
following: to obtain services from public authorities websites; to download official forms; and/or to send completed forms.
Data for Canada and New Zealand refer only to obtaining services from public authorities websites but does not include other activities such as downloading or completing
official forms.
Source: Eurostat, Community Survey on ICT usage in households and by individuals; OECD ICT database.

[Part 1/1]
Table A2.1 Tasks in the problem solving in technology-rich environments assessment
Proficiency level Score Item name Description
Level 1: 241-290
Tasks in which the
goal is explicitly stated
and for which a small
number of operations
are performed in
a single familiar
environment.
268 Club Membership – Member ID
Locate an item within a large amount of information in a multiple-column
spread-sheet based on a single explicit criterion; use e-mail to communicate
the result.
286 Reply All
With a defined goal and explicit criteria, use e-mail and send information to
three people.
286
Party Invitations –
Can / Cannot Come
Categorise a small number of messages in an e-mail application into existing
folders according to one explicit criterion.
Level 2: 291-340
Tasks that have explicit
criteria for success,
a small number of
applications, several
steps and operators,
and occasional
unexpected outcomes.
296
Club Membership –
Eligibility for Club President
Organise large amounts of information in a multiple-column spreadsheet using
multiple explicit criteria; locate and mark relevant entries.
299 Party Invitations Accommodations
Categorise a small number of messages in an e-mail application by creating a
new folder; evaluate the contents of the entries based on one criterion in order
to file them in the proper folder.
305 Digital Photography Book Purchase
Choose an item on a webpage that best matches a set of given criteria from
a search engine results page; the information can be made available only by
clicking on links and navigating through several webpages; based on a search
engine results page, navigate through several Internet sites in order to choose an
item on a webpage that best matches a set of given criteria.
316 CD Tally
Organise large amounts of information in a multiple-column spreadsheet and
determine a value based on a single explicit criterion; use a dropdown menu in
a novel Internet application to communicate the result.
320 Tickets
Use a novel Internet-based application involving multiple tools to complete an
order based on a combination of explicit criteria.
321 Lamp Return
Enact a plan to navigate through a website to complete an explicitly specified
consumer transaction. Monitor the progress of submitting a request, retrieving
an e-mail message, and filling out a novel online form.
325
Sprained Ankle –
Reliable / Trustworthy Source
Apply evaluation criteria and then navigate through multiple websites to infer
the most reliable and trustworthy site. Monitoring throughout the process is
required.
Level 3: 341 or more
Tasks involving
multiple applications, a
large number of steps,
occasional impasses,
and the discovery
and use of ad hoc
commands in a novel
environment.
342
Sprained Ankle –
Site Evaluation Table
Evaluate several entries in a search engine results page given an explicit set of
separate reliability criteria.
346 Meeting Rooms
Using information from a novel Internet application and several e-mail
messages, establish and apply criteria to solve a scheduling problem where an
impasse must be resolved, and communicate the outcome.
355 Local E-mail – File 3 E-mails
Infer the proper folder destination in order to transfer a subset of incoming
e-mail messages based on the subject header and the specific contents of each
message.
374 Class Attendance
Using information embedded in an e-mail message, establish and apply the
criteria to transform the e-mail information to a spreadsheet. Monitor the
progress of correctly organising information to perform computations through
novel built-in functions.

[Part 1/1]
Table A2.2 Percentage of adults scoring at each proficiency level in problem solving in technology-rich environments
Proficiency levels
No computer
experience Failed ICT core
Opted out of the
computer-based
assessment MissingBelow level 1 Level 1 Level 2 Level 3
OECD % S.E. % S.E. % S.E. % S.E. % S.E. % S.E. % S.E. % S.E.
National entities
Australia 9.2 (0.6) 28.9 (0.8) 31.8 (1.0) 6.2 (0.5) 4.0 (0.3) 3.5 (0.3) 13.7 (0.6) 2.7 (0.3)
Austria 9.9 (0.5) 30.9 (0.9) 28.1 (0.8) 4.3 (0.4) 9.6 (0.4) 4.0 (0.3) 11.3 (0.5) 1.8 (0.2)
Canada 14.8 (0.4) 30.0 (0.7) 29.4 (0.5) 7.1 (0.4) 4.5 (0.2) 5.9 (0.2) 6.3 (0.3) 1.9 (0.1)
Czech Republic 12.9 (0.9) 28.8 (1.3) 26.5 (1.1) 6.6 (0.6) 10.3 (0.5) 2.2 (0.3) 12.1 (0.8) 0.6 (0.2)
Denmark 13.9 (0.6) 32.9 (0.8) 32.3 (0.7) 6.3 (0.4) 2.4 (0.2) 5.3 (0.2) 6.4 (0.3) 0.4 (0.1)
Estonia 13.8 (0.5) 29.0 (0.7) 23.2 (0.6) 4.3 (0.4) 9.9 (0.3) 3.4 (0.2) 15.8 (0.4) 0.5 (0.1)
Finland 11.0 (0.5) 28.9 (0.8) 33.2 (0.7) 8.4 (0.6) 3.5 (0.3) 5.2 (0.3) 9.7 (0.4) 0.1 (0.1)
France m m m m m m m m 10.5 (0.3) 6.0 (0.3) 11.6 (0.4) m m
Germany 14.4 (0.8) 30.5 (0.8) 29.2 (0.8) 6.8 (0.6) 7.9 (0.5) 3.7 (0.4) 6.1 (0.5) 1.5 (0.2)
Ireland 12.6 (0.7) 29.5 (0.9) 22.1 (0.8) 3.1 (0.3) 10.1 (0.4) 4.7 (0.4) 17.4 (0.7) 0.6 (0.1)
Italy m m m m m m m m 24.4 (0.8) 2.5 (0.3) 14.6 (0.9) m m
Japan 7.6 (0.6) 19.7 (0.8) 26.3 (0.8) 8.3 (0.5) 10.2 (0.5) 10.7 (0.7) 15.9 (0.9) 1.3 (0.1)
Korea 9.8 (0.5) 29.6 (0.9) 26.8 (0.8) 3.6 (0.3) 15.5 (0.4) 9.1 (0.4) 5.4 (0.3) 0.3 (0.1)
Netherlands 12.5 (0.6) 32.6 (0.7) 34.3 (0.8) 7.3 (0.4) 3.0 (0.2) 3.7 (0.3) 4.5 (0.3) 2.3 (0.2)
Norway 11.4 (0.6) 31.8 (0.8) 34.9 (0.9) 6.1 (0.4) 1.6 (0.2) 5.2 (0.3) 6.7 (0.4) 2.2 (0.2)
Poland 12.0 (0.6) 19.0 (0.7) 15.4 (0.7) 3.8 (0.3) 19.5 (0.5) 6.5 (0.4) 23.8 (0.7) 0.0 (0.0)
Slovak Republic 8.9 (0.5) 28.8 (0.9) 22.8 (0.7) 2.9 (0.3) 22.0 (0.7) 2.2 (0.2) 12.2 (0.4) 0.3 (0.1)
Spain m m m m m m m m 17.0 (0.5) 6.2 (0.3) 10.7 (0.5) m m
Sweden 13.1 (0.5) 30.8 (0.8) 35.2 (0.9) 8.8 (0.6) 1.6 (0.2) 4.8 (0.3) 5.7 (0.3) 0.1 (0.0)
United States 15.8 (0.9) 33.1 (0.9) 26.0 (0.9) 5.1 (0.4) 5.2 (0.4) 4.1 (0.4) 6.3 (0.6) 4.3 (0.6)
Sub-national entities
Flanders (Belgium) 14.8 (0.6) 29.8 (0.8) 28.7 (0.8) 5.8 (0.4) 7.4 (0.3) 3.5 (0.3) 4.7 (0.3) 5.2 (0.2)
England (UK) 15.1 (0.8) 33.8 (1.1) 29.3 (0.9) 5.7 (0.5) 4.1 (0.3) 5.8 (0.4) 4.6 (0.4) 1.6 (0.2)
Northern Ireland (UK) 16.4 (1.5) 34.5 (1.2) 25.0 (1.2) 3.7 (0.6) 10.0 (0.6) 5.8 (0.4) 2.3 (0.3) 2.2 (0.3)
England/N. Ireland (UK) 15.1 (0.8) 33.9 (1.0) 29.1 (0.9) 5.6 (0.5) 4.3 (0.3) 5.8 (0.3) 4.5 (0.4) 1.6 (0.2)
Average1
12.3 (0.1) 29.4 (0.2) 28.2 (0.2) 5.8 (0.1) 8.0 (0.1) 4.9 (0.1) 9.9 (0.1) 1.5 (0.0)
Average-222
m m m m m m m m 9.3 (0.1) 4.9 (0.1) 10.2 (0.1) m m
Partners
Cyprus3
m m m m m m m m 18.4 (0.4) 1.9 (0.2) 18.0 (0.5) m m
Russian Federation4
14.9 (2.2) 25.6 (1.3) 20.4 (1.4) 5.5 (1.1) 18.3 (1.7) 2.5 (0.6) 12.8 (1.6) 0.0 (0.0)
1. Average of 19 participating OECD countries and entities.
2. Average of 22 OECD countries and entities: average of 19 countries with France, Italy and Spain.
3. See notes at the beginning of this Annex.
4. See note at the beginning of this Annex.
Source: Survey of Adult Skills (PIAAC) (2012).

[Part 1/1]
Table A2.3 Percentage of adults with high proficiency in problem solving in technology-rich environments
High proficiency
OECD % S.E.
National entities
Australia 38.0 (1.0)
Austria 32.5 (0.8)
Canada 36.6 (0.6)
Czech Republic 33.1 (1.1)
Denmark 38.7 (0.7)
Estonia 27.6 (0.7)
Finland 41.6 (0.7)
France m m
Germany 36.0 (0.8)
Ireland 25.3 (0.8)
Italy m m
Japan 34.6 (0.8)
Korea 30.4 (0.8)
Netherlands 41.5 (0.8)
Norway 41.0 (0.8)
Poland 19.2 (0.8)
Slovak Republic 25.6 (0.8)
Spain m m
Sweden 44.0 (0.7)
United States 31.1 (1.0)
Flanders (Belgium) 34.5 (0.8)
England (UK) 35.0 (0.9)
Northern Ireland (UK) 28.7 (1.3)
England/N. Ireland (UK) 34.8 (0.9)
Average1
34.0 (0.2)
Average-222
m m
Partners
Cyprus3
m m
Russian Federation4
25.9 (2.2)
Note: High proficiency is defined as scoring at Level 2 or 3 in problem solving in technology-rich environments.

[Part 1/1]
Table A2.4a Frequency of e-mail use in everyday life
Frequency of use
Never Less than once a month
Less than once a week
but at least once a month
At least once a week
but not everyday Everyday
OECD % S.E. % S.E. % S.E. % S.E. % S.E.
National entities
Australia 18.6 (0.6) 3.5 (0.3) 4.4 (0.3) 20.1 (0.6) 51.4 (0.7)
Austria 23.6 (0.6) 5.9 (0.4) 9.3 (0.5) 25.2 (0.6) 34.1 (0.6)
Canada 16.6 (0.4) 2.8 (0.2) 4.1 (0.2) 15.9 (0.4) 59.7 (0.5)
Czech Republic 24.6 (1.0) 1.8 (0.2) 3.7 (0.4) 23.1 (1.0) 46.2 (1.2)
Denmark 10.0 (0.4) 3.8 (0.2) 6.6 (0.4) 21.1 (0.6) 58.2 (0.6)
Estonia 21.9 (0.4) 2.6 (0.2) 4.9 (0.2) 18.9 (0.5) 51.3 (0.5)
Finland 13.8 (0.4) 4.4 (0.3) 7.8 (0.4) 29.6 (0.6) 44.4 (0.6)
France 24.6 (0.4) 3.3 (0.2) 4.0 (0.2) 16.1 (0.5) 51.2 (0.5)
Germany 20.1 (0.6) 5.5 (0.4) 7.1 (0.4) 24.2 (0.7) 41.7 (0.7)
Ireland 29.0 (0.5) 4.5 (0.3) 5.0 (0.3) 19.7 (0.6) 41.3 (0.6)
Italy 40.9 (0.8) 5.1 (0.4) 4.5 (0.4) 17.5 (0.8) 31.4 (0.8)
Japan 35.4 (0.7) 5.8 (0.3) 6.8 (0.4) 14.2 (0.5) 36.5 (0.7)
Korea 33.8 (0.6) 8.4 (0.4) 12.2 (0.4) 22.7 (0.5) 22.7 (0.6)
Netherlands 8.5 (0.4) 2.0 (0.2) 3.2 (0.2) 16.7 (0.6) 67.4 (0.6)
Norway 8.6 (0.4) 4.3 (0.3) 7.3 (0.4) 25.3 (0.6) 52.3 (0.7)
Poland 37.7 (0.5) 4.7 (0.3) 5.5 (0.3) 18.1 (0.5) 34.1 (0.5)
Slovak Republic 34.9 (0.6) 3.6 (0.3) 5.2 (0.3) 20.1 (0.6) 36.0 (0.6)
Spain 36.6 (0.6) 2.4 (0.2) 3.6 (0.3) 14.9 (0.6) 41.8 (0.7)
Sweden 10.9 (0.5) 4.7 (0.3) 7.1 (0.4) 23.8 (0.6) 53.4 (0.8)
United States 21.4 (0.7) 3.2 (0.4) 3.4 (0.4) 14.2 (0.5) 53.5 (1.0)
Flanders (Belgium) 14.9 (0.5) 3.5 (0.3) 3.9 (0.3) 21.3 (0.6) 51.1 (0.7)
England (UK) 17.2 (0.6) 4.4 (0.4) 5.4 (0.3) 21.9 (0.7) 49.7 (0.8)
Northern Ireland (UK) 27.8 (0.9) 6.4 (0.5) 7.1 (0.5) 19.2 (0.7) 37.3 (0.8)
England/N. Ireland (UK) 17.5 (0.6) 4.4 (0.3) 5.4 (0.3) 21.8 (0.6) 49.3 (0.8)
Average1
21.2 (0.1) 4.2 (0.1) 5.9 (0.1) 20.8 (0.1) 46.5 (0.2)
Average-222
22.9 (0.1) 4.1 (0.1) 5.7 (0.1) 20.2 (0.1) 45.9 (0.1)
Partners
Cyprus3
36.0 (0.6) 5.5 (0.3) 4.3 (0.3) 11.4 (0.5) 25.2 (0.6)
Russian Federation4
45.9 (2.5) 10.2 (0.9) 5.5 (0.5) 15.5 (1.2) 22.8 (1.8)

[Part 1/1]
Table A2.4b
Frequency of Internet use to better understand issues related to everyday life (e.g. health, financial
matters, or environmental issues)
Frequency of use
OECD % S.E. % S.E. % S.E. % S.E. % S.E.
National entities
Australia 20.2 (0.6) 6.3 (0.4) 9.1 (0.4) 27.9 (0.7) 34.6 (0.7)
Austria 21.5 (0.6) 5.9 (0.3) 13.0 (0.5) 31.1 (0.6) 26.7 (0.7)
Canada 18.0 (0.4) 6.6 (0.2) 10.7 (0.3) 27.5 (0.4) 36.1 (0.5)
Czech Republic 22.7 (1.1) 1.6 (0.3) 3.1 (0.4) 21.5 (0.7) 50.4 (1.2)
Denmark 11.3 (0.3) 7.5 (0.4) 13.8 (0.5) 30.8 (0.6) 36.3 (0.7)
Estonia 20.8 (0.4) 7.2 (0.3) 13.4 (0.4) 28.9 (0.5) 29.2 (0.5)
Finland 12.0 (0.4) 7.8 (0.3) 16.4 (0.4) 35.5 (0.7) 28.0 (0.6)
France 24.4 (0.5) 3.8 (0.2) 6.7 (0.3) 22.0 (0.5) 42.1 (0.5)
Germany 18.5 (0.6) 6.8 (0.4) 13.2 (0.5) 33.6 (0.6) 26.4 (0.7)
Ireland 30.0 (0.5) 6.7 (0.3) 9.5 (0.4) 24.1 (0.8) 29.2 (0.7)
Italy 40.2 (0.9) 9.3 (0.6) 8.6 (0.6) 19.9 (0.7) 21.4 (0.7)
Japan 35.4 (0.8) 15.0 (0.6) 17.9 (0.5) 20.3 (0.6) 10.2 (0.5)
Korea 29.3 (0.6) 9.8 (0.4) 19.9 (0.6) 28.0 (0.6) 12.7 (0.5)
Netherlands 12.0 (0.4) 8.7 (0.4) 14.2 (0.6) 29.2 (0.7) 33.6 (0.6)
Norway 8.7 (0.4) 7.4 (0.4) 15.6 (0.5) 35.5 (0.6) 30.5 (0.7)
Poland 34.3 (0.6) 6.0 (0.3) 8.5 (0.4) 21.6 (0.5) 29.5 (0.6)
Slovak Republic 34.7 (0.7) 6.8 (0.4) 7.5 (0.3) 23.0 (0.7) 27.6 (0.7)
Spain 37.3 (0.6) 4.9 (0.3) 7.9 (0.4) 20.4 (0.5) 28.7 (0.7)
Sweden 12.5 (0.5) 7.4 (0.4) 12.6 (0.4) 32.2 (0.7) 35.3 (0.7)
United States 21.9 (0.8) 6.6 (0.4) 10.4 (0.5) 23.8 (0.6) 33.1 (1.0)
England (UK) 19.1 (0.6) 9.2 (0.5) 13.3 (0.6) 28.3 (0.8) 28.7 (0.9)
Average1
21.0 (0.1) 7.4 (0.1) 12.4 (0.1) 28.1 (0.1) 29.7 (0.2)
Average-222
22.8 (0.1) 7.2 (0.1) 11.8 (0.1) 27.1 (0.1) 29.9 (0.1)
Partners
Cyprus3
33.1 (0.6) 7.4 (0.5) 6.8 (0.4) 15.2 (0.5) 19.9 (0.6)
Russian Federation4
41.8 (1.7) 11.0 (0.8) 10.1 (0.8) 16.6 (1.0) 20.3 (1.4)

[Part 1/1]
Table A2.4c Frequency of Internet use for conducting transactions (e.g. buying or selling products or services, or banking)
Frequency of use
OECD % S.E. % S.E. % S.E. % S.E. % S.E.
National entities
Australia 28.8 (0.7) 9.0 (0.5) 13.3 (0.6) 34.6 (0.8) 12.5 (0.4)
Austria 42.3 (0.7) 12.0 (0.6) 19.9 (0.5) 21.0 (0.6) 2.9 (0.2)
Canada 29.8 (0.5) 10.3 (0.3) 18.8 (0.4) 30.1 (0.5) 10.0 (0.3)
Czech Republic 37.6 (1.1) 14.8 (0.8) 20.7 (1.0) 22.0 (1.0) 4.2 (0.5)
Denmark 15.4 (0.4) 11.5 (0.4) 31.7 (0.6) 35.6 (0.6) 5.4 (0.3)
Estonia 24.7 (0.5) 7.8 (0.3) 33.0 (0.5) 28.2 (0.6) 5.7 (0.2)
Finland 15.5 (0.4) 5.3 (0.4) 31.9 (0.7) 44.8 (0.6) 2.4 (0.2)
France 39.4 (0.5) 19.7 (0.5) 21.3 (0.5) 15.0 (0.4) 3.7 (0.2)
Germany 35.0 (0.7) 14.2 (0.6) 21.3 (0.7) 23.7 (0.7) 4.3 (0.3)
Ireland 40.5 (0.7) 12.8 (0.5) 15.6 (0.5) 23.4 (0.7) 7.3 (0.4)
Italy 67.7 (0.8) 12.6 (0.6) 8.5 (0.5) 7.1 (0.4) 3.4 (0.4)
Japan 52.2 (0.6) 18.3 (0.5) 18.2 (0.5) 8.4 (0.4) 1.7 (0.2)
Korea 34.4 (0.6) 10.8 (0.4) 25.2 (0.6) 24.2 (0.5) 5.2 (0.3)
Netherlands 15.2 (0.5) 8.6 (0.4) 24.1 (0.6) 43.4 (0.8) 6.4 (0.4)
Norway 11.2 (0.5) 7.6 (0.4) 31.0 (0.6) 45.0 (0.7) 3.0 (0.2)
Poland 49.4 (0.6) 13.8 (0.5) 17.6 (0.5) 15.3 (0.5) 3.8 (0.3)
Slovak Republic 51.2 (0.8) 13.3 (0.5) 17.4 (0.6) 14.3 (0.5) 3.5 (0.4)
Spain 61.4 (0.7) 13.4 (0.5) 10.9 (0.4) 9.3 (0.4) 4.2 (0.3)
Sweden 16.4 (0.5) 8.7 (0.4) 47.0 (0.9) 25.6 (0.9) 2.2 (0.3)
United States 30.5 (0.9) 11.5 (0.6) 16.8 (0.5) 25.1 (0.7) 11.8 (0.6)
England (UK) 25.2 (0.6) 10.6 (0.5) 19.7 (0.7) 33.7 (0.8) 9.4 (0.6)
Average1
30.8 (0.1) 11.0 (0.1) 23.2 (0.1) 28.0 (0.2) 5.6 (0.1)
Average-222
34.3 (0.1) 11.6 (0.1) 21.9 (0.1) 25.6 (0.1) 5.3 (0.1)
Partners
Cyprus3
53.9 (0.6) 11.5 (0.5) 8.7 (0.5) 5.5 (0.4) 2.7 (0.3)
Russian Federation4
80.0 (1.1) 10.7 (0.8) 4.6 (0.4) 3.4 (0.4) 1.2 (0.2)

[Part 1/1]
Table A2.4d Frequency of spreadsheet software use (e.g. Excel)
Frequency of use
OECD % S.E. % S.E. % S.E. % S.E. % S.E.
National entities
Australia 62.1 (0.9) 17.4 (0.6) 10.2 (0.5) 6.5 (0.4) 1.9 (0.2)
Austria 57.2 (0.5) 20.1 (0.5) 11.9 (0.5) 7.2 (0.4) 1.8 (0.2)
Canada 57.4 (0.5) 18.9 (0.4) 11.2 (0.3) 8.8 (0.3) 2.8 (0.2)
Czech Republic 54.3 (1.0) 20.9 (1.0) 11.1 (0.8) 10.0 (0.8) 3.0 (0.4)
Denmark 50.5 (0.6) 22.4 (0.5) 15.0 (0.5) 9.2 (0.4) 2.5 (0.2)
Estonia 57.8 (0.5) 20.3 (0.4) 12.4 (0.3) 7.5 (0.3) 1.5 (0.1)
Finland 54.4 (0.6) 27.0 (0.6) 12.7 (0.5) 4.7 (0.3) 0.9 (0.1)
France 63.7 (0.5) 17.0 (0.4) 9.8 (0.3) 6.3 (0.3) 2.3 (0.2)
Germany 54.7 (0.7) 21.1 (0.6) 13.0 (0.6) 8.1 (0.5) 1.6 (0.2)
Ireland 71.9 (0.6) 12.7 (0.5) 6.7 (0.4) 5.8 (0.3) 2.4 (0.2)
Italy 69.5 (0.8) 12.2 (0.5) 6.6 (0.5) 7.8 (0.4) 3.1 (0.3)
Japan 68.4 (0.6) 16.4 (0.4) 8.3 (0.4) 4.1 (0.3) 1.5 (0.2)
Korea 66.2 (0.7) 11.5 (0.4) 12.6 (0.4) 7.1 (0.3) 2.3 (0.2)
Netherlands 51.3 (0.6) 19.9 (0.5) 14.1 (0.6) 9.9 (0.5) 2.5 (0.2)
Norway 49.7 (0.7) 26.4 (0.6) 14.3 (0.5) 6.4 (0.3) 0.8 (0.1)
Poland 67.0 (0.4) 16.5 (0.4) 8.7 (0.3) 6.4 (0.3) 1.4 (0.2)
Slovak Republic 62.8 (0.8) 15.2 (0.6) 8.3 (0.3) 10.4 (0.5) 2.9 (0.3)
Spain 71.1 (0.6) 12.0 (0.5) 7.0 (0.4) 6.3 (0.4) 2.8 (0.3)
Sweden 56.2 (0.7) 24.4 (0.6) 12.8 (0.5) 5.1 (0.4) 1.4 (0.2)
United States 57.5 (0.8) 17.7 (0.6) 10.6 (0.4) 7.1 (0.4) 2.9 (0.3)
England (UK) 62.2 (0.9) 16.1 (0.6) 10.1 (0.6) 8.0 (0.4) 2.3 (0.3)
Average1
58.7 (0.2) 19.1 (0.1) 11.4 (0.1) 7.4 (0.1) 2.0 (0.0)
Average-222
60.0 (0.1) 18.4 (0.1) 10.9 (0.1) 7.4 (0.1) 2.1 (0.0)
Partners
Cyprus3
60.7 (0.7) 10.6 (0.5) 4.3 (0.3) 4.5 (0.4) 2.2 (0.2)
Russian Federation4
73.4 (1.8) 13.1 (1.0) 5.6 (0.6) 5.7 (0.6) 2.1 (0.3)

[Part 1/1]
Table A2.4e Frequency of a word processor use (e.g. Word)
Frequency of use
OECD % S.E. % S.E. % S.E. % S.E. % S.E.
National entities
Australia 38.7 (0.8) 17.2 (0.6) 14.3 (0.5) 17.9 (0.6) 10.0 (0.4)
Austria 33.4 (0.6) 19.3 (0.6) 20.5 (0.5) 18.9 (0.5) 6.2 (0.3)
Canada 34.2 (0.5) 19.5 (0.4) 16.2 (0.4) 19.1 (0.4) 10.0 (0.3)
Czech Republic 38.1 (1.1) 16.3 (0.8) 16.3 (0.8) 20.6 (1.0) 8.0 (0.8)
Denmark 22.4 (0.5) 17.7 (0.5) 20.6 (0.5) 23.8 (0.5) 15.2 (0.5)
Estonia 44.5 (0.5) 18.3 (0.4) 16.9 (0.4) 15.2 (0.4) 4.5 (0.2)
Finland 28.9 (0.6) 28.9 (0.6) 23.3 (0.5) 15.7 (0.5) 2.9 (0.2)
France 44.3 (0.5) 21.4 (0.4) 15.6 (0.3) 12.3 (0.4) 5.6 (0.3)
Germany 28.8 (0.7) 18.2 (0.5) 22.4 (0.6) 21.2 (0.6) 7.9 (0.4)
Ireland 48.9 (0.6) 15.6 (0.6) 11.7 (0.5) 15.1 (0.5) 8.2 (0.4)
Italy 53.6 (0.8) 13.6 (0.6) 9.6 (0.5) 15.1 (0.6) 7.4 (0.5)
Japan 61.5 (0.8) 20.3 (0.6) 9.8 (0.5) 5.3 (0.3) 1.7 (0.2)
Korea 53.9 (0.8) 13.7 (0.4) 16.1 (0.5) 12.3 (0.5) 3.7 (0.3)
Netherlands 22.2 (0.6) 17.3 (0.6) 18.9 (0.6) 26.0 (0.6) 13.3 (0.5)
Norway 20.7 (0.5) 24.0 (0.6) 23.4 (0.5) 21.0 (0.6) 8.5 (0.4)
Poland 48.6 (0.6) 13.9 (0.5) 13.7 (0.4) 17.0 (0.5) 6.8 (0.4)
Slovak Republic 45.4 (0.8) 13.0 (0.5) 11.1 (0.5) 20.7 (0.5) 9.6 (0.5)
Spain 52.1 (0.6) 11.6 (0.5) 10.8 (0.5) 15.9 (0.5) 8.8 (0.4)
Sweden 26.8 (0.7) 25.5 (0.7) 21.0 (0.6) 19.6 (0.5) 7.1 (0.4)
United States 36.9 (0.8) 15.6 (0.6) 16.7 (0.5) 16.6 (0.4) 9.9 (0.5)
England (UK) 34.3 (0.8) 19.9 (0.7) 16.5 (0.6) 19.7 (0.7) 8.1 (0.5)
Average1
36.9 (0.2) 18.5 (0.1) 17.2 (0.1) 18.2 (0.1) 7.8 (0.1)
Average-222
38.7 (0.1) 18.1 (0.1) 16.5 (0.1) 17.7 (0.1) 7.8 (0.1)
Partners
Cyprus3
45.4 (0.6) 11.4 (0.4) 7.6 (0.4) 10.9 (0.5) 7.0 (0.4)
Russian Federation4
55.6 (2.4) 15.0 (1.2) 7.8 (0.5) 13.1 (1.0) 8.4 (1.0)

[Part 1/1]
Table A2.5 Literacy proficiency, frequent e-mail use and access to the Internet at home
Literacy mean score
Percentage of adults with frequent e-mail use
(at least once a month)
Households with Internet
access at home (2010 or
latest available year)
OECD Score S.E. % S.E. %
National entities
Australia 280.4 (0.9) 76.0 (0.6) 72.0
Austria 269.5 (0.7) 68.6 (0.7) 72.9
Canada 273.5 (0.6) 79.6 (0.4) 77.8
Czech Republic 274.0 (1.0) 72.9 (1.0) 60.5
Denmark 270.8 (0.6) 85.8 (0.4) 86.1
Estonia 275.9 (0.7) 75.1 (0.4) 67.8
Finland 287.5 (0.7) 81.7 (0.5) 80.5
France 262.1 (0.6) 71.2 (0.5) 73.6
Germany 269.8 (0.9) 72.9 (0.6) 82.5
Ireland 266.5 (0.9) 66.0 (0.6) 71.7
Italy 250.5 (1.1) 53.4 (0.8) 59.0
Japan 296.2 (0.7) 57.6 (0.8) 67.1
Korea 272.6 (0.6) 57.5 (0.6) 96.8
Netherlands 284.0 (0.7) 87.2 (0.4) 90.9
Norway 278.4 (0.6) 84.9 (0.5) 89.8
Poland 266.9 (0.6) 57.6 (0.6) 63.4
Slovak Republic 273.8 (0.6) 61.2 (0.6) 67.5
Spain 251.8 (0.7) 60.2 (0.7) 59.1
Sweden 279.2 (0.7) 84.3 (0.6) 88.3
United States 269.8 (1.0) 71.2 (0.9) 71.1
Flanders (Belgium) 275.5 (0.8) 76.3 (0.5) 72.7
England (UK) 272.6 (1.1) 77.0 (0.7) m
Northern Ireland (UK) 268.7 (1.9) 63.6 (0.9) m
England/N. Ireland (UK) 272.5 (1.0) 76.6 (0.7) 79.6
Average1
275.6 (0.2) 73.3 (0.1) 76.8
Average-222
272.8 (0.2) 71.7 (0.1) 75.0
Partners
Cyprus3
268.8 (0.8) 40.9 (0.6) m
Russian Federation4
275.2 (2.7) 43.8 (2.7) m
Source: Survey of Adult Skills (PIAAC) (2012); OECD, ICT Database; Eurostat, Community Survey on ICT usage in housholds and by individuals, November 2011.

[Part 1/2]
Table A3.1
Percentage differences between groups of adults who score at Level 2 or 3 in problem solving in
technology-rich environments, before and after accounting for various characteristics (country average)
Version 1
(socio-demographic variables)
Version 2 (socio-demographic
variables + e-mail use)
Coef. S.E.
Unadjusted
%
Adjusted
%
Unadjusted
% dif
Adjusted
% dif Coef. S.E.
Unadjusted
%
Adjusted
%
Unadjusted
% dif
Adjusted
% dif
Age (ref. value is 55-65 year-olds)
16-24 year-olds 1.7 *** (0.1) 50.7 41.2 39.0 29.5 1.5 *** (0.1) 50.7 36.1 39.0 24.4
25-34 year-olds 1.8 *** (0.0) 49.2 44.3 37.5 32.7 1.6 *** (0.1) 49.2 39.8 37.5 28.1
35-44 year-olds 1.4 *** (0.0) 38.1 34.4 26.4 22.7 1.2 *** (0.0) 38.1 31.3 26.4 19.6
45-54 year-olds 0.8 *** (0.0) 24.0 21.9 12.3 10.2 0.7 *** (0.0) 24.0 20.9 12.3 9.2
Educational attainment (ref. value is
lower than upper secondary)
Upper secondary 0.8 *** (0.0) 30.5 34.4 11.5 15.4 0.7 *** (0.0) 30.5 31.6 11.5 12.5
Tertiary 1.8 *** (0.0) 51.8 58.3 32.8 39.3 1.5 *** (0.0) 51.8 52.2 32.8 33.1
Gender (ref. value is women)
Men 0.3 *** (0.0) 36.3 38.8 4.7 7.1 0.3 *** (0.0) 36.3 39.2 4.7 7.6
(ref. value is neither parent attained
upper secondary)
At least one parent attained upper
secondary
0.5 *** (0.0) 37.6 24.2 21.8 8.4 0.4 *** (0.0) 37.6 22.6 21.8 6.8
At least one parent attained tertiary 0.9 *** (0.0) 55.0 32.2 39.3 16.5 0.8 *** (0.0) 55.0 29.8 39.3 14.0
(ref. value is foreign-born and foreign
language)
Native-born and native language 1.5 ** (0.6) 36.4 45.9 19.9 29.4 1.5 *** (0.6) 36.4 46.4 19.9 29.8
Native-born and foreign language 0.8 *** (0.1) 29.4 31.1 12.8 14.6 0.8 *** (0.1) 29.4 30.9 12.8 14.4
Foreign-born and native language 1.2 ** (0.6) 33.6 38.9 17.0 22.3 1.2 ** (0.6) 33.6 39.1 17.0 22.5
Participation in adult education
and training (ref. value is did not
participate)
Participated 0.6 *** (0.0) 42.3 30.0 23.8 11.5 0.5 *** (0.0) 42.3 27.8 23.8 9.3
Frequency of e-mail use (ref. value is
low frequency/irregular use)
High frequency/regular use 1.5 *** (0.0) 43.5 26.2 36.2 18.9
Level of literacy proficiency (ref value
is Level 2)
At or below Level 1
Level 3
Level 4/5
* Significant estimate p ≤ 0.10.
** Significant estimate p ≤ 0.05.
*** Significant estimate p ≤ 0.01.
Notes: The reference category for problem solving in technology-rich environments is Below Level 1. Adjusted results include controls for age, educational attainment, gender,
parents’ educational attainment, immigrant and language background, participation in adult education and training, e-mail use, and literacy proficiency. Results for each country
are available in Tables B3.1, B3.2, B3.3 in Annex B.

[Part 2/2]
Table A3.1
Percentage differences between groups of adults who score at Level 2 or 3 in problem solving in
technology-rich environments, before and after accounting for various characteristics (country average)
Version 3
(socio-demographic variables + e-mail use + literacy proficiency)
Coef. S.E. Unadjusted % Adjusted %
Unadjusted
% dif
Adjusted
% dif
16-24 year-olds 1.6 *** (0.1) 50.7 40.1 39.0 28.4
25-34 year-olds 1.6 *** (0.1) 49.2 38.6 37.5 26.9
35-44 year-olds 1.1 *** (0.1) 38.1 28.5 26.4 16.8
45-54 year-olds 0.6 *** (0.0) 24.0 19.5 12.3 7.9
(ref. value is lower than upper secondary)
Upper secondary 0.3 *** (0.0) 30.5 23.5 11.5 4.5
Tertiary 0.7 *** (0.1) 51.8 31.9 32.8 12.9
Men 0.3 *** (0.0) 36.3 38.7 4.7 7.0
(ref. value is neither parent attained upper secondary)
At least one parent attained upper secondary 0.3 *** (0.0) 37.6 20.2 21.8 4.4
At least one parent attained tertiary 0.5 *** (0.0) 55.0 23.2 39.3 7.5
(ref. value is foreign-born and foreign language)
Native-born and native language 0.9 (0.6) 36.4 32.1 19.9 15.5
Native-born and foreign language 0.4 *** (0.1) 29.4 22.7 12.8 6.1
Foreign-born and native language 0.8 (0.6) 33.6 30.1 17.0 13.6
(ref. value is did not participate)
Participated 0.4 *** (0.0) 42.3 25.3 23.8 6.9
Frequency of e-mail use
(ref. value is low frequency/irregular use)
High frequency/regular use 1.3 *** (0.0) 43.5 21.9 36.2 14.5
Level of literacy proficiency (ref value is Level 2)
At or below Level 1 -3.6 *** (1.3) 0.4 0.3 -10.1 -10.2
Level 3 2.0 *** (0.0) 50.1 46.3 39.5 35.8
Level 4/5 3.5 *** (0.1) 83.0 79.5 72.4 68.9
Notes: The reference category for problem solving in technology-rich environments is Below Level 1. Adjusted results include controls for age, educational attainment, gender,
parents’ educational attainment, immigrant and language background, participation in adult education and training, e-mail use, and literacy proficiency. Results for each country
are available in Tables B3.1, B3.2, B3.3 in Annex B.

[Part 1/1]
Table A3.2
Percentage differences between various groups of adults who have no computer experience, before and
after accounting for various characteristics (country average)
Version 1
(socio-demographic variables)
Version 2 (socio-demographic
variables + literacy proficiency)
Coef. S.E.
Unadjusted
%
Adjusted
%
Unadjusted
% dif
Adjusted
% dif Coef. S.E.
Unadjusted
%
Adjusted
%
Unadjusted
% dif
Adjusted
% dif
16-24 year-olds -4.9 *** (1.8) 0.7 0.2 -21.6 -22.0 -4.9 *** (1.8) 0.7 0.2 -21.6 -22.0
25-34 year-olds -3.3 *** (1.2) 1.7 1.1 -20.5 -21.1 -3.2 *** (1.2) 1.7 1.2 -20.5 -21.1
35-44 year-olds -1.9 *** (0.1) 4.1 4.3 -18.2 -17.9 -1.8 *** (0.1) 4.1 4.6 -18.2 -17.7
45-54 year-olds -0.7 *** (0.0) 10.8 12.0 -11.4 -10.2 -0.7 *** (0.0) 10.8 12.4 -11.4 -9.8
Educational attainment (ref. value is
lower than upper secondary)
Upper secondary -1.3 *** (0.0) 7.1 6.7 -13.5 -13.9 -1.1 *** (0.0) 7.1 7.9 -13.5 -12.7
Tertiary -3.0 *** (0.1) 1.0 1.2 -19.6 -19.4 -2.6 *** (0.1) 1.0 1.8 -19.6 -18.8
Men -0.5 *** (0.1) 7.8 5.0 -0.4 -3.2 -0.5 *** (0.1) 7.8 5.4 -0.4 -2.9
(ref. value is neither parent attained
upper secondary)
At least one parent attained upper
secondary -0.6 *** (0.1) 4.4 11.7 -14.3 -7.1 -0.5 *** (0.1) 4.4 12.6 -14.3 -6.2
At least one parent attained tertiary -1.0 *** (0.1) 1.4 7.6 -17.3 -11.2 -0.9 *** (0.1) 1.4 8.6 -17.3 -10.1
(ref. value is foreign-born and foreign
language)
Native-born and native language -0.9 *** (0.1) 7.7 5.5 -5.0 -7.2 -0.6 *** (0.1) 7.7 7.3 -5.0 -5.4
Native-born and foreign language -2.6 (2.1) 7.1 1.0 -5.6 -11.7 -2.5 (2.3) 7.1 1.2 -5.6 -11.5
Foreign-born and native language -1.4 (1.4) 10.5 3.5 -2.2 -9.2 -1.2 (1.5) 10.5 4.4 -2.2 -8.4
and training (ref. value is did not
participate)
Participated -1.4 *** (0.1) 2.6 4.5 -13.1 -11.2 -1.3 *** (0.1) 2.6 4.9 -13.1 -10.8
Level of literacy proficiency
(ref value is Level 2)
At or below Level 1 0.7 * (0.1) 23.9 17.7 14.1 7.9
Level 3 -0.6 *** (0.3) 3.6 5.8 -6.2 -4.1
Level 4/5 -3.5 (2.4) 0.9 0.3 -8.9 -9.5
Notes: The reference category for problem solving in rich-environments is Below Level 1. Adjusted results include controls for age, educational attainment, gender, parents’
educational attainment, immigrant and language background, participation in adult education and training, e-mail use, and literacy proficiency. Results for each country are
available in Tables B3.4 and B3.5 in Annex B.

[Part 1/2]
Table A3.3
Percentage of adults who score at Level 2 or 3 in problem solving in technology-rich environments or
have no computer experience, by age
16-24 year-olds 25-34 year-olds 35-44 year-olds
No computer
experience Level 2/3
No computer
No computer
OECD % S.E. % S.E. % S.E. % S.E. % S.E. % S.E.
National entities
Australia 0.4 (0.3) 50.7 (2.6) 1.0 (0.3) 47.9 (2.0) 1.8 (0.3) 42.0 (1.7)
Austria 0.2 (0.2) 50.7 (2.0) 1.6 (0.4) 49.1 (1.7) 4.8 (0.7) 36.9 (1.9)
Canada 0.2 (0.1) 50.8 (1.8) 0.8 (0.2) 49.0 (1.7) 1.7 (0.3) 42.0 (1.3)
Czech Republic 0.6 (0.3) 54.7 (2.9) 3.1 (1.0) 51.5 (2.2) 2.8 (0.5) 31.8 (2.6)
Denmark 0.1 (0.1) 50.4 (1.9) 1.1 (0.4) 57.7 (1.9) 1.0 (0.3) 47.9 (1.9)
Estonia 0.1 (0.1) 50.4 (2.1) 0.8 (0.2) 43.8 (1.6) 4.8 (0.6) 27.3 (1.1)
Finland 0.0 (0.0) 61.9 (2.4) 0.0 (0.0) 67.5 (2.1) 0.0 (0.0) 52.7 (1.9)
France 1.4 (1.4) m m 1.7 (0.4) m m 5.4 (0.5) m m
Germany 0.5 (0.3) 54.2 (1.7) 1.2 (0.4) 52.9 (1.8) 4.6 (0.8) 39.1 (1.8)
Ireland 0.6 (0.3) 40.3 (2.6) 1.6 (0.3) 36.0 (1.6) 6.3 (0.8) 26.2 (1.3)
Italy 1.4 (1.4) m m 7.3 (1.2) m m 17.8 (1.4) m m
Japan 1.6 (0.6) 45.8 (2.4) 1.8 (0.4) 53.7 (2.0) 3.5 (0.6) 44.6 (1.6)
Korea 0.7 (0.3) 63.4 (2.1) 1.0 (0.3) 48.6 (2.4) 4.4 (0.5) 29.1 (1.4)
Netherlands 0.0 (0.0) 58.3 (2.2) 0.5 (0.2) 57.6 (2.2) 1.4 (0.4) 49.5 (2.1)
Norway 0.2 (0.1) 54.9 (1.8) 0.3 (0.2) 56.3 (1.8) 0.3 (0.2) 48.4 (1.7)
Poland 0.7 (0.2) 37.9 (1.2) 3.6 (0.5) 29.9 (1.9) 13.3 (1.3) 18.3 (1.8)
Slovak Republic 4.8 (0.7) 40.5 (1.8) 9.4 (0.9) 34.9 (2.1) 16.4 (1.2) 26.3 (2.1)
Spain 1.4 (1.4) m m 4.2 (0.6) m m 9.4 (0.7) m m
Sweden 0.4 (0.3) 61.7 (2.1) 0.5 (0.3) 60.5 (1.8) 0.5 (0.3) 50.5 (1.8)
United States 0.8 (0.3) 37.6 (2.5) 1.9 (0.7) 38.9 (2.1) 4.9 (0.8) 34.3 (1.9)
Flanders (Belgium) 0.2 (0.1) 57.1 (1.9) 2.2 (0.5) 51.8 (2.0) 3.1 (0.5) 38.9 (1.9)
England (UK) 0.7 (0.4) 42.3 (2.6) 0.4 (0.1) 47.4 (1.8) 1.7 (0.5) 39.0 (1.9)
Northern Ireland (UK) c c 44.2 (3.3) 2.8 (0.9) 42.1 (2.3) 6.9 (1.0) 28.8 (2.2)
England/N. Ireland (UK) 0.7 (0.4) 42.4 (2.5) 0.4 (0.1) 47.2 (1.7) 1.8 (0.4) 38.6 (1.9)
Average1
0.7 (0.1) 50.7 (0.5) 1.7 (0.1) 49.2 (0.4) 4.1 (0.1) 38.1 (0.4)
Average-222
0.8 (0.1) m m 2.1 (0.1) m m 5.0 (0.1) m m
Partners
Cyprus3
1.5 (0.5) m m 4.4 (0.7) m m 13.4 (0.9) m m
Russian Federation4
0.8 (0.4) 38.8 (4.4) 3.6 (0.9) 33.8 (4.2) 12.4 (2.4) 22.0 (3.2)

[Part 2/2]
Table A3.3
have no computer experience, by age
45-54 year-olds 55-65 year-olds
No computer experience Level 2/3 No computer experience Level 2/3
OECD % S.E. % S.E. % S.E. % S.E.
National entities
Australia 4.9 (0.7) 30.8 (2.0) 12.3 (1.0) 17.2 (1.3)
Austria 11.3 (1.1) 22.6 (1.5) 29.2 (1.5) 7.5 (1.0)
Canada 6.1 (0.5) 28.2 (1.1) 12.5 (0.6) 16.4 (1.0)
Czech Republic 14.2 (1.5) 18.7 (2.2) 29.0 (1.9) 12.1 (1.9)
Denmark 2.5 (0.4) 30.0 (1.6) 6.8 (0.6) 13.2 (1.0)
Estonia 13.3 (0.9) 13.1 (1.2) 30.0 (1.1) 4.8 (0.7)
Finland 3.8 (0.8) 30.1 (1.6) 10.9 (0.9) 8.9 (0.9)
France 13.5 (0.9) m m 27.8 (1.0) m m
Germany 10.2 (1.0) 27.3 (1.7) 20.9 (1.7) 13.4 (1.6)
Ireland 16.1 (1.4) 13.8 (1.2) 31.2 (1.5) 5.3 (0.8)
Italy 33.6 (2.2) m m 53.8 (2.1) m m
Japan 9.6 (0.9) 26.8 (1.7) 28.6 (1.5) 9.9 (1.1)
Korea 24.2 (1.2) 11.3 (1.2) 52.0 (1.4) 4.1 (0.7)
Netherlands 3.3 (0.5) 32.3 (1.8) 8.6 (0.8) 16.6 (1.2)
Norway 1.8 (0.5) 31.7 (1.5) 5.3 (0.8) 14.2 (1.3)
Poland 31.9 (1.6) 7.9 (1.2) 47.3 (1.7) 2.5 (0.6)
Slovak Republic 30.4 (1.6) 17.4 (1.6) 49.2 (1.5) 9.2 (1.3)
Spain 23.0 (1.2) m m 42.6 (1.7) m m
Sweden 1.1 (0.4) 34.7 (1.8) 5.5 (0.8) 17.4 (1.2)
United States 7.5 (0.8) 25.6 (1.8) 10.8 (0.9) 19.7 (1.9)
Flanders (Belgium) 7.4 (0.7) 24.7 (1.5) 20.2 (1.1) 12.0 (1.2)
England (UK) 6.1 (0.8) 28.5 (1.5) 12.0 (1.2) 17.6 (1.8)
Northern Ireland (UK) 15.8 (1.4) 17.0 (1.6) 25.1 (2.1) 9.5 (1.7)
England/N. Ireland (UK) 6.4 (0.8) 28.1 (1.5) 12.4 (1.1) 17.4 (1.7)
Average1
10.8 (0.2) 24.0 (0.4) 22.2 (0.3) 11.7 (0.3)
Average-222
12.6 (0.2) m m 24.9 (0.3) m m
Partners
Cyprus3
30.1 (1.4) m m 48.9 (1.6) m m
Russian Federation4
26.7 (3.8) 25.4 (2.8) 48.6 (3.8) 9.0 (1.9)

[Part 1/1]
Table A3.4
have no computer experience, by educational attainment
Lower than upper secondary Upper secondary Tertiary
No computer
No computer
No computer
National entities
Australia 9.7 (0.8) 20.1 (1.4) 2.9 (0.4) 37.3 (1.6) 0.6 (0.2) 55.7 (1.5)
Austria 24.0 (1.3) 16.3 (1.4) 6.8 (0.5) 34.5 (1.1) 1.4 (0.4) 50.8 (2.2)
Canada 15.8 (0.8) 18.8 (1.6) 4.2 (0.3) 32.1 (0.9) 1.1 (0.1) 46.7 (1.0)
Czech Republic 22.6 (2.1) 27.5 (2.8) 10.1 (0.6) 27.9 (1.3) 0.5 (0.2) 58.8 (3.2)
Denmark 6.4 (0.6) 23.6 (1.1) 1.9 (0.2) 35.2 (1.2) 0.0 (0.0) 54.8 (1.2)
Estonia 19.1 (1.0) 20.8 (1.4) 12.3 (0.5) 23.3 (0.9) 2.5 (0.3) 36.4 (1.3)
Finland 11.3 (1.1) 26.3 (1.8) 2.9 (0.4) 36.2 (1.1) 0.1 (0.1) 56.3 (1.1)
France 25.3 (0.9) m m 7.3 (0.5) m m 0.6 (0.1) m m
Germany 15.3 (1.5) 27.1 (1.9) 8.8 (0.7) 30.5 (1.0) 2.4 (0.4) 52.9 (1.6)
Ireland 28.1 (1.2) 7.9 (0.9) 4.7 (0.4) 22.2 (1.5) 0.6 (0.1) 45.1 (1.5)
Italy 40.2 (1.4) m m 8.1 (0.7) m m 1.8 (0.5) m m
Japan 30.8 (1.9) 17.1 (1.7) 10.8 (0.7) 27.2 (1.2) 2.6 (0.3) 49.5 (1.3)
Korea 48.2 (1.3) 15.8 (1.1) 10.7 (0.6) 26.1 (1.3) 1.4 (0.2) 44.9 (1.6)
Netherlands 8.3 (0.7) 20.0 (1.1) 1.0 (0.2) 43.6 (1.5) 0.4 (0.2) 63.8 (1.5)
Norway 4.3 (0.6) 25.3 (1.5) 1.0 (0.3) 37.6 (1.1) 0.3 (0.1) 59.6 (1.5)
Poland 37.0 (1.7) 17.6 (1.4) 22.9 (0.8) 11.5 (0.6) 1.2 (0.3) 37.8 (1.8)
Slovak Republic 50.3 (1.6) 14.3 (1.3) 19.1 (0.7) 22.3 (1.1) 0.9 (0.3) 48.9 (2.2)
Spain 32.4 (0.9) m m 5.7 (0.6) m m 1.4 (0.3) m m
Sweden 4.5 (0.7) 22.4 (1.6) 0.9 (0.2) 44.1 (1.2) 0.2 (0.1) 62.1 (1.2)
United States 21.5 (1.8) 13.6 (1.5) 4.1 (0.4) 24.7 (1.3) 0.8 (0.2) 51.3 (1.7)
England (UK) 11.1 (0.9) 10.1 (1.2) 2.7 (0.4) 34.1 (1.4) 1.0 (0.3) 53.5 (1.6)
Northern Ireland (UK) 23.1 (1.5) 7.5 (1.5) 5.1 (0.6) 32.1 (2.2) 0.9 (0.4) 49.4 (2.4)
Average1
20.6 (0.3) 19.0 (0.3) 7.1 (0.1) 30.5 (0.3) 1.0 (0.1) 51.8 (0.4)
Average-222
22.2 (0.3) m m 7.1 (0.1) m m 1.0 (0.1) m m
Partners
Cyprus3
38.6 (1.0) m m 17.0 (0.9) m m 4.3 (0.5) m m
Russian Federation4
29.1 (4.5) 17.4 (3.2) 29.5 (2.7) 22.6 (2.5) 11.2 (1.3) 28.6 (2.6)

[Part 1/1]
Table A3.5
have no computer experience, by age and gender
16-65 year-olds 16-24 year-olds
Men Women Men Women
No computer
No computer
No computer
No computer
National entities
Australia 4.1 (0.4) 38.5 (1.2) 3.8 (0.4) 37.5 (1.5) 0.8 (0.5) 49.4 (3.2) 0.1 (0.1) 52.0 (4.1)
Austria 8.6 (0.5) 36.7 (1.0) 10.6 (0.7) 28.3 (1.2) 0.0 (0.0) 53.4 (2.6) 0.3 (0.3) 47.9 (3.4)
Canada 4.8 (0.3) 37.3 (0.7) 4.2 (0.2) 35.9 (0.8) 0.1 (0.1) 49.7 (2.3) 0.3 (0.2) 51.9 (2.4)
Czech Republic 9.4 (0.7) 35.7 (1.5) 11.2 (0.8) 30.6 (1.5) 1.0 (0.6) 56.6 (3.3) 0.1 (0.2) 52.8 (4.0)
Denmark 2.9 (0.3) 40.0 (1.0) 1.9 (0.2) 37.3 (1.0) 0.1 (0.1) 48.7 (3.2) 0.0 (0.0) 52.1 (2.3)
Estonia 11.1 (0.5) 28.3 (1.1) 8.8 (0.4) 26.9 (0.8) 0.0 (0.0) 49.1 (2.7) 0.1 (0.1) 51.9 (2.6)
Finland 4.0 (0.4) 42.7 (1.1) 3.0 (0.3) 40.4 (1.1) 0.0 (0.0) 65.7 (2.9) 0.0 (0.0) 58.0 (3.4)
France 10.3 (0.5) m m 10.6 (0.5) m m 0.2 (0.2) m m 0.7 (0.4) m m
Germany 6.4 (0.5) 39.9 (1.2) 9.5 (0.8) 32.0 (1.1) 0.2 (0.3) 56.2 (2.7) 0.7 (0.4) 52.2 (2.3)
Ireland 11.2 (0.6) 26.8 (1.0) 9.0 (0.5) 23.8 (1.3) 0.5 (0.4) 41.1 (3.6) 0.7 (0.6) 39.5 (3.4)
Italy 19.6 (1.0) m m 29.3 (1.1) m m 2.4 (0.9) m m 2.5 (1.0) m m
Japan 7.8 (0.5) 40.0 (1.2) 12.7 (0.7) 29.1 (1.1) 1.7 (0.7) 46.6 (3.1) 1.6 (0.8) 44.9 (3.3)
Korea 13.0 (0.5) 33.3 (1.1) 18.0 (0.6) 27.6 (1.1) 1.3 (0.7) 63.1 (2.8) 0.1 (0.1) 63.6 (3.0)
Netherlands 2.9 (0.3) 45.4 (1.1) 3.0 (0.3) 37.6 (1.0) 0.0 (0.0) 59.5 (2.6) 0.0 (0.0) 56.9 (3.0)
Norway 1.5 (0.2) 44.0 (1.0) 1.8 (0.3) 37.8 (1.1) 0.2 (0.2) 55.1 (2.4) 0.2 (0.2) 54.6 (2.6)
Poland 21.3 (0.8) 20.7 (1.1) 17.7 (0.7) 17.7 (0.9) 0.8 (0.3) 37.1 (1.7) 0.6 (0.2) 38.8 (1.8)
Slovak Republic 22.0 (0.9) 26.5 (1.2) 22.0 (0.8) 24.8 (1.0) 4.7 (1.0) 40.7 (2.9) 4.9 (1.0) 40.3 (2.9)
Spain 16.2 (0.6) m m 17.8 (0.7) m m 1.1 (0.5) m m 1.3 (0.6) m m
Sweden 1.3 (0.3) 45.9 (1.1) 1.8 (0.3) 42.0 (1.2) 0.0 (0.0) 62.2 (3.1) 0.7 (0.6) 61.1 (2.6)
United States 5.8 (0.5) 32.7 (1.3) 4.7 (0.6) 29.6 (1.3) 0.9 (0.4) 37.8 (3.1) 0.7 (0.4) 37.4 (3.8)
England (UK) 3.9 (0.4) 39.1 (1.4) 4.3 (0.4) 30.9 (1.0) 0.4 (0.5) 45.0 (3.8) 0.9 (0.7) 39.6 (2.9)
Average1
7.8 (0.1) 36.3 (0.3) 8.2 (0.1) 31.6 (0.3) 0.7 (0.1) 51.3 (0.7) 0.7 (0.1) 50.2 (0.7)
Average-222
8.9 (0.1) m m 9.7 (0.1) m m 0.8 (0.1) m m 0.8 (0.1) m m
Partners
Cyprus3
17.2 (0.7) m m 19.4 (0.6) m m 1.9 (0.9) m m 1.1 (0.6) m m
Russian Federation4
18.7 (2.1) 25.6 (2.4) 18.0 (1.6) 26.3 (2.7) 0.6 (0.3) 35.0 (4.5) 1.0 (0.6) 42.9 (5.6)

[Part 1/1]
Table A3.6
have no computer experience, by immigrant and language status
Native-born and native language Native-born and foreign language Foreign-born and native language Foreign-born and foreign language
No computer
No computer
No computer
No computer
National entities
Australia 3.4 (0.3) 41.1 (1.2) 2.5 (1.4) 37.3 (5.2) 3.1 0.7 40.8 (2.6) 8.5 (1.1) 25.1 (2.1)
Austria 9.2 (0.5) 35.6 (0.9) 1.9 (1.3) 26.8 (4.9) 4.8 2.0 43.3 (4.8) 16.9 (1.8) 13.5 (1.6)
Canada 3.8 (0.2) 40.3 (0.8) 2.7 (0.5) 39.8 (2.3) 3.3 0.6 33.6 (2.2) 8.4 (0.7) 24.0 (1.5)
Czech Republic 10.0 (0.5) 33.6 (1.2) c c c c 27.2 7.9 34.8 (10.5) 11.1 (3.0) 20.6 (7.3)
Denmark 2.2 (0.2) 41.2 (0.8) 1.0 (1.0) 41.0 (7.7) 3.2 2.0 42.5 (6.3) 4.8 (0.6) 17.6 (1.5)
Estonia 8.5 (0.3) 30.0 (0.7) 10.6 (2.3) 28.0 (4.6) 18.4 1.3 12.4 (1.7) 26.0 (4.2) 11.7 (3.4)
Finland 3.5 (0.3) 42.9 (0.8) 5.2 (2.6) 30.6 (4.6) 1.6 1.6 55.2 (7.3) 3.4 (2.5) 19.5 (5.7)
France 9.3 (0.4) m m 6.4 (2.3) m m 14.7 1.9 m m 23.3 (1.8) m m
Germany 6.9 (0.5) 40.2 (0.9) 5.9 (3.2) 23.9 (5.6) 13.8 3.3 26.2 (4.1) 16.0 (2.4) 12.6 (1.9)
Ireland 12.0 (0.5) 25.0 (1.0) 24.0 (7.0) 14.7 (5.5) 3.4 0.9 32.8 (2.5) 1.8 (0.6) 20.3 (2.4)
Italy 24.6 (0.8) m m 32.2 (8.4) m m 12.7 3.8 m m 25.7 (3.1) m m
Japan 10.4 (0.5) 34.9 (0.8) c c c c c c c c c c c c
Korea 15.4 (0.4) 31.0 (0.8) c c c c 36.2 7.0 15.8 (5.5) 15.6 (7.1) 0.0 (0.0)
Netherlands 2.4 (0.2) 45.6 (0.8) 4.3 (3.0) 27.3 (9.3) 3.6 1.6 41.3 (5.1) 8.9 (1.7) 16.7 (2.2)
Norway 1.5 (0.2) 44.9 (0.8) 1.7 (1.6) 34.8 (6.4) 0.0 0.0 46.7 (7.7) 2.7 (0.8) 22.0 (1.9)
Poland 19.6 (0.5) 19.3 (0.8) 8.8 (3.6) 12.7 (5.4) c c c c c c c c
Slovak Republic 20.8 (0.6) 26.8 (0.8) 34.1 (3.4) 11.8 (2.8) 45.5 6.9 12.7 (5.7) 46.4 (8.1) 13.0 (6.1)
Spain 17.5 (0.5) m m 21.3 (2.9) m m 8.0 1.4 m m 22.6 (2.8) m m
Sweden 1.1 (0.2) 49.3 (0.9) 0.0 (0.0) 41.0 (5.6) 1.4 1.5 37.6 (6.0) 4.4 (0.9) 18.2 (1.6)
United States 3.4 (0.3) 35.7 (1.3) 5.5 (1.6) 32.8 (5.5) 5.5 2.5 24.1 (4.3) 20.9 (3.1) 12.2 (1.9)
Flanders (Belgium) 7.7 (0.4) 37.8 (0.9) 3.5 (1.4) 33.6 (4.3) 2.6 1.3 39.9 (4.9) 15.3 (2.5) 11.4 (2.7)
England (UK) 4.1 (0.3) 37.1 (1.0) 2.4 (2.5) 34.5 (7.0) 4.5 1.3 31.4 (3.9) 5.0 (1.1) 23.4 (2.7)
Northern Ireland (UK) 10.4 (0.6) 29.8 (1.3) c c c c 9.1 3.2 28.8 (6.6) 4.7 (3.4) 21.2 (4.6)
England/N. Ireland (UK) 4.4 (0.3) 36.8 (1.0) 2.7 (2.4) 34.3 (6.9) 4.7 1.3 31.3 (3.8) 5.0 (1.1) 23.3 (2.7)
Average1
7.7 (0.1) 36.4 (0.2) 7.1 (0.7) 29.4 (1.4) 10.5 (0.8) 33.6 (1.3) 12.7 (0.8) 16.6 (0.8)
Average-222
9.0 (0.1) m m 9.2 (0.8) m m 10.7 (0.7) m m 14.4 (0.7) m m
Partners
Cyprus3
23.5 (0.5) m m c c m m 9.1 1.7 m m 18.5 (3.7) m m
Russian Federation4
m m m m m m m m m m m m m m m m
Notes: Results for the Russian Federation are missing as no language variables are available for the Russian Federation.

[Part 1/1]
Table A3.7
have no computer experience, by level of literacy proficiency
At or below Level 1 Level 2 Level 3 Level 4/5
No computer
No computer
No computer
No computer
National entities
Australia 18.1 (1.6) 0.0 (0.0) 4.3 (0.6) 11.2 (1.4) 1.0 (0.2) 52.0 (2.1) 0.2 (0.2) 83.3 (1.9)
Austria 24.5 (2.2) 0.0 (0.0) 11.7 (1.0) 11.6 (1.2) 3.9 (0.9) 55.9 (1.6) 0.0 (0.0) 86.4 (2.4)
Canada 14.8 (1.0) 0.5 (0.2) 4.7 (0.4) 12.9 (0.8) 1.3 (0.2) 55.1 (1.1) 0.4 (0.2) 86.0 (1.3)
Czech Republic 23.7 (3.1) 1.0 (0.9) 13.8 (1.4) 12.7 (1.7) 5.4 (0.8) 51.5 (2.2) 0.7 (0.7) 80.1 (3.8)
Denmark 10.1 (1.0) 0.3 (0.3) 2.1 (0.4) 14.1 (0.9) 0.3 (0.2) 61.3 (1.1) 0.0 (0.0) 93.4 (1.5)
Estonia 23.9 (1.5) 0.6 (0.4) 12.6 (0.8) 7.3 (0.9) 5.6 (0.5) 40.2 (1.1) 1.6 (0.5) 73.9 (2.0)
Finland 15.3 (2.0) 0.4 (0.4) 5.0 (0.8) 8.6 (1.2) 1.3 (0.3) 48.8 (1.5) 0.0 (0.0) 87.4 (1.3)
France 27.0 (1.2) m m 9.9 (0.7) m m 3.1 (0.4) m m 0.8 (0.5) m m
Germany 20.7 (2.1) 0.6 (0.3) 9.1 (1.2) 14.2 (1.0) 3.2 (0.6) 59.1 (2.0) 0.7 (0.4) 89.5 (2.1)
Ireland 25.0 (1.9) 0.4 (0.3) 10.6 (0.8) 9.7 (1.1) 4.6 (0.7) 41.7 (1.8) 0.8 (0.6) 77.2 (2.3)
Italy 42.5 (2.3) m m 23.7 (1.3) m m 9.9 (1.3) m m 3.8 (2.9) m m
Japan 48.0 (3.8) 0.0 (0.0) 19.8 (1.5) 6.6 (1.1) 6.2 (0.7) 36.9 (1.4) 1.6 (0.4) 67.1 (1.9)
Korea 51.3 (2.1) 0.0 (0.0) 17.7 (0.9) 8.5 (0.7) 5.4 (0.5) 49.2 (1.7) 1.7 (0.8) 82.7 (2.5)
Netherlands 14.5 (1.8) 0.0 (0.0) 3.7 (0.6) 8.6 (1.0) 0.7 (0.2) 54.8 (1.3) 0.0 (0.0) 90.9 (1.4)
Norway 5.9 (1.2) 1.1 (0.7) 2.0 (0.4) 14.0 (1.5) 0.7 (0.2) 58.1 (1.9) 0.0 (0.0) 90.8 (1.4)
Poland 41.8 (1.9) 0.5 (0.3) 21.8 (1.0) 5.9 (0.7) 9.6 (1.0) 32.6 (1.6) 2.9 (1.2) 57.4 (3.2)
Slovak Republic 51.8 (2.8) 0.5 (0.4) 26.3 (1.2) 7.6 (0.9) 13.5 (0.9) 39.1 (1.6) 6.2 (1.7) 72.9 (3.8)
Spain 37.4 (1.5) m m 13.8 (0.9) m m 4.5 (0.8) m m 1.3 (0.9) m m
Sweden 7.4 (1.4) 0.7 (0.5) 1.9 (0.6) 15.0 (1.6) 0.1 (0.2) 58.8 (1.6) 0.0 (0.0) 93.8 (1.5)
United States 21.2 (1.9) 0.0 (0.0) 4.0 (0.6) 9.7 (1.3) 0.7 (0.3) 51.3 (1.8) 0.0 (0.0) 90.1 (1.9)
England (UK) 11.7 (1.3) 1.3 (0.8) 5.0 (0.6) 13.5 (1.3) 1.5 (0.4) 52.9 (2.2) 0.2 (0.2) 85.7 (2.2)
Average1
23.9 (0.5) 0.4 (0.1) 9.8 (0.2) 10.6 (0.3) 3.6 (0.1) 50.1 (0.4) 0.9 (0.1) 83.0 (0.5)
Average-222
25.5 (0.4) m m 10.6 (0.2) m m 3.9 (0.1) m m 1.0 (0.2) m m
Partners
Cyprus3
33.8 (2.3) m m 23.7 (1.1) m m 18.5 (1.2) m m 11.0 (3.0) m m
Russian Federation4
22.0 (4.9) 2.4 (1.2) 20.9 (2.3) 10.8 (1.6) 16.9 (2.3) 36.4 (2.7) 10.6 (3.6) 63.2 (5.8)

[Part 1/1]
Table A4.1
Percentage of adults scoring at Level 2 or 3 in problem solving in technology-rich environments or have
no computer experience, by employment status
Non-worker
Worker
(working at the time of the survey or had worked
in the 12 months prior to it)
OECD % S.E. % S.E. % S.E. % S.E.
National entities
Australia 12.0 (1.1) 25.0 (2.2) 2.0 0.2 42.1 (1.1)
Austria 23.0 (1.4) 22.8 (1.5) 6.3 0.4 35.8 (1.0)
Canada 12.0 (0.7) 21.7 (1.2) 3.1 0.2 39.9 (0.6)
Czech Republic 21.4 (1.2) 29.5 (2.0) 6.2 0.5 34.8 (1.3)
Denmark 8.3 (0.8) 22.6 (1.7) 1.2 0.1 42.4 (0.8)
Estonia 25.6 (0.9) 21.4 (1.1) 5.8 0.3 29.3 (0.9)
Finland 11.0 (1.0) 27.1 (1.7) 1.7 0.2 45.2 (0.9)
France 17.9 (0.8) m m 7.6 0.4 m m
Germany 16.4 (1.5) 26.1 (1.5) 6.0 0.5 39.0 (1.0)
Ireland 17.6 (0.9) 16.6 (1.2) 6.6 0.4 29.5 (1.1)
Italy 36.3 (1.3) m m 17.5 1.0 m m
Japan 17.4 (1.5) 27.6 (1.7) 8.4 0.5 37.0 (0.9)
Korea 19.8 (0.9) 31.8 (1.4) 14.1 0.5 30.0 (1.0)
Netherlands 9.2 (1.2) 21.3 (1.6) 1.7 0.2 47.2 (0.9)
Norway 7.0 (1.0) 21.5 (1.9) 0.7 0.1 45.5 (0.8)
Poland 31.8 (1.2) 14.7 (0.8) 13.6 0.5 21.3 (1.0)
Slovak Republic 35.1 (1.1) 19.4 (1.2) 15.8 0.7 28.7 (0.9)
Spain 29.6 (0.9) m m 11.6 0.5 m m
Sweden 5.3 (0.9) 26.8 (1.7) 0.8 0.2 47.5 (0.8)
United States 11.8 (1.0) 21.9 (1.7) 4.0 0.4 35.0 (1.3)
Flanders (Belgium) 17.1 (0.9) 29.8 (1.3) 4.6 0.3 38.7 (1.1)
England (UK) 11.1 (0.9) 19.3 (1.6) 2.1 0.3 40.3 (1.0)
Northern Ireland (UK) 18.9 (1.5) 16.7 (1.9) 6.7 0.6 34.7 (1.5)
England/N. Ireland (UK) 11.5 (0.9) 19.2 (1.6) 2.3 0.3 40.1 (1.0)
Average1
16.5 (0.2) 23.5 (0.4) 5.5 (0.1) 37.3 (0.2)
Average-222
18.1 (0.2) m m 6.4 (0.1) m m
Partners
Cyprus3
30.2 (1.0) m m 18.8 0.7 m m
Russian Federation4
25.2 (2.7) 23.8 (4.0) 15.2 1.6 26.9 (1.9)

[Part 1/1]
Table A4.2a Frequency of e-mail use at work
Frequency of usage
Never
Less than once
a month
but at least once
a month
but not everyday Everyday Missing
National entities
Australia 33.8 (0.7) 2.3 (0.3) 2.0 (0.2) 6.5 (0.4) 53.1 (0.8) 2.3 (0.2)
Austria 38.5 (0.8) 3.2 (0.3) 2.8 (0.3) 6.8 (0.4) 46.3 (0.8) 2.4 (0.2)
Canada 36.7 (0.6) 2.2 (0.2) 2.3 (0.2) 5.3 (0.3) 52.3 (0.5) 1.2 (0.1)
Czech Republic 44.1 (1.3) 1.4 (0.2) 2.0 (0.3) 7.2 (0.7) 44.4 (1.2) 0.9 (0.3)
Denmark 30.2 (0.6) 2.7 (0.2) 3.4 (0.3) 7.4 (0.4) 55.7 (0.7) 0.6 (0.1)
Estonia 44.9 (0.7) 1.7 (0.2) 1.8 (0.2) 5.0 (0.3) 45.8 (0.7) 0.8 (0.1)
Finland 28.3 (0.6) 3.6 (0.3) 3.4 (0.3) 9.5 (0.5) 55.1 (0.6) 0.1 (0.1)
France 44.8 (0.6) 1.9 (0.2) 1.5 (0.2) 4.5 (0.3) 46.2 (0.5) 1.1 (0.1)
Germany 41.7 (0.8) 2.4 (0.3) 2.8 (0.2) 6.4 (0.5) 44.7 (0.8) 1.9 (0.2)
Ireland 45.5 (1.0) 2.4 (0.3) 2.2 (0.2) 5.9 (0.4) 43.3 (1.0) 0.6 (0.2)
Italy 57.2 (1.0) 1.5 (0.3) 1.2 (0.3) 4.4 (0.4) 34.6 (0.9) 1.1 (0.3)
Japan 47.1 (0.8) 4.6 (0.4) 4.1 (0.3) 7.4 (0.4) 34.9 (0.8) 1.9 (0.2)
Korea 49.9 (0.8) 3.0 (0.3) 5.7 (0.4) 10.6 (0.5) 30.3 (0.6) 0.5 (0.1)
Netherlands 27.1 (0.6) 1.6 (0.2) 2.0 (0.2) 6.2 (0.4) 60.2 (0.6) 2.9 (0.2)
Norway 25.0 (0.5) 2.7 (0.3) 2.8 (0.2) 8.6 (0.4) 58.3 (0.6) 2.7 (0.2)
Poland 55.1 (0.8) 1.8 (0.2) 2.0 (0.2) 7.1 (0.5) 33.6 (0.8) 0.4 (0.1)
Slovak Republic 52.2 (1.1) 1.7 (0.2) 1.7 (0.2) 7.8 (0.5) 36.1 (1.0) 0.5 (0.1)
Spain 53.8 (0.7) 1.5 (0.2) 1.4 (0.2) 4.3 (0.3) 37.5 (0.7) 1.5 (0.2)
Sweden 27.4 (0.7) 4.1 (0.3) 3.6 (0.3) 8.6 (0.5) 56.1 (0.6) 0.2 (0.1)
United States 35.1 (1.0) 2.3 (0.2) 2.6 (0.3) 6.5 (0.4) 48.2 (0.9) 5.2 (0.7)
England (UK) 34.1 (0.8) 2.5 (0.3) 2.1 (0.3) 5.7 (0.4) 53.7 (0.9) 1.8 (0.2)
Average1
38.3 (0.2) 2.5 (0.1) 2.7 (0.1) 7.0 (0.1) 47.7 (0.2) 1.8 (0.1)
Average-222
40.2 (0.2) 2.4 (0.1) 2.5 (0.1) 6.7 (0.1) 46.5 (0.2) 1.7 (0.1)
Partners
Cyprus3
43.3 (0.8) 2.5 (0.2) 1.9 (0.2) 4.1 (0.4) (24.5) (0.7) 23.8 (0.5)
Russian Federation4
66.5 (1.9) 3.9 (0.5) 2.9 (0.6) 6.7 (0.8) (19.7) (1.5) 0.3 (0.1)

[Part 1/1]
Table A4.2b Frequency of Internet use to better understand issues related to work
Frequency of usage
Never
Less than once
a month
but at least once
a month
National entities
Australia 35.6 (0.8) 4.2 (0.3) 5.3 (0.3) 13.7 (0.6) 38.8 (0.8) 2.3 (0.2)
Austria 41.6 (0.8) 5.0 (0.4) 5.7 (0.4) 14.3 (0.6) 31.0 (0.8) 2.4 (0.2)
Canada 37.6 (0.6) 5.4 (0.3) 5.7 (0.3) 13.5 (0.4) 36.6 (0.6) 1.2 (0.1)
Czech Republic 46.3 (1.4) 3.6 (0.5) 3.9 (0.5) 9.7 (0.6) 35.7 (1.3) 0.9 (0.3)
Denmark 31.7 (0.6) 5.6 (0.3) 7.3 (0.4) 17.1 (0.5) 37.7 (0.8) 0.6 (0.1)
Estonia 43.9 (0.7) 3.3 (0.2) 4.3 (0.3) 11.5 (0.4) 36.1 (0.6) 0.8 (0.1)
Finland 29.9 (0.6) 7.5 (0.4) 9.5 (0.5) 20.2 (0.7) 32.7 (0.7) 0.1 (0.1)
France 49.7 (0.6) 5.9 (0.3) 5.4 (0.3) 12.6 (0.4) 25.4 (0.6) 1.1 (0.1)
Germany 43.4 (0.9) 4.1 (0.3) 5.6 (0.4) 16.3 (0.7) 28.6 (0.7) 1.9 (0.2)
Ireland 47.5 (0.9) 4.2 (0.4) 4.6 (0.3) 12.4 (0.5) 30.6 (0.9) 0.6 (0.2)
Italy 59.0 (1.0) 2.7 (0.3) 2.4 (0.3) 8.8 (0.6) 26.0 (0.9) 1.1 (0.3)
Japan 42.8 (0.7) 5.6 (0.4) 7.3 (0.5) 15.2 (0.6) 27.3 (0.7) 1.9 (0.2)
Korea 45.3 (0.7) 3.0 (0.3) 5.9 (0.3) 13.9 (0.6) 31.4 (0.7) 0.5 (0.1)
Netherlands 31.9 (0.6) 5.4 (0.4) 6.4 (0.3) 13.5 (0.6) 39.9 (0.8) 2.9 (0.2)
Norway 25.4 (0.6) 7.4 (0.4) 8.5 (0.4) 21.3 (0.5) 34.7 (0.7) 2.7 (0.2)
Poland 54.0 (0.8) 2.5 (0.3) 2.9 (0.3) 11.6 (0.6) 28.6 (0.8) 0.4 (0.1)
Slovak Republic 53.7 (1.0) 3.5 (0.3) 3.7 (0.3) 11.3 (0.5) 27.2 (0.9) 0.5 (0.1)
Spain 55.3 (0.7) 3.1 (0.3) 2.6 (0.3) 8.2 (0.5) 29.4 (0.7) 1.5 (0.2)
Sweden 31.6 (0.7) 8.5 (0.4) 9.4 (0.5) 18.8 (0.7) 31.4 (0.6) 0.3 (0.1)
United States 35.5 (1.0) 4.6 (0.4) 5.4 (0.3) 12.5 (0.5) 36.8 (1.0) 5.2 (0.7)
England (UK) 36.2 (0.9) 5.7 (0.5) 5.4 (0.4) 16.0 (0.8) 34.9 (0.8) 1.8 (0.2)
Average1
39.4 (0.2) 4.9 (0.1) 5.9 (0.1) 14.6 (0.1) 33.4 (0.2) 1.8 (0.1)
Average-222
41.5 (0.2) 4.8 (0.1) 5.6 (0.1) 13.9 (0.1) 32.5 (0.2) 1.7 (0.1)
Partners
Cyprus3
45.4 (0.8) 3.4 (0.3) 2.8 (0.3) 6.4 (0.4) (18.1) (0.7) 23.8 (0.5)
Russian Federation4
64.1 (1.6) 5.8 (0.5) 3.4 (0.5) 9.8 (0.9) (16.6) (1.1) 0.3 (0.1)

[Part 1/1]
Table A4.2c
Frequency of Internet use for conducting transactions (e.g. buying or selling products or services, or
banking) at work
Frequency of usage
Never
Less than once
a month
but at least once
a month
National entities
Australia 62.2 (0.8) 6.1 (0.4) 5.2 (0.4) 10.0 (0.5) 14.3 (0.6) 2.3 (0.2)
Austria 72.1 (0.6) 5.1 (0.4) 4.9 (0.3) 7.5 (0.4) 8.0 (0.5) 2.4 (0.2)
Canada 68.4 (0.5) 5.7 (0.3) 5.4 (0.3) 7.8 (0.3) 11.4 (0.4) 1.2 (0.1)
Czech Republic 72.2 (1.2) 5.1 (0.5) 4.0 (0.4) 9.0 (0.8) 8.8 (0.6) 0.9 (0.3)
Denmark 62.3 (0.6) 8.6 (0.4) 6.8 (0.3) 11.1 (0.4) 10.5 (0.4) 0.6 (0.1)
Estonia 65.4 (0.7) 4.9 (0.3) 5.0 (0.3) 9.9 (0.4) 14.1 (0.5) 0.8 (0.1)
Finland 66.7 (0.6) 6.7 (0.3) 6.5 (0.4) 13.0 (0.5) 6.8 (0.4) 0.1 (0.1)
France 81.1 (0.4) 4.4 (0.3) 3.3 (0.2) 4.5 (0.3) 5.6 (0.3) 1.1 (0.1)
Germany 76.1 (0.8) 4.1 (0.3) 3.4 (0.3) 7.1 (0.4) 7.5 (0.5) 1.9 (0.2)
Ireland 72.7 (0.8) 5.0 (0.4) 4.1 (0.3) 6.9 (0.4) 10.7 (0.6) 0.6 (0.2)
Italy 82.0 (0.8) 3.8 (0.4) 2.5 (0.3) 4.6 (0.4) 6.0 (0.5) 1.1 (0.3)
Japan 81.4 (0.6) 4.9 (0.3) 3.9 (0.3) 3.5 (0.3) 4.4 (0.3) 1.9 (0.2)
Korea 62.6 (0.7) 4.3 (0.3) 8.3 (0.4) 12.3 (0.5) 12.0 (0.5) 0.5 (0.1)
Netherlands 67.2 (0.7) 6.3 (0.4) 4.8 (0.3) 8.8 (0.4) 10.0 (0.5) 2.9 (0.2)
Norway 62.0 (0.7) 7.5 (0.4) 6.9 (0.4) 12.6 (0.5) 8.3 (0.4) 2.7 (0.2)
Poland 77.7 (0.7) 4.3 (0.3) 3.6 (0.3) 6.4 (0.5) 7.6 (0.5) 0.4 (0.1)
Slovak Republic 75.9 (0.8) 3.7 (0.4) 3.6 (0.3) 7.1 (0.4) 9.1 (0.6) 0.5 (0.1)
Spain 82.2 (0.6) 3.0 (0.2) 2.6 (0.3) 3.5 (0.3) 7.2 (0.4) 1.5 (0.2)
Sweden 69.2 (0.7) 7.2 (0.4) 7.8 (0.4) 9.1 (0.5) 6.4 (0.4) 0.2 (0.1)
United States 60.2 (1.0) 6.7 (0.4) 6.2 (0.4) 8.4 (0.4) 13.3 (0.8) 5.2 (0.7)
England (UK) 65.6 (1.0) 6.4 (0.4) 4.8 (0.4) 9.1 (0.5) 12.3 (0.8) 1.8 (0.2)
Average1
68.8 (0.2) 5.7 (0.1) 5.2 (0.1) 8.8 (0.1) 9.7 (0.1) 1.8 (0.1)
Average-222
70.6 (0.2) 5.4 (0.1) 4.9 (0.1) 8.2 (0.1) 9.3 (0.1) 1.7 (0.1)
Partners
Cyprus3
64.1 (0.7) 3.2 (0.3) 2.1 (0.3) 2.1 (0.3) (4.6) (0.4) 23.8 (0.5)
Russian Federation4
87.2 (1.1) 4.2 (0.5) 2.3 (0.4) 2.4 (0.3) (3.5) (0.4) 0.3 (0.1)

[Part 1/1]
Table A4.2d Frequency of spreadsheet software (e.g. Excel) use at work
Frequency of usage
Never
Less than once
a month
but at least once
a month
National entities
Australia 45.0 (0.8) 7.1 (0.4) 6.7 (0.3) 13.8 (0.6) 25.1 (0.7) 2.4 (0.2)
Austria 51.4 (0.8) 7.7 (0.5) 7.3 (0.4) 12.6 (0.6) 18.6 (0.7) 2.4 (0.2)
Canada 50.3 (0.5) 6.1 (0.2) 6.6 (0.2) 11.1 (0.3) 24.8 (0.5) 1.2 (0.1)
Czech Republic 50.8 (1.3) 5.7 (0.5) 5.9 (0.6) 14.0 (0.9) 22.8 (1.2) 0.9 (0.3)
Denmark 51.9 (0.7) 8.6 (0.4) 7.7 (0.3) 12.7 (0.5) 18.4 (0.5) 0.6 (0.1)
Estonia 53.4 (0.7) 7.3 (0.3) 6.8 (0.3) 11.6 (0.4) 20.0 (0.5) 0.8 (0.1)
Finland 50.9 (0.6) 11.5 (0.5) 10.3 (0.5) 14.2 (0.5) 12.9 (0.5) 0.2 (0.1)
France 55.5 (0.6) 6.5 (0.3) 5.4 (0.3) 9.8 (0.4) 21.7 (0.5) 1.1 (0.1)
Germany 52.7 (0.8) 7.3 (0.4) 6.3 (0.4) 10.8 (0.5) 20.9 (0.7) 2.0 (0.2)
Ireland 57.7 (0.9) 5.4 (0.4) 4.4 (0.3) 8.2 (0.5) 23.7 (0.8) 0.6 (0.2)
Italy 63.2 (0.9) 4.9 (0.5) 4.2 (0.5) 7.2 (0.5) 19.4 (0.7) 1.1 (0.3)
Japan 48.2 (0.8) 6.5 (0.4) 7.6 (0.4) 13.3 (0.5) 22.6 (0.7) 1.9 (0.2)
Korea 56.7 (0.7) 3.4 (0.3) 7.4 (0.4) 11.0 (0.5) 21.1 (0.6) 0.5 (0.1)
Netherlands 43.4 (0.7) 7.2 (0.4) 8.6 (0.4) 14.2 (0.6) 23.7 (0.7) 2.9 (0.2)
Norway 48.4 (0.7) 10.6 (0.4) 9.3 (0.4) 13.4 (0.5) 15.7 (0.5) 2.7 (0.2)
Poland 64.6 (0.7) 6.3 (0.5) 5.9 (0.4) 8.7 (0.5) 14.2 (0.6) 0.4 (0.1)
Slovak Republic 56.8 (1.1) 6.0 (0.5) 5.2 (0.4) 11.6 (0.7) 19.8 (0.9) 0.5 (0.1)
Spain 64.0 (0.8) 4.1 (0.3) 4.1 (0.4) 7.7 (0.4) 18.7 (0.6) 1.5 (0.2)
Sweden 50.7 (0.7) 10.8 (0.5) 9.0 (0.4) 12.9 (0.5) 16.5 (0.5) 0.2 (0.1)
United States 48.5 (1.0) 6.3 (0.5) 7.2 (0.5) 10.8 (0.6) 21.9 (0.8) 5.2 (0.7)
England (UK) 46.3 (0.9) 6.1 (0.4) 6.5 (0.5) 11.9 (0.6) 27.4 (0.9) 1.8 (0.2)
Average1
51.2 (0.2) 7.2 (0.1) 7.1 (0.1) 12.0 (0.1) 20.7 (0.2) 1.8 (0.1)
Average-222
52.5 (0.2) 6.9 (0.1) 6.7 (0.1) 11.5 (0.1) 20.6 (0.1) 1.7 (0.0)
Partners
Cyprus3
49.9 (0.7) 4.2 (0.4) 2.9 (0.3) 5.3 (0.3) (13.9) (0.5) 23.8 (0.5)
Russian Federation4
67.1 (1.0) 6.8 (0.7) 4.1 (0.5) 7.8 (0.6) (13.9) (0.8) 0.3 (0.1)

[Part 1/1]
Table A4.2e Frequency of a word processor (e.g. Word) use at work
Frequency of usage
Never
Less than once
a month
but at least once
a month
National entities
Australia 41.7 (0.8) 4.5 (0.3) 5.5 (0.4) 12.9 (0.6) 33.2 (0.7) 2.3 (0.2)
Austria 42.3 (0.8) 6.3 (0.4) 7.1 (0.4) 14.4 (0.5) 27.5 (0.8) 2.4 (0.2)
Canada 44.0 (0.6) 5.1 (0.2) 5.9 (0.3) 12.4 (0.4) 31.5 (0.5) 1.2 (0.1)
Czech Republic 46.5 (1.2) 4.3 (0.5) 5.7 (0.5) 15.8 (0.9) 26.9 (1.1) 0.9 (0.3)
Denmark 36.9 (0.6) 5.8 (0.3) 8.5 (0.4) 15.3 (0.6) 32.8 (0.6) 0.7 (0.1)
Estonia 50.7 (0.7) 5.4 (0.3) 6.5 (0.3) 14.2 (0.6) 22.5 (0.5) 0.8 (0.1)
Finland 36.2 (0.5) 10.0 (0.5) 11.9 (0.4) 20.9 (0.6) 20.8 (0.5) 0.2 (0.1)
France 51.3 (0.5) 5.5 (0.3) 5.3 (0.2) 10.6 (0.3) 26.2 (0.5) 1.1 (0.1)
Germany 42.6 (0.9) 4.8 (0.4) 5.6 (0.5) 13.7 (0.6) 31.3 (0.7) 2.0 (0.2)
Ireland 51.2 (1.0) 3.6 (0.3) 3.4 (0.3) 11.2 (0.6) 29.9 (0.8) 0.7 (0.2)
Italy 59.0 (0.9) 4.0 (0.4) 3.2 (0.4) 9.0 (0.6) 23.7 (0.8) 1.1 (0.3)
Japan 47.0 (0.9) 9.1 (0.4) 9.5 (0.6) 14.5 (0.5) 18.0 (0.6) 1.9 (0.2)
Korea 53.4 (0.8) 3.7 (0.3) 7.7 (0.4) 12.8 (0.5) 21.8 (0.5) 0.5 (0.1)
Netherlands 33.8 (0.6) 4.5 (0.3) 5.5 (0.4) 13.1 (0.5) 40.2 (0.7) 2.9 (0.2)
Norway 31.9 (0.5) 8.4 (0.5) 9.1 (0.4) 18.6 (0.6) 29.4 (0.6) 2.7 (0.2)
Poland 57.2 (0.8) 3.4 (0.3) 4.9 (0.4) 11.9 (0.5) 22.2 (0.7) 0.4 (0.1)
Slovak Republic 52.8 (1.0) 3.1 (0.3) 4.0 (0.4) 13.2 (0.6) 26.4 (0.9) 0.5 (0.1)
Spain 57.4 (0.8) 3.1 (0.3) 3.4 (0.3) 8.9 (0.5) 25.8 (0.7) 1.5 (0.2)
Sweden 37.2 (0.8) 10.0 (0.5) 10.3 (0.5) 17.8 (0.7) 24.6 (0.7) 0.2 (0.1)
United States 43.0 (1.0) 5.1 (0.4) 6.3 (0.4) 12.2 (0.6) 28.2 (0.7) 5.2 (0.7)
England (UK) 38.6 (0.8) 4.9 (0.3) 4.6 (0.4) 13.5 (0.6) 36.5 (0.9) 1.8 (0.2)
Average1
43.4 (0.2) 5.6 (0.1) 6.7 (0.1) 14.3 (0.1) 28.1 (0.2) 1.8 (0.1)
Average-222
45.1 (0.2) 5.4 (0.1) 6.4 (0.1) 13.7 (0.1) 27.7 (0.2) 1.7 (0.1)
Partners
Cyprus3
43.6 (0.8) 3.3 (0.3) 2.8 (0.3) 6.7 (0.4) (19.8) (0.6) 23.8 (0.5)
Russian Federation4
62.0 (1.0) 4.1 (0.6) 3.2 (0.4) 9.8 (0.9) (20.6) (1.4) 0.3 (0.1)

[Part 1/1]
Table A4.2f Use of a computer at work
Yes No Missing
OECD % S.E. % S.E. % S.E.
National entities
Australia 74.7 (0.7) 23.0 (0.6) 2.3 (0.2)
Austria 69.4 (0.8) 28.2 (0.8) 2.4 (0.2)
Canada 73.4 (0.5) 25.4 (0.5) 1.2 (0.1)
Czech Republic 64.4 (1.2) 34.8 (1.2) 0.9 (0.3)
Denmark 78.7 (0.6) 20.7 (0.6) 0.6 (0.1)
Estonia 63.2 (0.7) 36.0 (0.7) 0.8 (0.1)
Finland 79.7 (0.6) 20.2 (0.6) 0.1 (0.0)
France 64.8 (0.6) 34.2 (0.6) 1.0 (0.1)
Germany 67.8 (0.8) 30.3 (0.8) 1.9 (0.2)
Ireland 64.9 (0.8) 34.5 (0.8) 0.6 (0.2)
Italy 49.4 (1.1) 49.5 (1.1) 1.1 (0.3)
Japan 69.5 (0.7) 28.6 (0.7) 1.9 (0.2)
Korea 62.7 (0.8) 36.9 (0.8) 0.5 (0.1)
Netherlands 77.5 (0.5) 19.7 (0.5) 2.9 (0.2)
Norway 80.7 (0.5) 16.6 (0.5) 2.7 (0.2)
Poland 53.5 (0.8) 46.2 (0.8) 0.4 (0.1)
Slovak Republic 55.7 (1.0) 43.8 (1.0) 0.5 (0.1)
Spain 54.6 (0.8) 43.9 (0.8) 1.5 (0.2)
Sweden 81.9 (0.7) 18.0 (0.7) 0.2 (0.1)
United States 70.4 (0.7) 24.4 (0.8) 5.2 (0.7)
Flanders (Belgium) 69.2 (0.8) 23.9 (0.7) 7.0 (0.3)
England (UK) 73.7 (0.8) 24.5 (0.8) 1.8 (0.2)
Northern Ireland (UK) 69.2 (1.0) 27.6 (1.0) 3.2 (0.4)
England/N. Ireland (UK) 73.6 (0.8) 24.6 (0.8) 1.8 (0.2)
Average1
70.0 (0.2) 28.2 (0.2) 1.8 (0.1)
Average-222
68.2 (0.2) 30.1 (0.2) 1.7 (0.0)
Partners
Cyprus3
43.1 (0.8) 33.1 (0.8) 23.8 (0.5)
Russian Federation4
45.0 (1.4) 54.8 (1.3) 0.2 (0.1)

[Part 1/1]
Table A4.3
Percentage of workers scoring at Level 2 or 3 in problem solving in technology-rich environments or
having no computer experience, by frequency of complex problem solving
Less than monthly or never At least monthly
OECD % S.E. % S.E. % S.E. % S.E.
National entities
Australia 3.6 (0.4) 32.3 (1.6) 1.2 (0.2) 47.7 (1.5)
Austria 10.6 (0.8) 24.6 (1.3) 2.5 (0.4) 45.7 (1.5)
Canada 5.2 (0.4) 31.6 (0.9) 1.4 (0.2) 46.3 (0.9)
Czech Republic 10.9 (1.0) 22.7 (1.7) 2.6 (0.6) 44.2 (1.9)
Denmark 2.1 (0.3) 31.6 (1.2) 0.5 (0.1) 50.8 (1.1)
Estonia 8.9 (0.5) 20.9 (1.2) 2.8 (0.3) 37.5 (1.2)
Finland 2.9 (0.5) 35.7 (1.3) 0.7 (0.2) 52.7 (1.2)
France 11.4 (0.6) m m 3.4 (0.3) m m
Germany 10.1 (0.9) 28.0 (1.3) 2.5 (0.4) 48.4 (1.5)
Ireland 10.2 (0.7) 21.9 (1.4) 3.3 (0.5) 36.5 (1.5)
Italy 27.3 (1.7) m m 10.6 (1.0) m m
Japan 12.7 (0.9) 27.7 (1.0) 2.7 (0.4) 49.6 (1.7)
Korea 21.1 (0.9) 22.8 (1.1) 6.6 (0.5) 37.7 (1.4)
Netherlands 2.9 (0.4) 36.8 (1.3) 0.5 (0.2) 56.9 (1.2)
Norway 0.9 (0.2) 35.0 (1.3) 0.5 (0.2) 53.4 (1.2)
Poland 20.1 (0.8) 15.2 (1.0) 6.9 (0.5) 27.4 (1.4)
Slovak Republic 23.4 (1.2) 21.1 (1.3) 10.0 (0.8) 34.6 (1.4)
Spain 16.8 (0.8) m m 6.3 (0.7) m m
Sweden 1.3 (0.3) 38.0 (1.3) 0.5 (0.2) 54.5 (1.1)
United States 6.6 (0.8) 26.1 (1.6) 2.5 (0.4) 39.8 (1.6)
England (UK) 4.1 (0.7) 25.2 (1.6) 0.9 (0.2) 49.3 (1.2)
Average1
8.7 (0.2) 27.6 (0.3) 2.7 (0.1) 45.3 (0.3)
Average-222
10.1 (0.2) m m 3.2 (0.1) m m
Partners
Cyprus3
23.7 (1.3) m m 14.2 (0.9) m m
Russian Federation4
20.6 (2.5) 21.5 (2.5) 11.6 (1.8) 31.2 (2.4)
Note: Complex problems are defined as problems that take at least 30 minutes to find a good solution.

[Part 1/1]
Table A4.4a Percentage of workers, by adequacy of reported computer skills to do their job well
Lack the computer skills to do the job well Has the computer skills to do the job well No use of computer at work
National entities
Australia 6.3 (0.5) 68.3 (0.9) 23.0 (0.6)
Austria 3.0 (0.3) 66.4 (0.9) 28.2 (0.8)
Canada 4.5 (0.2) 68.9 (0.5) 25.4 (0.5)
Czech Republic 2.5 (0.4) 61.8 (1.2) 34.8 (1.2)
Denmark 8.1 (0.4) 70.5 (0.7) 20.7 (0.6)
Estonia 6.9 (0.3) 56.3 (0.7) 36.0 (0.7)
Finland 10.0 (0.5) 69.6 (0.6) 20.2 (0.6)
France 8.6 (0.4) 56.0 (0.6) 34.1 (0.6)
Germany 3.9 (0.4) 63.9 (0.8) 30.3 (0.8)
Ireland 5.2 (0.4) 59.6 (0.8) 34.5 (0.8)
Italy 4.0 (0.4) 45.4 (1.1) 49.5 (1.1)
Japan 25.7 (0.7) 43.8 (0.8) 28.6 (0.7)
Korea 13.6 (0.5) 49.1 (0.6) 36.9 (0.8)
Netherlands 4.8 (0.3) 72.6 (0.7) 19.7 (0.5)
Norway 13.5 (0.5) 67.2 (0.6) 16.6 (0.5)
Poland 4.4 (0.4) 49.0 (0.8) 46.2 (0.8)
Slovak Republic 2.8 (0.3) 52.9 (1.0) 43.8 (1.0)
Spain 5.0 (0.4) 49.6 (0.7) 43.9 (0.8)
Sweden 7.6 (0.4) 74.1 (0.8) 18.0 (0.7)
United States 4.4 (0.3) 66.0 (0.7) 24.4 (0.8)
England (UK) 5.8 (0.4) 67.7 (0.9) 24.5 (0.8)
Average1
7.3 (0.1) 62.6 (0.2) 28.2 (0.2)
Average-222
7.1 (0.1) 61.0 (0.2) 30.1 (0.2)
Partners
Cyprus3
3.5 (0.3) 39.6 (0.7) 33.1 (0.8)
Russian Federation4
3.3 (0.5) 41.4 (1.5) 54.8 (1.3)

[Part 1/1]
Table A4.4b
Percentage of workers by adequacy of reported computer skills affecting the chances of getting
a job, promotion or pay raise
A lack of computer skills has affected the
chances of getting a job/promotion/pay raise
A lack of computer skills has not affected the
chances of getting a job/promotion/pay raise No use of computer at work
National entities
Australia 6.3 (0.4) 68.2 (0.8) 23.0 (0.6)
Austria 3.1 (0.3) 66.3 (0.9) 28.2 (0.8)
Canada 6.1 (0.3) 67.1 (0.5) 25.4 (0.5)
Czech Republic 2.5 (0.4) 61.8 (1.2) 34.8 (1.2)
Denmark 3.9 (0.3) 74.6 (0.6) 20.7 (0.6)
Estonia 5.4 (0.3) 57.7 (0.7) 36.0 (0.7)
Finland 3.5 (0.3) 76.0 (0.7) 20.2 (0.6)
France 4.8 (0.3) 59.4 (0.6) 34.1 (0.6)
Germany 2.8 (0.3) 64.8 (0.9) 30.3 (0.8)
Ireland 4.4 (0.3) 60.4 (0.8) 34.5 (0.8)
Italy 3.6 (0.3) 45.7 (1.1) 49.5 (1.1)
Japan 16.3 (0.6) 53.1 (0.8) 28.6 (0.7)
Korea 1.7 (0.2) 60.9 (0.7) 36.9 (0.8)
Netherlands 3.0 (0.3) 74.4 (0.6) 19.7 (0.5)
Norway 4.6 (0.3) 75.8 (0.5) 16.6 (0.5)
Poland 5.4 (0.3) 48.0 (0.9) 46.2 (0.8)
Slovak Republic 3.0 (0.3) 52.6 (1.0) 43.8 (1.0)
Spain 3.7 (0.4) 50.7 (0.8) 43.9 (0.8)
Sweden 3.5 (0.3) 77.6 (0.6) 18.0 (0.7)
United States 6.9 (0.4) 63.5 (0.8) 24.4 (0.8)
England (UK) 4.8 (0.4) 68.8 (0.8) 24.5 (0.8)
Average1
4.8 (0.1) 65.1 (0.2) 28.2 (0.2)
Average-222
4.7 (0.1) 63.3 (0.2) 30.1 (0.2)
Partners
Cyprus3
4.4 (0.4) 38.6 (0.8) 33.1 (0.8)
Russian Federation4
5.1 (0.6) 39.6 (1.3) 54.8 (1.3)

[Part 1/1]
Table A4.5
Percentage of workers who reported that their lack of computer skills either have or have not affected
their chances of getting a job, promotion or pay raise
A lack of computer skills has not affected the chances
of getting a job/promotion/pay raise
A lack of computer skills has affected the chances of getting
a job/promotion/pay raise
Has the computer skills to do
the job well
Lack the computer skills to do
the job well
Has the computer skills to do
the job wel
Lack the computer skills to do
the job well
OECD % S.E. % S.E. % S.E. % S.E.
National entities
Australia 94.7 (0.4) 5.3 (0.4) 77.2 (3.4) 22.8 (3.4)
Austria 97.0 (0.3) 2.9 (0.3) 90.5 (2.7) 9.5 (2.7)
Canada 96.2 (0.2) 3.8 (0.2) 84.7 (1.4) 15.3 (1.4)
Czech Republic 97.6 (0.4) 2.3 (0.4) 90.2 (3.4) 9.8 (3.4)
Denmark 92.5 (0.4) 7.4 (0.4) 73.5 (3.7) 26.5 (3.7)
Estonia 93.9 (0.3) 6.1 (0.3) 77.9 (2.7) 21.7 (2.7)
Finland 90.3 (0.5) 9.6 (0.5) 78.8 (3.4) 21.2 (3.4)
France 91.9 (0.4) 8.0 (0.4) 79.1 (2.9) 20.9 (2.9)
Germany 96.4 (0.4) 3.6 (0.4) 83.4 (4.3) 15.1 (4.2)
Ireland 95.5 (0.4) 4.5 (0.4) 80.1 (3.5) 19.9 (3.5)
Italy 96.2 (0.4) 3.8 (0.4) 89.7 (3.3) 10.3 (3.3)
Japan 74.4 (0.9) 25.6 (0.9) 70.9 (1.9) 29.1 (1.9)
Korea 86.9 (0.5) 13.1 (0.5) 57.5 (5.4) 42.5 (5.4)
Netherlands 95.4 (0.3) 4.6 (0.3) 86.2 (3.3) 13.8 (3.3)
Norway 87.1 (0.6) 12.9 (0.6) 67.9 (3.0) 32.1 (3.0)
Poland 95.7 (0.4) 4.1 (0.4) 90.6 (2.5) 9.4 (2.5)
Slovak Republic 97.2 (0.3) 2.8 (0.3) 96.5 (1.5) 3.5 (1.5)
Spain 95.5 (0.4) 4.5 (0.4) 80.4 (3.6) 19.6 (3.6)
Sweden 93.0 (0.4) 6.9 (0.4) 78.3 (4.5) 21.7 (4.5)
United States 96.7 (0.3) 3.3 (0.3) 78.4 (2.4) 21.2 (2.4)
England (UK) 94.7 (0.4) 5.2 (0.4) 79.5 (2.7) 20.5 (2.7)
Average1
93.1 (0.1) 6.9 (0.1) 80.4 (0.7) 19.4 (0.7)
Average-222
93.3 (0.1) 6.7 (0.1) 80.8 (0.7) 19.1 (0.7)
Partners
Cyprus3
96.0 (0.3) 4.0 (0.3) 85.1 (2.9) 14.9 (2.9)
Russian Federation4
97.2 (0.5) 2.6 (0.5) 83.7 (4.6) 16.2 (4.6)

[Part 1/1]
Table A4.6
Labour force participation rate, by proficiency in problem solving in technology-rich environments
among adults aged 25-65
No computer
experience Failed ICT core Opted out Below Level 1 Level 1 Level 2/3 Total
OECD % S.E. % S.E. % S.E. % S.E. % S.E. % S.E. % S.E.
National entities
Australia 38.6 (2.9) 60.7 (4.3) 69.1 (1.3) 76.2 (2.7) 81.5 (1.3) 88.6 (0.9) 78.6 (0.3)
Austria 46.8 (2.2) 70.1 (3.9) 70.5 (2.0) 77.7 (3.3) 84.6 (1.5) 90.4 (0.9) 78.7 (0.6)
Canada 52.6 (2.5) 74.1 (1.8) 72.2 (1.7) 77.7 (1.3) 84.2 (0.8) 90.5 (0.7) 82.1 (0.4)
Czech Republic 43.0 (2.5) 73.4 (5.2) 77.1 (2.3) 78.2 (2.5) 79.7 (1.7) 87.7 (1.4) 76.8 (0.2)
Denmark 35.2 (4.1) 71.0 (2.7) 60.5 (2.7) 71.6 (2.0) 85.2 (1.0) 91.8 (0.8) 81.5 (0.4)
Estonia 47.3 (1.9) 80.8 (2.3) 79.1 (1.2) 86.9 (1.5) 91.3 (0.9) 93.9 (0.7) 83.2 (0.4)
Finland 32.6 (3.9) 62.8 (3.3) 59.9 (2.4) 70.8 (1.9) 86.4 (1.0) 91.4 (0.7) 79.6 (0.6)
France 50.0 (1.7) 68.9 (1.9) 72.1 (1.3) m m m m m m m m
Germany 59.9 (2.8) 80.1 (3.4) 72.5 (3.0) 79.6 (2.2) 86.6 (1.2) 91.5 (0.8) 83.4 (0.6)
Ireland 48.0 (2.4) 75.7 (3.4) 65.7 (1.8) 70.1 (2.3) 81.1 (1.5) 88.3 (1.3) 73.9 (0.7)
Italy 48.1 (1.7) 71.1 (5.8) 70.1 (2.3) m m m m m m m m
Japan 60.2 (2.5) 75.8 (2.1) 72.9 (1.7) 77.4 (2.6) 80.6 (1.8) 86.0 (1.0) 78.0 (0.3)
Korea 64.3 (1.2) 75.8 (1.9) 76.5 (2.3) 78.9 (2.0) 79.0 (1.3) 84.4 (1.4) 77.1 (0.5)
Netherlands 43.3 (4.2) 66.9 (3.9) 57.2 (3.7) 68.0 (2.6) 83.5 (1.1) 92.8 (0.8) 81.4 (0.5)
Norway 32.6 (5.7) 78.7 (2.9) 63.7 (2.9) 76.1 (2.4) 88.4 (1.1) 94.0 (0.7) 85.4 (0.5)
Poland 49.5 (1.6) 73.4 (3.2) 72.2 (1.4) 78.7 (2.2) 85.8 (1.7) 91.8 (1.2) 72.9 (0.6)
Slovak Republic 52.8 (1.6) 73.9 (4.7) 73.2 (1.9) 80.2 (2.3) 84.4 (1.3) 88.8 (1.3) 75.1 (0.6)
Spain 48.3 (1.4) 74.5 (3.0) 71.1 (2.0) m m m m m m m m
Sweden 39.9 (7.6) 74.4 (3.7) 66.5 (3.1) 77.2 (2.4) 87.2 (1.3) 92.9 (0.8) 85.0 (0.5)
United States 56.8 (3.0) 71.8 (3.9) 69.4 (2.7) 82.8 (1.6) 85.8 (1.2) 90.0 (0.9) 82.8 (0.7)
Flanders (Belgium) 39.7 (2.2) 69.7 (3.3) 65.9 (3.0) 72.1 (1.7) 83.7 (0.9) 91.7 (0.7) 78.7 (0.3)
England (UK) 40.9 (4.0) 69.6 (3.0) 68.0 (2.8) 73.8 (1.9) 81.8 (1.0) 90.5 (0.8) 79.9 (0.2)
Northern Ireland (UK) 48.0 (3.2) 64.2 (4.2) 51.0 (5.8) 67.2 (2.8) 78.6 (1.7) 90.7 (1.1) 74.1 (0.6)
England/N. Ireland (UK) 41.5 (3.8) 69.4 (2.9) 67.7 (2.7) 73.5 (1.8) 81.7 (0.9) 90.6 (0.8) 79.7 (0.2)
Average1
46.6 (0.8) 72.6 (0.8) 69.0 (0.6) 76.5 (0.5) 84.2 (0.3) 90.4 (0.2) 79.7 (0.1)
Average-222
46.9 (0.7) 72.4 (0.7) 69.3 (0.5) m m m m m m m m
Partners
Cyprus3
58.2 (1.5) 83.8 (4.9) 82.8 (1.4) m m m m m m m m
Russian Federation4
53.4 (3.8) 57.1 (5.2) 64.1 (2.1) 66.7 (3.6) 75.1 (2.6) 78.5 (4.5) 67.9 (1.8)

[Part 1/1]
Table A4.7 Labour force participation rate, by frequency of e-mail use in everyday life among adults aged 25-65
Low frequency of e-mail use High frequency of e-mail use Total
National entities
Australia 65.0 (1.2) 83.1 (0.5) 78.6 (0.3)
Austria 66.0 (1.3) 85.0 (0.7) 78.7 (0.6)
Canada 70.5 (1.0) 85.3 (0.4) 82.1 (0.4)
Czech Republic 61.4 (1.5) 83.5 (0.8) 76.8 (0.2)
Denmark 64.0 (1.7) 84.5 (0.5) 81.5 (0.4)
Estonia 64.9 (1.1) 90.7 (0.4) 83.2 (0.4)
Finland 58.5 (1.7) 85.4 (0.6) 79.6 (0.6)
France 63.4 (0.8) 81.3 (0.4) 75.7 (0.2)
Germany 73.2 (1.4) 87.6 (0.7) 83.4 (0.6)
Ireland 62.9 (1.7) 80.5 (0.8) 73.9 (0.7)
Italy 58.5 (1.1) 82.8 (0.8) 70.4 (0.5)
Japan 73.8 (0.9) 81.2 (0.7) 78.0 (0.3)
Korea 72.0 (0.8) 81.4 (0.8) 77.1 (0.5)
Netherlands 59.0 (2.3) 84.6 (0.5) 81.4 (0.5)
Norway 67.3 1.9 88.4 0.5 85.4 0.5
Poland 59.6 (1.1) 85.8 (0.7) 72.9 (0.6)
Slovak Republic 61.2 (1.2) 86.2 (0.8) 75.1 (0.6)
Spain 63.5 (1.0) 84.9 (0.7) 75.6 (0.5)
Sweden 72.9 1.8 87.6 0.6 85.0 0.5
United States 73.8 (1.5) 86.3 (0.8) 82.8 (0.7)
England (UK) 67.8 (1.3) 83.6 (0.4) 79.9 (0.2)
Average1
66.0 (0.3) 85.0 (0.1) 79.7 (0.1)
Average-222
65.4 (0.3) 84.7 (0.1) 78.9 (0.1)
Partners
Cyprus3
70.4 (1.0) 88.4 (0.9) 78.0 (0.7)
Russian Federation4
62.3 (2.3) 76.9 (1.3) 67.9 (1.8)
Note: High frequency stands for use of e-mail at least once a month.

[Part 1/2]
Table A4.8
Employment and unemployment rates, by proficiency in problem solving in technology-rich environments
No computer experience Failed ICT core Opted out Below Level 1
Employment
rate
Unemployment
rate
Employment
rate
Unemployment
rate
Employment
rate
Unemployment
rate
Employment
rate
Unemployment
rate
National entities
Australia 96.4 (1.8) 3.6 (1.8) 94.2 (2.5) 5.8 (2.5) 93.2 (1.2) 6.8 (1.2) 93.0 (1.7) 7.0 (1.7)
Austria 95.6 (1.8) 4.4 (1.8) 94.5 (2.3) 5.5 (2.3) 96.4 (1.0) 3.6 (1.0) 94.0 (1.7) 6.0 (1.7)
Canada 91.3 (2.3) 8.7 (2.3) 93.4 (1.5) 6.6 (1.5) 95.6 (1.0) 4.4 (1.0) 95.5 (0.7) 4.5 (0.7)
Czech Republic 90.9 (2.1) 9.1 (2.1) 94.2 (2.8) 5.8 (2.8) 90.9 (2.3) 9.1 (2.3) 95.4 (1.5) 4.6 (1.5)
Denmark 96.5 (2.1) 3.5 (2.1) 92.1 (1.7) 7.9 (1.7) 91.0 (2.2) 9.0 (2.2) 94.5 (1.3) 5.5 (1.3)
Estonia 82.8 (1.9) 17.2 (1.9) 92.7 (1.9) 7.3 (1.9) 91.2 (0.9) 8.8 (0.9) 94.1 (0.9) 5.9 (0.9)
Finland 92.4 (3.6) 7.6 (3.6) 90.7 (2.7) 9.3 (2.7) 96.4 (1.1) 3.6 (1.1) 95.8 (1.2) 4.2 (1.2)
France 92.6 (1.2) 7.4 (1.2) 93.0 (1.3) 7.0 (1.3) 91.8 (1.0) 8.2 (1.0) m m m m
Germany 93.8 (2.2) 6.2 (2.2) 93.4 (2.4) 6.6 (2.4) 90.1 (2.1) 9.9 (2.1) 95.3 (1.2) 4.7 (1.2)
Ireland 85.5 (2.6) 14.5 (2.6) 87.0 (3.6) 13.0 (3.6) 88.9 (1.6) 11.1 (1.6) 87.4 (1.8) 12.6 (1.8)
Italy 81.9 (2.1) 18.1 (2.1) 76.8 (5.8) 23.2 (5.8) 90.8 (1.6) 9.2 (1.6) m m m m
Japan 98.4 (0.9) 1.6 (0.9) 96.7 (1.2) 3.3 (1.2) 97.7 (0.8) 2.3 (0.8) 96.2 (1.5) 3.8 (1.5)
Korea 97.1 (0.6) 2.9 (0.6) 96.4 (0.9) 3.6 (0.9) 97.3 (1.0) 2.7 (1.0) 97.5 (0.9) 2.5 (0.9)
Netherlands 91.2 (4.4) 8.8 (4.4) 94.0 (2.7) 6.0 (2.7) 91.1 (3.2) 8.9 (3.2) 91.8 (2.0) 8.2 (2.0)
Norway c c c c 93.4 (1.9) 6.6 (1.9) 98.5 (1.2) 1.5 (1.2) 96.9 (1.2) 3.1 (1.2)
Poland 84.4 (1.6) 15.6 (1.6) 91.2 (2.1) 8.8 (2.1) 91.5 (1.0) 8.5 (1.0) 93.4 (1.5) 6.6 (1.5)
Slovak Republic 81.2 (1.6) 18.8 (1.6) 88.8 (3.8) 11.2 (3.8) 91.3 (1.6) 8.7 (1.6) 89.3 (2.3) 10.7 (2.3)
Spain 75.8 (1.9) 24.2 (1.9) 75.6 (2.9) 24.4 (2.9) 80.6 (2.2) 19.4 (2.2) m m m m
Sweden c c c c 82.4 (4.0) 17.6 (4.0) 88.0 (3.5) 12.0 (3.5) 93.3 (1.6) 6.7 (1.6)
United States 95.7 (1.3) 4.3 (1.3) 91.3 (1.7) 8.7 (1.7) 89.1 (2.4) 10.9 (2.4) 90.1 (1.6) 9.9 (1.6)
England (UK) 86.8 (4.7) 13.2 (4.7) 86.4 (2.6) 13.6 (2.6) 94.9 (2.0) 5.1 (2.0) 91.4 (1.4) 8.6 (1.4)
Average1
91.7 (0.6) 8.3 (0.6) 92.2 (0.6) 7.8 (0.6) 93.2 (0.4) 6.8 (0.4) 93.8 (0.3) 6.2 (0.3)
Average-222
90.4 (0.5) 9.6 (0.5) 90.7 (0.6) 9.3 (0.6) 92.5 (0.4) 7.5 (0.4) m m m m
Partners
Cyprus3
88.4 (1.5) 11.6 (1.5) 97.8 (2.1) 2.2 (2.1) 92.5 (1.2) 7.5 (1.2) m m m m
Russian Federation4
94.3 (2.3) 5.7 (2.3) 98.3 (1.7) 1.7 (1.7) 96.9 (0.9) 3.1 (0.9) 95.9 (1.8) 4.1 (1.8)

[Part 2/2]
Table A4.8
Employment and unemployment rates, by proficiency in problem solving in technology-rich environments
Level 1 Level 2/3 Total
Employment rate Unemployment rate Employment rate Unemployment rate Employment rate Unemployment rate
National entities
Australia 95.9 (0.7) 4.1 (0.7) 97.4 (0.5) 2.6 (0.5) 95.7 (0.2) 4.3 (0.2)
Austria 96.0 (0.8) 4.0 (0.8) 97.4 (0.6) 2.6 (0.6) 96.2 (0.4) 3.8 (0.4)
Canada 96.2 (0.5) 3.8 (0.5) 96.8 (0.4) 3.2 (0.4) 96.0 (0.2) 4.0 (0.2)
Czech Republic 93.9 (1.2) 6.1 (1.2) 96.0 (1.0) 4.0 (1.0) 94.2 (0.2) 5.8 (0.2)
Denmark 95.2 (0.7) 4.8 (0.7) 94.5 (0.7) 5.5 (0.7) 94.5 (0.4) 5.5 (0.4)
Estonia 93.9 (0.6) 6.1 (0.6) 96.9 (0.6) 3.1 (0.6) 93.4 (0.3) 6.6 (0.3)
Finland 95.9 (0.7) 4.1 (0.7) 95.6 (0.6) 4.4 (0.6) 95.5 (0.4) 4.5 (0.4)
France m m m m m m m m 92.9 (0.2) 7.1 (0.2)
Germany 95.0 (0.8) 5.0 (0.8) 96.8 (0.5) 3.2 (0.5) 95.2 (0.4) 4.8 (0.4)
Ireland 88.6 (1.2) 11.4 (1.2) 92.4 (1.1) 7.6 (1.1) 89.2 (0.5) 10.8 (0.5)
Italy m m m m m m m m 87.7 (0.7) 12.3 (0.7)
Japan 97.2 (0.9) 2.8 (0.9) 98.2 (0.5) 1.8 (0.5) 97.6 (0.2) 2.4 (0.2)
Korea 97.0 (0.6) 3.0 (0.6) 96.1 (0.6) 3.9 (0.6) 96.8 (0.3) 3.2 (0.3)
Netherlands 96.1 (0.7) 3.9 (0.7) 97.4 (0.5) 2.6 (0.5) 95.8 (0.4) 4.2 (0.4)
Norway 96.5 (0.6) 3.5 (0.6) 97.9 (0.4) 2.1 (0.4) 97.1 (0.3) 2.9 (0.3)
Poland 93.7 (1.2) 6.3 (1.2) 96.1 (0.8) 3.9 (0.8) 91.9 (0.5) 8.1 (0.5)
Slovak Republic 93.5 (1.0) 6.5 (1.0) 94.2 (1.0) 5.8 (1.0) 90.7 (0.5) 9.3 (0.5)
Spain m m m m m m m m 82.7 (0.6) 17.3 (0.6)
Sweden 95.0 (0.9) 5.0 (0.9) 97.7 (0.5) 2.3 (0.5) 94.9 (0.4) 5.1 (0.4)
United States 91.8 (1.0) 8.2 (1.0) 94.7 (0.7) 5.3 (0.7) 92.5 (0.4) 7.5 (0.4)
England (UK) 93.6 (0.8) 6.4 (0.8) 96.8 (0.5) 3.2 (0.5) 94.0 (0.1) 6.0 (0.1)
Average1
94.9 (0.2) 5.1 (0.2) 96.4 (0.2) 3.6 (0.2) 94.7 (0.1) 5.3 (0.1)
Average-222
m m m m m m m m 93.8 (0.1) 6.2 (0.1)
Partners
Cyprus3
m m m m m m m m 92.0 (0.6) 8.0 (0.6)
Russian Federation4
94.3 (1.7) 5.7 (1.7) 94.0 (1.7) 6.0 (1.7) 94.9 (1.0) 5.1 (1.0)

[Part 1/1]
Table A4.9
Employment and unemployment rates, by frequency of e-mail use in everyday life among adults
aged 25-65
Low frequency of e-mail use High frequency of e-mail use Total
Employment rate Unemployment rate Employment rate Unemployment rate Employment rate Unemployment rate
National entities
Australia 95.6 (0.8) 4.4 (0.8) 95.7 (0.2) 4.3 (0.2) 95.7 (0.2) 4.3 (0.2)
Austria 96.2 (0.9) 3.8 (0.9) 96.2 (0.4) 3.8 (0.4) 96.2 (0.4) 3.8 (0.4)
Canada 95.6 (0.6) 4.4 (0.6) 96.0 (0.2) 4.0 (0.2) 96.0 (0.2) 4.0 (0.2)
Czech Republic 92.5 (1.0) 7.5 (1.0) 94.8 (0.3) 5.2 (0.3) 94.2 (0.2) 5.8 (0.2)
Denmark 93.9 (1.3) 6.1 (1.3) 94.6 (0.4) 5.4 (0.4) 94.5 (0.4) 5.5 (0.4)
Estonia 89.8 (0.8) 10.2 (0.8) 94.5 (0.4) 5.5 (0.4) 93.4 (0.3) 6.6 (0.3)
Finland 96.3 (0.9) 3.7 (0.9) 95.4 (0.4) 4.6 (0.4) 95.5 (0.4) 4.5 (0.4)
France 92.2 (0.7) 7.8 (0.7) 93.1 (0.3) 6.9 (0.3) 92.9 (0.2) 7.1 (0.2)
Germany 93.6 (1.0) 6.4 (1.0) 95.8 (0.5) 4.2 (0.5) 95.2 (0.4) 4.8 (0.4)
Ireland 88.6 (1.2) 11.4 (1.2) 89.5 (0.6) 10.5 (0.6) 89.2 (0.5) 10.8 (0.5)
Italy 86.4 (1.3) 13.6 (1.3) 88.7 (0.9) 11.3 (0.9) 87.7 (0.7) 12.3 (0.7)
Japan 97.9 (0.5) 2.1 (0.5) 97.4 (0.4) 2.6 (0.4) 97.6 (0.2) 2.4 (0.2)
Korea 97.4 (0.3) 2.6 (0.3) 96.4 (0.4) 3.6 (0.4) 96.8 (0.3) 3.2 (0.3)
Netherlands 95.2 (1.5) 4.8 (1.5) 95.9 (0.4) 4.1 (0.4) 95.8 (0.4) 4.2 (0.4)
Norway 97.9 0.8 2.1 0.8 97.0 0.3 3.0 0.3 97.1 0.3 2.9 0.3
Poland 88.2 (0.9) 11.8 (0.9) 94.4 (0.5) 5.6 (0.5) 91.9 (0.5) 8.1 (0.5)
Slovak Republic 85.8 (1.0) 14.2 (1.0) 93.4 (0.5) 6.6 (0.5) 90.7 (0.5) 9.3 (0.5)
Spain 81.8 (1.1) 18.2 (1.1) 83.2 (0.8) 16.8 (0.8) 82.7 (0.6) 17.3 (0.6)
Sweden 94.8 1.5 5.2 1.5 94.9 0.4 5.1 0.4 94.9 0.4 5.1 0.4
United States 92.7 (1.1) 7.3 (1.1) 92.4 (0.5) 7.6 (0.5) 92.5 (0.4) 7.5 (0.4)
England (UK) 92.2 (1.0) 7.8 (1.0) 94.4 (0.3) 5.6 (0.3) 94.0 (0.1) 6.0 (0.1)
Average1
93.8 (0.2) 6.2 (0.2) 95.1 (0.1) 4.9 (0.1) 94.7 (0.1) 5.3 (0.1)
Average-222
92.8 (0.2) 7.2 (0.2) 94.2 (0.1) 5.8 (0.1) 93.8 (0.1) 6.2 (0.1)
Partners
Cyprus3
91.4 (0.8) 8.6 (0.8) 92.5 (0.9) 7.5 (0.9) 92.0 (0.6) 8.0 (0.6)
Russian Federation4
95.7 (1.0) 4.3 (1.0) 93.9 (1.3) 6.1 (1.3) 94.9 (1.0) 5.1 (1.0)
Note: High frequency stands for use of e-mail at least once a month.

[Part 1/1]
Table A4.10 Mean hourly wage, by proficiency in problem solving in technology-rich environments
No computer
experience Failed ICT core Opted out Below Level 1 Level 1 Level 2/3
OECD Mean
wage S.E.
Mean
wage S.E.
Mean
wage S.E.
Mean
wage S.E.
Mean
wage S.E.
Mean
wage S.E.
National entities
Australia 14.2 (0.5) 17.8 (1.1) 16.5 (0.5) 17.1 (0.6) 18.2 (0.3) 20.7 (0.3)
Austria 14.7 (0.4) 16.8 (0.7) 16.0 (0.4) 16.9 (0.6) 18.9 (0.3) 21.5 (0.4)
Canada 15.0 (0.6) 18.2 (0.6) 17.4 (0.5) 17.9 (0.4) 20.2 (0.3) 22.5 (0.3)
Czech Republic 6.5 (0.3) 7.2 (0.5) 7.3 (0.2) 7.9 (0.3) 9.0 (0.3) 10.4 (0.3)
Denmark 19.6 (0.8) 21.3 (0.7) 20.3 (0.6) 22.2 (0.4) 23.3 (0.3) 25.2 (0.2)
Estonia 5.7 (0.3) 8.2 (0.4) 8.0 (0.2) 8.7 (0.3) 9.9 (0.2) 11.6 (0.2)
Finland 15.0 (0.9) 16.7 (0.8) 16.8 (0.3) 17.6 (0.4) 18.9 (0.2) 20.6 (0.2)
France 12.2 (0.2) 13.9 (0.4) 14.8 (0.3) m m m m m m
Germany 13.4 (0.5) 16.6 (0.8) 15.6 (0.6) 16.5 (0.5) 18.4 (0.4) 21.5 (0.4)
Ireland 16.5 (1.1) 18.2 (1.2) 19.3 (0.6) 19.0 (0.8) 21.9 (0.5) 24.5 (0.6)
Italy 12.9 (0.4) 14.6 (1.4) 14.7 (0.5) m m m m m m
Japan 11.3 (0.5) 14.9 (0.8) 13.5 (0.5) 15.3 (0.9) 16.2 (0.6) 18.3 (0.4)
Korea 12.6 (0.6) 15.7 (0.9) 17.3 (1.6) 17.4 (1.1) 19.0 (0.7) 19.0 (0.6)
Netherlands 15.6 (1.2) 18.4 (0.9) 18.7 (1.0) 18.5 (0.5) 20.7 (0.3) 23.1 (0.3)
Norway c c 20.3 0.6 21.1 0.6 21.5 0.5 23.8 0.3 26.0 0.2
Poland 6.7 (0.3) 8.9 (0.5) 8.9 (0.2) 8.7 (0.4) 9.9 (0.3) 11.4 (0.3)
Slovak Republic 6.2 (0.2) 10.0 (1.0) 8.0 (0.3) 7.9 (0.7) 9.0 (0.3) 10.7 (0.3)
Spain 10.8 (0.3) 13.5 (0.7) 13.0 (0.5) m m m m m m
Sweden c c 17.7 1.0 16.5 0.6 16.9 0.3 18.1 0.2 19.8 0.2
United States 12.1 (0.6) 19.4 (2.2) 16.6 (0.9) 17.0 (0.8) 20.6 (0.6) 26.6 (0.8)
England (UK) 11.4 (0.7) 13.4 (0.7) 14.7 (0.8) 14.2 (0.4) 16.5 (0.3) 22.0 (0.4)
Northern Ireland (UK) 12.1 (0.6) 14.3 (1.3) c c 14.1 (0.6) 16.1 (0.5) 18.3 (0.5)
Average1
12.6 (0.2) 15.7 (0.2) 15.4 (0.2) 15.9 (0.1) 17.7 (0.1) 19.9 (0.1)
Average-222
12.5 (0.1) 15.5 (0.2) 15.2 (0.1) m m m m m m
Partners
Cyprus3
14.1 (0.6) 15.5 (1.5) 17.3 (0.5) m m m m m m
Russian Federation4
3.6 (0.2) 5.0 (0.6) 5.0 (0.2) 4.7 (0.3) 4.9 (0.2) 5.6 (0.1)

[Part 1/1]
Table A4.11 Mean hourly wage, by frequency of e-mail use at work
Less than monthly or never (A) At least monthly (B) Wage premium for (B)
OECD Mean wage S.E. Mean wage S.E. % diff. S.E.
National entities
Australia 14.5 (0.2) 21.6 (0.3) 48.8 (0.0)
Austria 14.8 (0.2) 22.0 (0.3) 48.5 (0.0)
Canada 14.8 (0.2) 23.9 (0.2) 61.2 (0.0)
Czech Republic 7.3 (0.1) 10.6 (0.2) 44.8 (0.0)
Denmark 18.8 (0.2) 26.0 (0.2) 38.6 (0.0)
Estonia 8.0 (0.1) 11.1 (0.1) 38.9 (0.0)
Finland 15.3 (0.2) 20.8 (0.1) 36.5 (0.0)
France 12.7 (0.1) 17.9 (0.1) 40.7 (0.0)
Germany 14.1 (0.2) 22.8 (0.3) 61.3 (0.0)
Ireland 16.3 (0.3) 25.8 (0.4) 58.0 (0.0)
Italy 13.8 (0.3) 19.4 (0.4) 40.6 (0.0)
Japan 12.4 (0.2) 19.9 (0.3) 60.9 (0.0)
Korea 14.0 (0.4) 21.0 (0.4) 49.8 (0.1)
Netherlands 14.5 (0.2) 24.4 (0.2) 67.7 (0.0)
Norway 18.9 0.2 26.2 0.1 38.7 (0.0)
Poland 7.6 (0.1) 11.5 (0.2) 51.4 (0.0)
Slovak Republic 7.1 (0.1) 10.9 (0.2) 54.8 (0.0)
Spain 11.8 (0.2) 18.6 (0.3) 57.6 (0.0)
Sweden 15.9 0.2 19.7 0.1 24.1 (0.0)
United States 14.1 (0.3) 26.1 (0.6) 84.6 (0.0)
England (UK) 12.1 (0.2) 21.5 (0.3) 78.2 (0.0)
Average1
13.6 (0.0) 20.6 (0.1) 51.8 (0.0)
Average-222
13.5 (0.0) 20.3 (0.1) 51.1 (0.0)
Partners
Cyprus3
15.1 (0.3) 19.8 (0.4) 30.6 (0.0)
Russian Federation4
4.4 (0.1) 6.1 (0.2) 39.9 (0.1)

[Part 1/1]
Table A4.12 Mean hourly wage, by frequency of complex problem solving
Less than monthly or never (A) At least monthly (B) Wage premium for (B)
OECD Mean wage S.E. Mean wage S.E. % diff. S.E.
National entities
Australia 15.4 (0.2) 20.7 (0.2) 34.8 (0.0)
Austria 15.9 (0.2) 21.6 (0.2) 35.9 (0.0)
Canada 16.5 (0.2) 23.2 (0.2) 40.7 (0.0)
Czech Republic 7.8 (0.1) 9.9 (0.2) 27.7 (0.0)
Denmark 20.3 (0.2) 26.3 (0.2) 29.5 (0.0)
Estonia 8.2 (0.1) 11.0 (0.1) 34.0 (0.0)
Finland 16.7 (0.2) 20.9 (0.2) 25.0 (0.0)
France 13.7 (0.1) 17.3 (0.1) 26.7 (0.0)
Germany 15.2 (0.2) 22.1 (0.3) 45.6 (0.0)
Ireland 18.1 (0.3) 24.5 (0.4) 35.4 (0.0)
Italy 13.7 (0.3) 17.8 (0.3) 30.5 (0.0)
Japan 13.8 (0.2) 19.0 (0.4) 38.1 (0.0)
Korea 15.3 (0.5) 20.0 (0.4) 30.7 (0.0)
Netherlands 17.9 (0.2) 24.6 (0.2) 37.6 (0.0)
Norway 20.9 0.2 26.6 0.2 27.5 (0.0)
Poland 8.0 (0.2) 10.7 (0.2) 33.1 (0.0)
Slovak Republic 7.1 (0.1) 10.2 (0.2) 43.2 (0.0)
Spain 13.1 (0.2) 16.8 (0.3) 27.6 (0.0)
Sweden 16.6 0.2 20.1 0.1 20.8 (0.0)
United States 16.8 (0.5) 24.2 (0.5) 44.1 (0.1)
England (UK) 13.6 (0.3) 20.8 (0.3) 53.6 (0.0)
Average1
15.0 (0.1) 20.0 (0.1) 34.5 (0.0)
Average-222
14.8 (0.1) 19.7 (0.1) 33.7 (0.0)
Partners
Cyprus3
15.7 (0.3) 18.5 (0.3) 18.1 (0.0)
Russian Federation4
4.4 (0.1) 5.3 (0.2) 20.7 (0.1)

[Part 1/1]
Table A4.13
Mean hourly wage and wage premium, by adequacy of computer skills affecting the chances of getting
a job, promotion or pay raise
Has the computer
skills to do the job
well (A)
Lack the computer
skills to do the job
well (B)
Wage premium
for (A)
A lack of computer
skills has not
affected
the chances of
getting a job/
promotion/pay
raise or does not
use computer at
work (C)
A lack of computer
skills has affected
the chances of
getting a job/
promotion/pay
raise (D)
Wage premium
for (D)
Does not use
computer at work
OECD Mean
wage S.E.
Mean
wage S.E. % diff. S.E.
Mean
wage S.E.
Mean
wage S.E. % diff. S.E.
Mean
wage S.E.
National entities
Australia 20.7 (0.7) 20.2 (0.2) 2.5 (0.0) 20.4 (0.2) 18.7 (0.5) -8.0 (0.0) 14.7 (0.3)
Austria 21.7 (1.2) 20.7 (0.2) 4.8 (0.1) 20.9 (0.2) 17.9 (0.7) -14.1 (0.0) 14.4 (0.2)
Canada 23.2 (0.8) 22.1 (0.2) 4.8 (0.0) 22.4 (0.2) 20.0 (0.5) -10.6 (0.0) 14.6 (0.2)
Czech Republic 8.8 (0.4) 10.1 (0.2) -12.7 (0.0) 10.0 (0.1) 10.0 (0.7) -0.4 (0.1) 7.0 (0.1)
Denmark 24.6 (0.3) 25.0 (0.2) -1.5 (0.0) 25.0 (0.1) 24.4 (0.7) -2.1 (0.0) 18.8 (0.3)
Estonia 10.0 (0.3) 10.6 (0.1) -5.5 (0.0) 10.7 (0.1) 9.4 (0.4) -11.8 (0.0) 8.0 (0.2)
Finland 19.4 (0.4) 20.2 (0.1) -3.9 (0.0) 20.2 (0.1) 19.7 (0.6) -2.2 (0.0) 15.1 (0.2)
France 16.8 (0.3) 17.0 (0.1) -1.1 (0.0) 17.0 (0.1) 16.7 (0.4) -1.8 (0.0) 12.4 (0.1)
Germany 21.0 (0.7) 21.4 (0.3) -1.8 (0.0) 21.5 (0.3) 17.4 (0.8) -19.1 (0.0) 13.3 (0.2)
Ireland 25.7 (1.1) 24.1 (0.3) 6.5 (0.0) 24.5 (0.3) 20.2 (0.8) -17.5 (0.0) 15.5 (0.3)
Italy 18.9 (1.1) 18.6 (0.3) 1.7 (0.1) 18.7 (0.3) 16.7 (1.2) -11.0 (0.1) 13.3 (0.3)
Japan 16.6 (0.3) 18.5 (0.4) -10.3 (0.0) 17.8 (0.3) 17.5 (0.4) -1.5 (0.0) 11.5 (0.3)
Korea 18.4 (0.7) 19.9 (0.4) -7.4 (0.0) 19.7 (0.3) 17.0 (1.9) -13.5 (0.1) 13.8 (0.5)
Netherlands 24.4 (1.0) 23.1 (0.2) 5.3 (0.0) 23.3 (0.2) 21.8 (0.8) -6.3 (0.0) 14.3 (0.2)
Norway 26.5 0.3 25.0 0.1 6.3 0.0 25.4 0.1 23.1 0.7 -9.0 0.0 19.2 (0.4)
Poland 11.5 (0.7) 10.9 (0.2) 5.1 (0.1) 11.0 (0.2) 11.1 (0.6) 1.0 (0.1) 7.3 (0.1)
Slovak Republic 9.1 (0.6) 10.4 (0.2) -12.8 (0.1) 10.4 (0.2) 10.0 (0.6) -3.5 (0.1) 6.8 (0.1)
Spain 16.8 (0.8) 17.6 (0.3) -4.9 (0.0) 17.7 (0.3) 16.6 (1.2) -6.2 (0.1) 11.2 (0.2)
Sweden 19.9 0.4 19.1 0.1 4.2 0.0 19.3 0.1 17.1 0.5 -11.3 0.0 15.7 (0.3)
United States 22.9 (1.1) 24.1 (0.5) -4.8 (0.0) 24.3 (0.6) 21.5 (1.3) -11.4 (0.1) 13.8 (0.3)
Flanders (Belgium) 25.0 (0.7) 24.0 (0.2) 4.4 (0.0) 24.2 (0.2) 22.0 (0.7) -9.2 (0.0) 17.1 (0.2)
England (UK) 19.4 (1.0) 20.1 (0.2) -3.3 (0.0) 20.3 (0.2) 16.6 (0.8) -18.3 (0.0) 12.0 (0.2)
Northern Ireland (UK) 20.3 (1.1) 17.6 (0.2) 15.1 (0.1) 17.9 (0.2) 15.2 (1.0) -15.0 (0.1) 12.2 (0.5)
England/N. Ireland (UK) 19.4 (0.9) 20.0 (0.2) -2.7 (0.0) 20.2 (0.2) 16.5 (0.8) -18.1 (0.0) 12.0 (0.2)
Average1
19.4 (0.2) 19.4 (0.1) (0.0) 19.5 (0.1) 17.7 (0.2) -8.9 (0.0) 13.3 (0.1)
Average-222
19.2 (0.2) 19.2 (0.1) (0.0) 19.3 (0.1) 17.5 (0.2) -8.5 (0.0) 13.2 (0.1)
Partners
Cyprus3
20.5 (1.2) 19.2 (0.3) 6.9 (0.1) 19.7 (0.3) 15.7 (0.9) -20.0 (0.0) 14.0 (0.4)
Russian Federation4
4.7 (0.5) 5.7 (0.1) -17.0 (0.1) 5.6 (0.1) 6.3 (0.9) 13.6 (0.2) 4.3 (0.1)

[Part 1/2]
Table A4.14
Differences in the rate of labour force participation between various groups after accounting for various
characteristics
Version 1 (socio-demographic controls) Version 2 (Version 1 + literacy and numeracy)
No computer
experience
Failed ICT
core Opted out Level 1 Level 2/3
No computer
experience
Failed ICT
OECD % point % point % point % point % point % point % point % point % point % point
National entities
Australia -24.7 *** -12.8 * -2.3 2.1 7.8 * -23.1 *** -12.2 * -2.7 0.4 5.3
Austria -13.4 ** -6.1 -7.4 2.6 5.3 -14.6 *** -7.1 -10.2 * -0.9 -1.1
Canada -11.7 *** -5.9 ** -3.2 2.7 7.5 *** -10.6 *** -6.3 ** -4.5 -0.3 2.7
Czech Republic -15.3 *** 7.5 6.0 -0.3 5.3 -15.7 *** 7.2 5.3 -1.3 3.9
Denmark -27.1 *** -6.8 -10.1 ** 7.2 ** 14.6 *** -26.9 *** -6.6 -11.6 ** 2.1 7.1
Estonia -25.7 *** -5.8 * -5.2 *** 2.0 4.9 ** -26.2 *** -6.4 * -6.9 *** 0.0 2.0
Finland -26.2 *** -12.9 *** -7.6 ** 11.2 *** 17.7 *** -27.4 *** -15.3 *** -14.6 *** 6.5 ** 10.4 **
France m m m m m m m m m m
Germany -5.7 1.9 -4.9 3.8 7.2 ** -5.3 2.7 -7.8 * -1.3 -1.6
Ireland -11.5 *** 4.5 -4.1 7.1 * 14.5 *** -11.4 ** 4.7 -5.4 5.3 11.8 **
Italy m m m m m m m m m m
Japan -8.6 * -3.1 -2.3 -0.4 3.8 -7.9 * -2.5 -1.8 -0.3 3.8
Korea -7.2 *** -5.1 -1.5 -1.4 4.3 -7.0 ** -4.9 -1.2 -1.5 4.1
Netherlands -10.1 * -1.4 -9.0 * 7.9 ** 13.7 *** -10.1 -0.9 -7.7 9.4 ** 15.9 ***
Norway -30.7 *** 0.3 -7.4 * 8.4 ** 13.6 *** -30.2 *** 2.1 -8.4 * 5.0 8.0 *
Poland -11.5 *** -3.6 -1.9 3.8 9.3 *** -10.5 *** -2.8 -2.5 2.5 7.5 *
Slovak Republic -9.3 ** -7.6 0.9 2.8 5.8 * -9.0 ** -8.7 -0.7 -0.3 0.5
Spain m m m m m m m m m m
Sweden -23.3 ** -9.0 -8.7 * 5.3 10.5 *** -19.7 ** -4.0 -13.5 ** -3.2 -4.9
United States -18.9 *** -11.7 ** -9.2 *** 3.1 4.4 -16.9 *** -10.4 ** -10.0 *** 0.6 -0.1
Flanders (Belgium) -21.0 *** -7.6 -3.5 1.0 5.2 -21.8 *** -9.8 -6.9 -2.8 -1.3
England (UK) -32.4 *** -5.1 0.4 5.1 11.1 *** -31.0 *** -4.4 0.6 3.6 8.8 *
Northern Ireland (UK) -9.4 1.4 -16.6 3.3 13.0 ** -10.1 0.2 -19.3 * -1.4 5.2
England/N. Ireland (UK) -31.4 *** -4.9 0.3 5.1 11.2 *** -30.1 *** -4.3 0.4 3.5 8.8 *
Average1
-16.2 *** -3.7 *** -3.6 *** 4.2 *** 9.3 *** -15.7 *** -3.3 *** -5.0 *** 1.4 5.1 ***
Average-222
m m m m m m m m m m
Partners
Cyprus3
m m m m m m m m m m
Russian Federation4
3.9 -11.7 2.7 6.6 9.0 -0.1 -15.0 * -3.2 1.9 -0.6
Notes: The reference category for problem solving in rich-environment is Below Level 1 and low users for use of e-mail. Version 1 adjusts for socio-demographic characteristics
(age, gender, foreign-born status, years of education and marital status).Version 2 adds literacy and numeracy proficiency to the regression ofVersion 1.Version 3 adds frequency
of ICT use (e-mail) in everyday life as an adjustment to Version 2. Version 4 adds use of reading/writing/numeracy skills in everyday life as an additional adjustment to Version 3.
Regression coefficients of the versions are available in Table B4.12 in Annex B.

[Part 2/2]
Table A4.14
Differences in the rate of labour force participation between various groups after accounting for various
characteristics
Versions 3 (Version 2 + e-mail use in everyday life) Version 4 (Version 3 + reading/writing/numeracy use in everyday life)
No
computer
experience
Failed ICT
Frequent
use of
e-mail
No
computer
experience
Failed ICT
Frequent
use of
e-mail
OECD % point % point % point % point % point % point % point % point % point % point % point % point
National entities
Australia -20.8 *** -11.8 * -1.8 0.3 5.2 3.2 -28.2 *** -10.7 -3.1 0.7 4.9 9.7 ***
Austria -15.6 *** -7.4 -10.7 * -0.8 -0.9 -1.4 -25.0 *** -9.2 -12.0 ** -1.6 -1.5 2.6
Canada -11.0 *** -6.3 ** -4.7 -0.3 2.7 -0.6 -17.0 *** -5.6 * -5.9 * -0.4 2.8 3.8 *
Czech Republic -14.6 ** 7.4 5.6 -1.4 3.8 1.5 -13.0 * 9.2 4.9 -1.1 4.0 8.1
Denmark -27.7 *** -6.7 -11.9 ** 2.2 7.2 -1.2 -25.5 *** -5.4 -11.1 ** 3.1 8.9 3.8
Estonia -20.6 *** -5.4 -5.0 * -0.1 2.0 7.4 *** -17.9 *** -8.0 * -4.9 0.3 1.9 7.9 ***
Finland -22.0 *** -13.6 *** -11.6 *** 6.1 * 10.0 ** 7.4 ** -21.4 *** -17.8 *** -9.7 ** 5.6 9.0 * 12.9 ***
France m m m m m m m m m m m m
Germany -6.8 2.4 -8.8 * -1.0 -1.1 -2.7 -7.2 2.5 -9.0 * 0.0 1.5 -0.7
Ireland -9.9 ** 4.7 -4.8 5.0 11.5 ** 2.7 -9.8 * 5.7 -6.8 4.5 11.7 ** 6.3 *
Italy m m m m m m m m m m m m
Japan -9.2 ** -2.8 -2.2 -0.1 4.1 -2.4 -12.5 ** -3.8 -4.4 -1.1 4.0 -3.4
Korea -6.3 ** -4.6 -0.9 -1.6 3.9 1.8 -5.7 -2.4 -3.3 -3.3 3.6 4.5
Netherlands -2.0 0.8 -4.6 9.0 ** 15.8 *** 9.9 ** 1.5 -1.2 -7.4 8.4 16.8 *** 15.0 **
Norway -28.2 *** 2.2 -7.5 * 4.8 7.9 * 2.4 -32.0 *** 6.0 -4.3 7.5 * 11.3 ** 5.2
Poland -6.5 ** -2.5 -0.7 1.9 6.9 * 9.1 *** -8.8 * -2.7 -0.6 1.6 6.8 12.4 ***
Slovak Republic -4.0 -7.3 1.6 -0.7 -0.3 9.3 ** -3.6 -9.2 2.2 -0.8 0.0 9.8 **
Spain m m m m m m m m m m m m
Sweden -25.3 ** -4.6 -16.5 *** -2.4 -3.8 -6.8 * -7.2 -2.4 -15.4 ** -2.8 -4.6 -4.3
United States -18.8 *** -11.2 ** -11.3 *** 0.8 0.1 -3.2 -30.7 *** -13.9 ** -12.3 *** 0.2 -2.1 1.4
Flanders (Belgium) -28.8 *** -11.3 * -9.8 * -2.0 -0.3 -9.4 ** -25.7 *** -13.3 -13.3 * -1.6 1.0 -5.4
England (UK) -29.1 *** -4.1 1.4 3.4 8.5 * 2.6 -34.5 *** -2.4 4.4 5.0 11.3 ** 7.4 **
Northern Ireland (UK) -7.7 1.1 -18.1 -2.0 4.2 4.3 -9.2 -0.2 -13.3 -1.8 3.7 8.5 **
England/N. Ireland (UK) -28.0 *** -3.9 1.2 3.2 8.5 * 2.8 -33.2 *** -2.4 4.3 4.9 11.2 ** 7.6 **
Average1
-14.4 *** -3.2 *** -4.4 *** 1.4 5.0 *** 2.1 *** -14.6 *** -2.6 * -4.7 *** 1.5 5.6 *** 5.8 ***
Average-222
m m m m m m m m m m m m
Partners
Cyprus3
Russian Federation4
-0.1 -15.0 * -3.2 1.9 -0.6 0.1 8.3 -13.9 -0.9 6.5 4.3 4.7

[Part 1/2]
Table A4.15
Differences in the rate of unemployment between various groups after accounting for various
characteristics
Version 1 (socio-demographic controls) Version 2 (Model 1 + literacy and numeracy)
No computer
experience
Failed ICT
No computer
experience
Failed ICT
OECD % point % point % point % point % point % point % point % point % point % point
National entities
Australia -4.3 -2.2 -0.9 -2.7 -4.4 ** -4.2 -2.4 -1.3 -3.1 -4.8 **
Austria -2.3 -1.7 -2.1 -0.2 -2.2 -2.0 -1.5 -1.4 0.4 -1.3
Canada 4.1 * 2.8 * -0.2 0.0 -0.5 2.9 2.4 -0.5 0.8 1.2
Czech Republic 3.3 1.8 4.7 2.8 1.2 3.2 1.5 5.9 5.3 5.0
Denmark -3.3 0.2 3.4 0.6 2.9 -3.4 -0.2 3.3 3.2 10.2 **
Estonia 7.6 *** 0.7 3.3 ** 1.2 -1.8 7.8 *** 0.9 4.3 *** 2.8 * 0.0
Finland 1.6 2.8 -0.3 0.1 0.1 1.5 0.7 -1.2 1.0 2.6
Germany 0.9 1.4 5.0 ** 0.9 -1.6 -0.3 -0.4 4.8 * 2.8 0.9
Ireland 0.7 -3.1 -1.4 1.0 -3.2 0.6 -3.0 -0.3 3.7 1.0
Japan -2.4 -0.5 -1.6 -1.0 -2.1 -3.0 ** -2.6 -2.8 * -2.1 -3.1 **
Korea 0.1 0.7 0.1 0.1 0.0 -0.4 -0.1 -0.8 0.1 0.0
Netherlands -5.8 -4.1 -0.2 -3.5 -4.8 ** -6.3 -4.3 -1.1 -3.7 -5.1
Norway -3.1 1.9 -1.5 0.9 -0.7 -3.1 0.8 -1.4 2.6 1.7
Poland 5.0 * 1.4 2.0 0.8 -1.6 4.6 * 1.2 2.1 1.1 -1.2
Slovak Republic 5.7 * 0.4 -1.8 -4.0 -3.4 4.9 1.1 -2.0 -3.4 -1.9
Sweden 16.2 5.5 6.6 -0.4 -3.9 ** 16.5 5.1 7.6 * 0.6 -3.0
United States -6.8 *** -1.5 -1.4 -1.4 -3.7 -7.3 *** -2.7 -1.2 1.4 1.0
Flanders (Belgium) -1.8 -1.4 -0.5 -0.7 -1.0 -1.8 -1.2 0.1 -0.4 -0.5
England (UK) -0.3 3.7 -3.7 -2.1 -4.9 ** -1.0 3.0 -3.6 -0.2 -2.3
Northern Ireland (UK) 2.3 -3.5 -4.9 -3.1 -4.2 * 2.3 -3.6 -5.0 -2.3 -2.8
England/N. Ireland (UK) -0.1 3.5 -3.7 -2.2 -4.8 ** -0.8 2.8 -3.6 -0.3 -2.3
Average1
1.0 0.8 0.7 -0.2 -1.6 *** 0.6 0.0 0.7 1.0 0.5
Average-222
m m m m m m m m m m
Partners
Cyprus3
m m m m m m m m m m
Russian Federation4
2.8 -1.7 0.5 3.3 6.2 0.1 -2.3 -1.8 0.3 -0.2

[Part 2/2]
Table A4.15
Differences in the rate of unemployment between various groups after accounting for various
characteristics
Version 3 (Version 2 + e-mail use in everyday life) Version 4 (Version 3 + reading/writing/numeracy use in everyday life)
No
computer
experience
Failed ICT
Frequent
use of
e-mail
No
computer
experience
Failed ICT
Frequent
use of
e-mail
OECD % point % point % point % point % point % point % point % point % point % point % point % point
National entities
Australia -3.7 -2.3 -1.0 -3.1 -4.8 ** 1.2 -0.7 -1.6 0.7 -3.4 -4.6 * 0.1
Austria -2.0 -1.5 -1.4 0.4 -1.3 0.0 -1.0 -1.7 -2.0 -0.3 -2.3 -1.3
Canada 5.3 * 2.7 0.1 0.7 1.1 2.5 ** 11.4 ** 5.0 ** 1.3 1.0 1.7 0.7
Czech Republic 3.2 1.5 5.9 5.3 5.0 0.1 3.9 1.9 5.3 5.2 4.5 -1.9
Denmark -3.3 -0.2 3.3 3.2 10.2 ** 0.2 -5.5 0.2 1.8 3.7 11.1 ** -2.3
Estonia 10.0 *** 1.2 5.0 *** 2.8 * 0.1 2.3 11.0 *** 2.2 5.9 *** 3.6 ** 1.1 -0.6
Finland 4.2 0.9 -0.7 0.8 2.2 2.8 ** 9.4 * 2.2 -0.3 1.4 2.5 -0.3
Germany -0.3 -0.4 4.8 * 2.8 0.9 0.1 1.8 0.3 5.4 * 1.9 0.0 0.0
Ireland 2.0 -3.1 0.3 3.2 0.5 2.5 4.8 -3.9 0.9 3.6 0.2 -2.4
Japan -2.9 ** -2.6 -2.8 * -2.2 -3.1 ** 0.3 -3.2 ** -3.0 ** -3.0 ** -2.4 -3.3 ** -0.2
Korea -0.2 -0.2 -0.7 0.1 0.0 0.7 0.4 -0.1 -1.1 -0.2 -0.2 0.0
Netherlands -4.0 -4.1 0.3 -3.9 -5.1 7.8 ** -8.2 -4.6 0.5 -3.2 -4.6 2.5
Norway -3.1 0.7 -1.4 2.5 1.6 0.3 -3.1 0.8 -0.9 3.1 1.3 0.2
Poland 4.2 1.2 2.0 1.2 -1.1 -0.8 7.6 ** 1.7 2.4 0.8 -1.3 -2.7
Slovak Republic 3.2 0.9 -2.5 -3.3 -1.6 -2.4 7.1 * 2.5 -2.7 -2.8 -1.1 -3.2
Sweden 24.2 5.0 9.5 * 0.0 -3.4 3.9 -6.7 6.6 6.9 -1.1 -4.0 * 4.2
United States -6.6 ** -2.1 0.3 1.1 0.7 3.6 * -3.9 -0.5 4.3 3.3 5.1 -1.0
Flanders (Belgium) -1.5 -1.1 0.4 -0.4 -0.6 0.8 -1.8 -1.7 3.3 -0.6 -0.5 0.3
England (UK) -0.4 3.1 -3.4 -0.3 -2.4 1.0 11.0 7.3 * -1.1 0.3 -2.1 -1.2
Northern Ireland (UK) 1.7 -3.6 -5.1 -2.2 -2.6 -0.9 1.5 -3.6 -5.3 -2.5 -3.0 -3.5 **
England/N. Ireland (UK) -0.2 2.9 -3.4 -0.4 -2.4 0.9 10.4 6.9 * -1.2 0.2 -2.2 -1.3
Average1
1.9 0.1 1.1 0.9 0.4 1.9 *** 2.2 0.8 1.7 1.0 0.5 -0.1
Average-222
Partners
Cyprus3
Russian Federation4
0.9 -2.4 -1.5 0.1 -0.5 2.7 -0.5 -2.7 -3.9 1.9 -0.6 1.8

[Part 1/4]
Table A4.16
Percentage differences in wages between various groups, before and after accounting for various
characteristics
No computer
experience
Failed ICT
No computer
experience
Failed ICT
OECD ß ß ß ß ß ß ß ß ß ß
National entities
Australia -0.07 0.03 -0.03 0.04 0.12 *** -0.05 0.01 -0.06 -0.01 0.02
Austria -0.09 ** 0.04 -0.04 0.09 *** 0.20 *** -0.11 *** 0.02 -0.09 ** 0.04 0.10 ***
Canada -0.10 ** 0.00 -0.03 0.09 *** 0.18 *** -0.09 ** -0.03 -0.08 *** 0.02 0.04
Czech Republic -0.14 ** -0.09 -0.06 0.08 ** 0.17 *** -0.16 *** -0.13 * -0.11 ** 0.05 0.11 ***
Denmark -0.10 ** 0.00 -0.04 0.04 * 0.11 *** -0.11 ** 0.00 -0.05 0.02 0.06 **
Estonia -0.30 *** -0.07 -0.06 ** 0.08 *** 0.20 *** -0.30 *** -0.08 -0.09 *** 0.04 0.11 **
Finland -0.09 -0.01 -0.03 0.07 *** 0.13 *** -0.10 * -0.01 -0.05 0.04 * 0.08 **
Germany -0.16 *** -0.01 -0.08 * 0.08 ** 0.19 *** -0.19 *** -0.03 -0.13 *** 0.03 0.09 **
Ireland -0.11 0.06 0.00 0.12 *** 0.20 *** -0.11 0.04 -0.03 0.06 0.09 *
Japan -0.19 *** -0.03 -0.05 0.06 0.15 *** -0.20 *** -0.09 * -0.10 * 0.00 0.05
Korea -0.10 -0.09 -0.02 0.06 0.09 -0.12 * -0.13 ** -0.07 0.02 0.02
Netherlands -0.16 ** 0.03 -0.02 0.09 *** 0.18 *** -0.16 ** 0.00 -0.06 0.04 0.08 *
Norway -0.11 -0.02 -0.01 0.09 *** 0.17 *** -0.10 -0.01 -0.02 0.06 ** 0.11 ***
Poland -0.09 * 0.07 0.04 0.09 ** 0.18 *** -0.11 ** 0.04 -0.01 0.05 0.11 **
Slovak Republic -0.10 ** 0.19 ** 0.05 0.13 ** 0.23 *** -0.12 ** 0.16 * 0.01 0.08 0.15 **
Sweden -0.03 0.06 -0.04 0.05 *** 0.14 *** -0.02 0.07 -0.05 0.02 0.06 **
United States -0.10 * 0.04 -0.05 0.13 *** 0.25 *** -0.08 0.03 -0.09 * 0.06 0.12 **
Flanders (Belgium) -0.10 ** -0.05 -0.06 0.09 *** 0.14 *** -0.10 ** -0.06 -0.08 * 0.05 ** 0.07 **
England (UK) -0.15 ** -0.02 0.04 0.13 *** 0.30 *** -0.16 ** -0.07 -0.04 0.03 0.10 **
Northern Ireland (UK) -0.11 * -0.04 0.00 0.11 *** 0.21 *** -0.12 * -0.07 -0.05 0.06 0.09 *
England/N. Ireland (UK) -0.15 ** -0.02 0.04 0.13 *** 0.30 *** -0.15 ** -0.07 -0.04 0.03 0.10 **
Average1
-0.12 *** 0.01 -0.03 *** 0.08 *** 0.18 *** -0.12 *** -0.01 -0.06 *** 0.04 *** 0.08 ***
Average-222
m m m m m m m m m m
Partners
Cyprus3
m m m m m m m m m m
Russian Federation4
-0.05 0.26 *** 0.08 0.13 0.26 *** -0.07 0.23 ** 0.05 0.11 0.22 *
Notes: The reference category for problem solving in rich-environment is Below Level 1. Version 1 adjusts for socio-demographic characteristics (age, gender, foreign-born
status, years of education, marital status and years of experience). Version 2 adds literacy and numeracy proficiency to the regression of Version 1. Version 3 adds the frequency
of ICT use (e-mail) at work, the two adequacy measures of computer skills for work and the frequency of complex problem solving at work as an adjustment to Version 2.
Version 4 adds use of reading/writing/numeracy skills at work as an additional adjustment to Version 3. Version 5 adds occupation as an additional adjustment to Version 4.

[Part 2/4]
Table A4.16
characteristics
Version 3 (Version 2 + e-mail use, adequacy of ICT skills and frequency of complex problem solving at work)
No computer
experience
Failed ICT
Frequent use
of e-mail
Computer
workers
without
computer skills
to do the job
well
Computer
workers
whose skills
have affected
employment
Regular users
of complex
problem
solving
OECD ß ß ß ß ß ß ß ß ß
National entities
Australia 0.01 0.01 -0.04 -0.03 -0.02 0.16 *** -0.02 -0.05 ** 0.09 ***
Austria -0.01 0.06 -0.04 0.01 0.06 * 0.14 *** 0.00 -0.09 ** 0.10 ***
Canada 0.00 -0.01 -0.03 -0.01 -0.01 0.21 *** 0.05 * -0.08 *** 0.11 ***
Czech Republic -0.07 -0.10 -0.09 ** 0.01 0.03 0.10 *** -0.06 0.00 0.04 *
Denmark -0.06 0.00 -0.03 0.00 0.03 0.10 *** -0.04 *** -0.02 0.05 ***
Estonia -0.24 *** -0.06 -0.07 ** 0.01 0.05 0.17 *** 0.00 -0.08 ** 0.10 ***
Finland -0.04 0.00 -0.01 0.03 0.06 ** 0.08 *** -0.03 -0.01 0.07 ***
France m m m m m m m m m
Germany -0.06 0.05 -0.06 -0.01 0.03 0.13 *** -0.03 -0.09 * 0.09 ***
Ireland 0.01 0.08 0.02 0.03 0.05 0.15 *** 0.06 * -0.10 *** 0.06 ***
Italy m m m m m m m m m
Japan -0.10 ** -0.09 * -0.07 -0.03 -0.02 0.19 *** -0.03 -0.03 0.07 ***
Korea -0.05 -0.09 -0.02 0.01 -0.03 0.24 *** 0.00 -0.15 * 0.08 ***
Netherlands -0.05 0.02 -0.02 0.01 0.04 0.20 *** 0.02 -0.05 0.09 ***
Norway -0.08 -0.01 -0.01 0.04 0.09 *** 0.08 *** 0.01 -0.06 ** 0.06 ***
Poland -0.06 0.04 0.01 0.04 0.09 * 0.12 *** 0.06 0.01 0.08 ***
Slovak Republic -0.03 0.15 * 0.05 0.07 0.12 ** 0.14 *** -0.07 0.06 0.09 ***
Spain m m m m m m m m m
Sweden 0.03 0.07 -0.02 0.01 0.04 0.06 *** 0.02 -0.07 ** 0.08 ***
United States -0.02 0.06 -0.04 0.02 0.06 0.26 *** -0.03 -0.03 0.11 ***
Flanders (Belgium) -0.03 -0.03 -0.05 0.03 0.04 0.09 *** 0.00 -0.07 *** 0.03 ***
England (UK) -0.01 -0.01 0.03 -0.01 0.04 0.24 *** -0.03 -0.16 *** 0.13 ***
Northern Ireland (UK) -0.05 -0.04 -0.01 0.04 0.05 0.16 *** 0.07 -0.08 * 0.11 ***
England/N. Ireland (UK) -0.01 -0.01 0.03 -0.01 0.04 0.23 *** -0.02 -0.15 *** 0.13 ***
Average1
-0.05 0.01 *** -0.03 0.01 *** 0.04 0.15 -0.01 *** -0.06 0.08
Average-222
m m m m m m m m m
Partners
Cyprus3
m m m m m m m m m
Russian Federation4
-0.05 0.21 ** 0.05 0.08 0.14 0.28 ** -0.15 0.16 0.10 *

[Part 3/4]
Table A4.16
characteristics
Version 4 (Version 3 + reading/writing/numeracy use at work)
No computer
experience
Failed ICT
Frequent use
of e-mail
Computer
workers
without
computer skills
to do the job
well
Computer
workers
whose skills
have affected
employment
Regular users
of complex
problem
solving
National entities
Australia 0,06 -0,07 -0,04 -0,04 -0,03 0,11 *** -0,02 -0,05 ** 0,07 ***
Austria 0,03 0,03 -0,12 ** 0,00 0,04 0,08 *** -0,02 -0,12 ** 0,09 ***
Canada -0,02 0,00 -0,06 -0,01 -0,02 0,16 *** 0,05 ** -0,06 *** 0,09 ***
Czech Republic 0,09 -0,13 -0,05 0,00 0,03 0,08 *** -0,07 -0,04 0,03
Denmark -0,04 0,02 -0,08 ** 0,01 0,02 0,08 *** -0,04 *** 0,00 0,06 ***
Estonia -0,25 *** -0,13 * -0,06 -0,02 0,02 0,09 *** -0,03 -0,07 ** 0,09 ***
Finland 0,09 0,01 -0,02 0,03 0,05 * 0,03 -0,02 0,00 0,05 ***
Germany -0,16 ** 0,01 -0,12 * -0,03 -0,02 0,11 *** -0,01 -0,14 ** 0,06 ***
Ireland 0,19 ** -0,03 -0,02 0,02 0,01 0,05 0,05 -0,09 *** 0,02
Japan -0,16 ** -0,08 -0,09 -0,03 -0,02 0,12 *** -0,04 * -0,03 0,01
Korea -0,12 -0,05 0,00 0,00 -0,07 0,17 *** -0,01 -0,12 0,07 **
Netherlands -0,01 0,08 -0,01 0,01 0,04 0,16 *** 0,00 -0,07 ** 0,09 ***
Norway -0,21 -0,05 -0,06 0,01 0,05 0,05 ** 0,01 -0,06 ** 0,04 ***
Poland -0,04 -0,01 0,03 0,05 0,09 0,04 0,03 0,01 0,04
Slovak Republic 0,04 0,14 0,03 0,05 0,09 0,11 *** -0,10 0,07 0,08 ***
Sweden -0,14 * 0,19 ** -0,03 0,02 0,06 * 0,04 0,01 -0,09 *** 0,06 ***
United States -0,11 0,11 -0,05 0,01 0,04 0,21 *** -0,07 -0,04 0,09 ***
Flanders (Belgium) -0,17 ** -0,12 ** -0,10 * 0,01 0,01 0,05 ** 0,00 -0,04 0,02
England (UK) 0,09 -0,06 -0,02 0,01 0,05 0,18 *** -0,02 -0,17 *** 0,08 ***
Northern Ireland (UK) -0,19 * -0,04 -0,09 0,03 0,03 0,04 0,05 -0,06 0,09 ***
England/N. Ireland (UK) 0,08 -0,06 -0,02 0,01 0,05 0,17 *** -0,02 -0,16 *** 0,09 ***
Average1
-0,04 *** -0,01 -0,04 *** 0,00 0,02 0,10 *** -0,02 *** -0,06 *** 0,06 ***
Average-222
m m m m m m m m
Partners
Cyprus3
m m m m m m m m m
Russian Federation4
0,05 0,10 0,07 0,03 0,07 0,28 *** 0,03 0,16 0,12

[Part 4/4]
Table A4.16
characteristics
Version 5 (Version 4 + occupation)
No computer
experience
Failed ICT
Frequent use
of e-mail
Computer
workers
without
computer skills
to do the job
well
Computer
workers
whose skills
have affected
employment
Regular users
of complex
problem
solving
National entities
Australia 0.05 -0.08 -0.03 -0.04 -0.04 0.09 *** -0.03 -0.05 ** 0.06 ***
Austria 0.02 0.04 -0.13 ** -0.01 0.04 0.08 *** -0.02 -0.11 ** 0.07 ***
Canada -0.05 0.01 -0.05 -0.01 -0.01 0.13 *** 0.05 * -0.06 ** 0.07 ***
Czech Republic 0.07 -0.12 -0.04 -0.01 0.02 0.09 *** -0.08 -0.03 0.01
Denmark -0.04 0.03 -0.07 * 0.01 0.02 0.07 *** -0.04 *** 0.00 0.05 ***
Estonia -0.26 *** -0.14 ** -0.07 -0.01 0.02 0.08 ** -0.04 -0.07 ** 0.08 ***
Finland 0.04 0.01 -0.02 0.03 0.05 0.02 -0.03 0.00 0.03 ***
Germany -0.17 *** 0.00 -0.12 * -0.04 -0.03 0.11 *** -0.01 -0.13 ** 0.05 ***
Ireland 0.15 * -0.02 -0.02 0.02 0.01 0.05 0.02 -0.08 ** 0.00
Japan -0.16 ** -0.07 -0.07 -0.03 -0.02 0.11 *** -0.05 ** -0.03 * 0.00
Korea -0.14 -0.06 -0.02 -0.01 -0.08 0.16 *** -0.01 -0.10 0.06 *
Netherlands -0.02 0.06 -0.02 0.00 0.03 0.15 *** 0.00 -0.08 ** 0.07 ***
Norway -0.22 -0.04 -0.05 0.02 0.05 0.03 0.01 -0.05 ** 0.03 *
Poland -0.07 -0.02 0.03 0.05 0.07 0.04 0.03 0.00 0.02
Slovak Republic 0.02 0.13 0.03 0.04 0.08 0.11 *** -0.10 * 0.07 0.06 **
Sweden -0.15 ** 0.16 ** -0.04 0.02 0.04 0.00 0.00 -0.08 *** 0.04 ***
United States -0.11 0.11 -0.03 0.00 0.03 0.19 *** -0.07 -0.03 0.05
Flanders (Belgium) -0.16 ** -0.10 * -0.09 0.01 0.01 0.05 ** 0.00 -0.04 0.02
England (UK) 0.12 -0.02 0.00 0.01 0.05 0.13 *** 0.00 -0.15 *** 0.07 **
Northern Ireland (UK) -0.18 * -0.06 -0.12 * 0.02 0.01 0.03 0.03 -0.05 0.07 ***
England/N. Ireland (UK) 0.10 -0.02 0.00 0.01 0.05 0.12 *** 0.00 -0.15 *** 0.07 ***
Average1
-0.06 *** -0.01 -0.04 *** 0.00 0.02 0.09 *** -0.02 *** -0.05 *** 0.04 ***
Average-222
m m m m m m m m m
Partners
Cyprus3
m m m m m m m m m
Russian Federation4
0.06 0.11 0.06 0.04 0.07 0.28 *** 0.03 0.16 0.12

Annex B
additional TABLES
All tables in Annex B are available on line.
•• Chapter 1 tables . . . . . . . . . . . . . . . . . . . 141
•• Chapter 2 tables . . . . . . . . . . . . . . . . . . . 146
•• Chapter 3 tables . . . . . . . . . . . . . . . . . . . 150
•• Chapter 4 tables . . . . . . . . . . . . . . . . . . . 170

Annex B: additional Tables

additional Tables: Annex B
[Part 1/1]
Table B1.1
Percentage of households with access to a computer at home (including PC, portable, handheld),
2000 to 2011
OECD 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Australia 53.0 58.0 61.0 66.0 67.0 70.0 73.0 75.0 78.0 m 82.6 m
Austria 34.0 m 49.2 50.8 58.6 63.1 67.1 70.7 75.9 74.5 76.2 78.1
Belgium m m m m m m 57.5 67.2 70.0 71.1 76.7 78.9
Canada 55.2 59.8 64.1 66.6 68.7 72.0 75.4 78.4 79.4 81.7 82.7 m
Chile 17.9 m m 25.5 m m 34.5 m m 43.9 m m
Czech Republic m m m 23.8 29.5 30.0 39.0 43.4 52.4 59.6 64.1 69.9
Denmark 65.0 69.6 72.2 78.5 79.3 83.8 85.0 83.0 85.5 86.2 88.0 90.4
Estonia m m m m 36.0 43.0 52.4 57.2 59.6 65.1 69.2 71.4
Finland 47.0 52.9 54.5 57.4 57.0 64.0 71.1 74.0 75.8 80.1 82.0 85.1
France 27.0 32.4 36.6 45.7 49.8 m m 65.6 68.4 74.2 76.5 78.2
Germany 47.3 53.0 61.0 65.2 68.7 69.9 76.9 78.6 81.8 84.1 85.7 86.9
Greece m m 25.3 28.7 29.0 32.6 36.7 40.2 44.0 47.3 53.4 57.2
Hungary m m m m 31.9 42.3 49.6 53.5 58.8 63.0 66.4 69.7
Iceland m m m m 85.7 89.3 84.6 89.1 91.9 92.5 93.1 94.7
Ireland 32.4 m m 42.2 46.3 54.9 58.6 65.5 70.3 72.8 76.5 80.6
Israel 47.1 49.8 53.8 54.6 59.2 62.4 65.8 68.9 71.0 74.4 76.7 m
Italy 29.4 m 39.9 47.7 47.4 45.7 51.6 53.4 56.0 61.3 64.8 66.2
Japan 50.5 58.0 71.7 78.2 77.5 80.5 80.8 85.0 85.9 87.2 83.4 77.4
Korea 71.0 76.9 78.6 77.9 77.8 78.9 79.6 80.4 80.9 81.4 81.8 81.9
Luxembourg m m 52.6 58.0 67.3 74.5 77.3 80.0 82.8 87.9 90.2 91.7
Mexico m 11.8 15.2 m 18.0 18.6 20.6 22.1 25.7 26.8 29.9 30.0
Netherlands m m 69.0 70.8 74.0 77.9 80.0 86.3 87.7 90.8 92.0 94.2
New Zealand m 46.6 m 62.0 m m 72.0 m m 80.0 m m
Norway m m m 71.2 71.5 74.2 75.4 82.4 85.8 87.6 90.9 91.0
Poland m m m m 36.1 40.1 45.4 53.7 58.9 66.1 69.0 71.3
Portugal 27.0 39.0 26.8 38.3 41.3 42.5 45.6 48.3 49.8 56.0 59.5 63.7
Slovak Republic m m m m 38.5 46.7 50.1 55.4 63.2 64.0 72.2 75.4
Slovenia m m m m 58.0 61.0 65.3 66.0 65.1 71.2 70.5 74.4
Spain 30.4 m m 47.1 52.1 54.6 57.2 60.4 63.6 66.3 68.7 71.5
Sweden 59.9 69.2 m m m 79.7 82.5 82.9 87.1 87.6 89.5 91.6
Switzerland 57.7 62.2 65.4 68.9 70.6 76.5 77.4 79.2 81.4 82.5 m m
Turkey m m m m 10.2 12.2 m 27.3 33.4 37.4 44.2 m
United Kingdom 38.0 49.0 57.9 63.2 65.3 70.0 71.5 75.4 78.0 81.2 82.6 84.6
United States 51.0 56.2 m 61.8 m m m m m m 77.0 m
OECD average 45.7 52.8 53.0 57.6 54.2 59.0 64.2 66.1 69.3 71.4 74.7 77.2
Partners
Brazil m 12.6 14.2 15.3 16.3 18.5 22.1 26.5 31.2 32.3 34.9 45.4
China m m 10.2 14.3 20.0 25.0 27.0 29.0 31.8 34.4 35.4 38.0
India m m 0.3 1.0 1.5 2.0 3.0 3.7 4.4 5.3 6.1 6.9
Indonesia m m 2.5 3.0 2.8 3.7 4.4 5.9 8.3 10.2 10.8 12.0
Russian Federation m m 7.0 11.0 13.0 14.0 15.1 35.0 43.0 49.0 55.0 57.1
South Africa m 8.6 9.9 11.0 12.0 13.0 13.9 14.8 15.9 17.1 18.3 19.5
Source: OECD, ICT database; Eurostat, Community Survey on ICT usage in households and by individuals, June 2012; and for non-OECD countries: International
Telecommunication Union (ITU), World Telecommunication/ICT Indicators 2012 database, June 2012.

[Part 1/1]
Table B1.2 Percentage of households with access to the Internet, 2000-2011
OECD 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Australia 32.0 42.0 46.0 53.0 56.0 60.0 64.0 67.0 72.0 m 78.9 m
Austria 19.0 m 33.5 37.4 44.6 46.7 52.3 59.6 68.9 69.8 72.9 75.4
Belgium m m m m m 50.2 54.0 60.2 63.6 67.4 72.7 76.5
Canada 42.6 49.9 54.5 56.9 59.8 64.3 68.1 72.7 74.6 77.8 78.4 m
Chile 8.7 m m 12.8 m m 19.7 m m 30.0 m m
Czech Republic m m m 14.8 19.4 19.1 29.3 35.1 45.9 54.2 60.5 66.6
Denmark 46.0 59.0 55.6 64.2 69.4 74.9 78.7 78.1 81.9 82.5 86.1 90.1
Estonia m m m m 30.8 38.7 45.6 52.9 58.1 63.0 67.8 70.8
Finland 30.0 39.5 44.3 47.4 50.9 54.1 64.7 68.8 72.4 77.8 80.5 84.2
France 11.9 18.1 23.0 31.0 33.6 m 40.9 55.1 62.3 68.9 73.6 75.9
Germany 16.4 36.0 46.1 54.1 60.0 61.6 67.1 70.7 74.9 79.1 82.5 83.3
Greece m m 12.2 16.3 16.5 21.7 23.1 25.4 31.0 38.1 46.4 50.2
Hungary m m m m 14.2 22.1 32.3 38.4 48.4 55.1 60.5 65.2
Iceland m m m m 80.6 84.4 83.0 83.7 87.7 89.6 92.0 92.6
Ireland 20.4 m m 35.6 39.7 47.2 50.0 57.3 63.0 66.7 71.7 78.1
Israel 19.8 22.5 25.4 30.8 40.7 48.9 54.6 59.3 61.8 66.3 68.1 m
Italy 18.8 m 33.7 32.1 34.1 38.6 40.0 43.4 46.9 53.5 59.0 61.6
Japan m m 48.8 53.6 55.8 57.0 60.5 62.1 63.9 67.1 m m
Korea 49.8 63.2 70.2 68.8 86.0 92.7 94.0 94.1 94.3 95.9 96.8 97.2
Luxembourg m m 39.9 45.4 58.6 64.6 70.2 74.6 80.1 87.2 90.3 90.6
Mexico m 6.2 7.5 m 8.7 9.0 10.1 12.0 13.5 18.4 22.3 23.3
Netherlands 41.0 m 58.0 60.5 65.0 78.3 80.3 82.9 86.1 89.7 90.9 93.6
New Zealand m 37.4 m m m m 65.0 m m 75.0 m m
Norway m m m 60.5 60.1 64.0 68.8 77.6 84.0 85.6 89.8 92.2
Poland m m 11.0 14.0 26.0 30.4 35.9 41.0 47.6 58.6 63.4 66.6
Portugal 8.0 18.0 15.1 21.7 26.2 31.5 35.2 39.6 46.0 47.9 53.7 58.0
Slovak Republic m m m m 23.3 23.0 26.6 46.1 58.3 62.2 67.5 70.8
Slovenia m m m m 46.9 48.2 54.4 57.6 58.9 63.9 68.1 72.6
Spain m m m 27.5 33.6 35.5 39.1 44.6 51.0 54.0 59.1 63.9
Sweden 48.2 53.3 m m m 72.5 77.4 78.5 84.4 86.0 88.3 90.6
Switzerland m m m m 61.0 m 70.5 73.9 77.0 79.4 85.0 m
Turkey 6.9 m m m 7.0 7.7 m 19.7 25.4 30.0 41.6 m
United Kingdom 19.0 40.0 49.7 55.1 55.9 60.2 62.6 66.7 71.1 76.7 79.6 82.7
United States 41.5 50.3 m 54.6 m m m 61.7 m 68.7 71.1 m
OECD average 27.7 38.2 37.5 42.5 43.6 48.5 54.8 58.1 63.1 66.2 71.6 74.9
Partners
Brazil m 8.6 10.3 11.5 12.4 13.6 16.8 20.0 23.8 23.9 27.1 37.8
China m m 5.0 7.0 9.0 11.0 13.4 16.4 18.3 20.3 23.7 30.9
India m m 0.2 0.7 1.4 1.6 2.9 3.0 3.4 3.5 4.2 6.0
Indonesia m m m m m 1.0 1.2 1.3 1.9 2.7 4.6 7.0
Russian Federation m m 3.5 5.0 6.0 7.0 8.2 25.0 29.0 36.0 41.3 46.0
South Africa m m 1.9 2.1 2.5 3.0 3.6 4.8 6.5 8.8 10.1 9.8
Source: OECD, ICT database; Eurostat, Community Survey on ICT usage in households and by individuals, June 2012; and for non-OECD countries: International
Telecommunication Union (ITU), World Telecommunication/ICT Indicators 2012 database, June 2012.

[Part 1/1]
Table B1.3 Percentage of individuals aged 16-74 using any handheld device to access the Internet
OECD 2012
Austria 36
Belgium 30
Czech Republic 14
Denmark 51
Estonia 18
Finland 45
France 33
Germany 24
Greece 16
Hungary 12
Iceland 44
Ireland 29
Italy 12
Luxembourg 48
Netherlands 44
Norway 58
Poland 15
Portugal 13
Slovak Republic 27
Slovenia 22
Spain 31
Sweden 60
United Kingdom 57
Average 32
[Part 1/1]
Table B1.4 Percentage of Individuals using the Internet in middle income and developing countries
2013
Albania 60
Argentina 60
Bahrain 90
Bermuda 95
Bhutan 30
Brazil 52
Canada 86
China 46
Costa Rica 46
Egypt 50
India 15
Indonesia 16
Jordan 44
Kazakhstan 54
Lebanon 71
Malaysia 67
Morocco 56
Nigeria 38
Qatar 85
Romania 50
Russian Federation 61
Saudi Arabia 61
South Africa 49
Tunisia 44
Ukraine 42
United Arab Emirates 88
Source: International Telecommunication Union (ITU) estimate.

[Part 1/1]
Table B1.5 Percentage of individuals aged 16-74 using online banking
OECD 2005 2013 2014
Austria 22 49 48
Belgium 23 58 61
Czech Republic 5 41 46
Denmark 49 82 84
Estonia 45 72 77
Finland 56 84 86
France1
19 58 58
Germany1
32 47 49
Greece 1 11 13
Hungary 6 26 30
Iceland 61 87 91
Ireland 13 46 48
Italy 8 22 26
Luxembourg 37 63 67
Netherlands 50 82 83
Norway 62 87 89
Poland 6 32 33
Portugal 8 23 25
Slovak Republic 10 39 41
Slovenia 12 32 32
Spain 14 33 37
Sweden 51 82 82
Turkey 2 11 m
United Kingdom 27 54 57
Average 26 51 55
Notes: Within the three months prior to the survey. Internet banking includes electronic transactions with a bank for payment etc. or for looking up account information.
[Part 1/1]
Table B1.6 Percentage of individuals aged 16-74 using the Internet for sending and/or receiving e-mails
OECD 2005 2013 2014
Austria 48 74 73
Belgium 49 76 77
Czech Republic 27 70 74
Denmark 69 88 90
Estonia 49 67 72
Finland 63 83 86
France1
34 74 73
Germany1
60 78 80
Greece 14 46 50
Hungary 31 69 71
Iceland 75 93 93
Ireland 31 67 67
Italy 26 51 53
Luxembourg 63 88 89
Netherlands 73 90 90
Norway 68 88 90
Poland 24 51 53
Portugal 26 53 54
Slovak Republic 42 71 69
Slovenia 36 63 62
Spain 34 62 64
Sweden 67 87 86
Switzerland m m 84
Turkey 9 27 m
United Kingdom 57 79 80
Average 45 71 74
Notes: Within the three months prior to the survey.

[Part 1/1]
Table B1.7 Percentage of enterprises (with at least 10 employees) sending and/or receiving e-invoices
OECD 2007 2008 2009 2010
Austria 18 17 12 18
Belgium 31 36 39 39
Czech Republic 33 17 18 17
Denmark 37 43 38 39
Estonia 25 39 40 39
Finland 27 25 24 36
France 10 20 21 36
Germany 19 27 31 36
Greece 10 15 11 16
Hungary 4 5 6 8
Iceland m 20 m 25
Ireland 26 22 21 28
Italy 34 29 34 56
Luxembourg 23 24 20 37
Netherlands 11 29 34 35
Norway 29 31 31 47
Poland 8 11 12 16
Portugal 14 24 23 27
Slovak Republic 14 23 30 34
Slovenia 7 8 9 10
Spain 9 12 17 25
Sweden 18 17 25 28
Turkey 5 m m 13
United Kingdom 15 11 8 11
Average 19 22 23 28

[Part 1/3]
Table B2.1 Percentage of adults who opted out of taking the computer-based assessment by various characteristics
Age Education
16-24 year-olds 25-34 year-olds 35-44 year-olds 45-54 year-olds 55-65 year-olds
Lower than
upper secondary
Upper
secondary Tertiary
National entities
Australia 6.9 (1.1) 9.5 (1.1) 13.5 (1.2) 16.8 (1.1) 22.3 (1.5) 21.9 (1.3) 13.9 (0.9) 7.4 (0.7)
Austria 4.6 (0.8) 7.8 (1.1) 10.5 (1.0) 14.4 (1.0) 17.3 (1.2) 15.6 (1.2) 11.3 (0.6) 6.6 (0.8)
Canada 1.9 (0.3) 3.0 (0.4) 6.0 (0.6) 8.1 (0.6) 11.5 (0.7) 10.6 (0.9) 7.2 (0.4) 4.3 (0.3)
Czech Republic 4.0 (0.9) 6.0 (0.9) 11.4 (1.5) 19.5 (2.7) 18.5 (1.7) 13.5 (1.7) 13.3 (1.0) 6.5 (1.4)
Denmark 2.5 (0.5) 3.7 (0.5) 4.3 (0.6) 7.3 (0.7) 12.7 (0.8) 11.7 (0.8) 5.9 (0.4) 2.8 (0.3)
Estonia 3.7 (0.5) 8.2 (0.7) 14.6 (0.9) 23.2 (1.1) 28.5 (1.1) 12.4 (0.8) 18.6 (0.6) 14.2 (0.7)
Finland 1.8 (0.5) 1.6 (0.4) 4.7 (0.8) 10.9 (0.9) 24.2 (1.3) 14.8 (1.1) 12.4 (0.7) 3.8 (0.4)
France 3.9 (0.5) 8.4 (0.7) 10.8 (0.8) 15.2 (1.0) 17.7 (1.0) 16.1 (0.8) 11.7 (0.6) 6.8 (0.6)
Germany 1.3 (0.4) 3.2 (0.8) 6.3 (0.9) 7.9 (1.0) 9.9 (1.1) 7.0 (1.1) 6.9 (0.7) 4.4 (0.6)
Ireland 7.2 (1.1) 12.0 (1.2) 16.7 (1.1) 24.6 (1.6) 29.1 (1.7) 25.5 (1.2) 19.0 (1.1) 8.4 (0.6)
Italy 6.3 (1.4) 11.7 (1.4) 16.7 (1.2) 18.2 (1.5) 17.0 (1.7) 16.2 (1.1) 15.0 (1.1) 7.6 (1.2)
Japan 12.9 (1.6) 12.3 (1.5) 13.9 (1.4) 16.1 (1.3) 22.2 (1.5) 17.4 (1.6) 20.1 (1.3) 11.5 (0.9)
Korea 0.8 (0.3) 1.6 (0.3) 4.2 (0.5) 9.4 (0.8) 10.6 (0.9) 8.0 (0.7) 6.8 (0.5) 2.1 (0.3)
Netherlands 1.6 (0.5) 1.8 (0.5) 3.0 (0.5) 5.8 (0.7) 9.0 (0.9) 8.2 (0.7) 3.7 (0.4) 2.1 (0.4)
Norway 1.1 (0.4) 2.9 (0.6) 5.6 (0.6) 6.5 (0.8) 17.0 (1.4) 12.0 (0.9) 7.0 (0.5) 2.5 (0.4)
Poland 12.4 (0.7) 19.3 (1.3) 28.1 (1.6) 30.3 (1.8) 28.5 (1.4) 14.7 (1.2) 28.3 (0.9) 18.8 (1.1)
Slovak Republic 6.9 (0.7) 9.9 (0.8) 10.8 (0.9) 14.6 (1.2) 18.6 (1.3) 9.1 (0.8) 14.4 (0.6) 8.6 (1.0)
Spain 3.5 (0.6) 7.7 (0.8) 11.1 (0.9) 12.6 (0.9) 15.4 (1.3) 13.2 (0.7) 11.6 (1.0) 6.1 (0.6)
Sweden 0.7 (0.3) 2.0 (0.6) 4.3 (0.8) 7.0 (1.0) 13.0 (1.0) 10.3 (1.1) 4.9 (0.5) 2.9 (0.4)
United States 3.0 (0.7) 4.7 (0.9) 5.0 (0.7) 7.0 (0.9) 12.0 (1.2) 11.9 (1.4) 8.2 (1.0) 2.2 (0.4)
England (UK) 0.8 (0.4) 2.5 (0.5) 3.7 (0.6) 6.3 (0.9) 9.4 (1.1) 8.0 (0.9) 3.8 (0.6) 3.0 (0.5)
Average1
4.0 (0.2) 6.0 (0.2) 8.9 (0.2) 12.7 (0.3) 17.0 (0.3) 12.6 (0.3) 11.1 (0.2) 6.0 (0.2)
Average-222
4.1 (0.2) 6.5 (0.2) 9.5 (0.2) 13.1 (0.3) 17.0 (0.3) 13.0 (0.2) 11.3 (0.2) 6.1 (0.2)
Partners
Cyprus3
12.8 (1.5) 15.4 (1.2) 20.3 (1.3) 22.7 (1.4) 19.5 (1.5) 7.8 (0.8) 21.9 (1.0) 20.8 (1.0)
Russian Federation4
6.6 (1.3) 12.5 (1.7) 14.6 (2.9) 13.6 (1.9) 16.2 (2.1) 3.2 (1.0) 11.3 (1.5) 14.7 (2.3)

[Part 2/3]
Gender Parents’ Education Immigrant and language background
Men Women
Neither parent
attained upper
secondary
At least
one parent
attained upper
secondary
At least one
parent attained
tertiary
Native-born
and native
language
Native-born
and foreign
language
Foreign-born
and native
language
Foreign-born
and foreign
language
OECD % S.E. % S.E. % S.E. % S.E. % S.E. % S.E. % S.E. % S.E. % S.E.
National entities
Australia 13.1 (0.8) 14.3 (0.7) 18.6 (0.9) 12.1 (1.1) 7.1 (0.8) 13.7 (0.7) 12.3 (2.8) 12.6 (1.3) 17.1 (1.6)
Austria 9.9 (0.6) 12.5 (0.7) 16.4 (1.2) 10.3 (0.7) 7.5 (1.0) 10.9 (0.5) 9.6 (3.3) 9.4 (2.5) 16.2 (1.9)
Canada 6.0 (0.4) 6.6 (0.3) 11.2 (0.7) 6.2 (0.5) 3.1 (0.2) 5.5 (0.3) 5.6 (0.8) 8.0 (1.2) 9.1 (0.8)
Czech Republic 10.4 (0.9) 13.8 (1.1) 20.6 (3.0) 11.6 (0.9) 5.8 (1.3) 12.0 (0.9) c c 7.8 (4.7) 18.5 (6.3)
Denmark 6.5 (0.4) 6.3 (0.4) 9.9 (0.6) 6.7 (0.5) 2.6 (0.4) 5.6 (0.3) 6.0 (3.1) 2.3 (0.9) 13.8 (1.0)
Estonia 14.4 (0.5) 17.1 (0.6) 24.8 (1.1) 14.4 (0.7) 9.5 (0.7) 14.7 (0.5) 19.5 (2.4) 23.1 (1.6) 21.7 (3.9)
Finland 9.4 (0.6) 10.0 (0.6) 17.3 (0.8) 5.8 (0.5) 2.1 (0.4) 9.8 (0.4) 4.1 (2.0) 2.8 (2.7) 18.0 (4.1)
France 11.4 (0.5) 11.8 (0.6) 16.0 (0.7) 7.5 (0.6) 7.1 (0.7) 10.5 (0.4) 9.5 (2.3) 21.0 (1.9) 18.9 (1.8)
Germany 4.9 (0.5) 7.3 (0.7) 10.7 (2.0) 6.0 (0.6) 4.0 (0.7) 5.5 (0.5) 3.6 (2.3) 8.0 (2.4) 11.2 (1.8)
Ireland 17.1 (0.8) 17.7 (0.9) 24.1 (1.0) 12.1 (1.1) 8.6 (0.9) 17.3 (0.7) 41.7 (7.9) 14.3 (1.5) 20.5 (2.7)
Italy 14.6 (1.0) 14.6 (1.1) 16.7 (1.0) 10.8 (1.4) 7.1 (1.9) 14.0 (0.9) 22.9 (6.7) 9.3 (3.9) 22.8 (3.1)
Japan 13.2 (0.9) 18.7 (1.2) 19.0 (1.3) 17.1 (1.3) 12.8 (1.2) 16.2 (0.9) c c c c c c
Korea 5.5 (0.4) 5.3 (0.4) 7.8 (0.5) 2.9 (0.5) 2.4 (0.4) 5.2 (0.3) c c 7.3 (3.6) 18.9 (6.7)
Netherlands 3.8 (0.4) 5.2 (0.4) 6.2 (0.5) 3.4 (0.5) 2.1 (0.4) 3.7 (0.3) 3.8 (2.9) 5.4 (1.9) 12.2 (1.6)
Norway 5.9 (0.5) 7.5 (0.6) 14.1 (1.0) 5.4 (0.6) 2.4 (0.4) 6.6 (0.4) 8.2 (4.2) 5.1 (2.9) 8.6 (1.2)
Poland 21.5 (0.8) 26.0 (0.9) 26.8 (1.3) 24.4 (0.8) 14.7 (1.3) 23.7 (0.7) 29.1 (5.8) c c c c
Slovak Republic 11.5 (0.7) 12.9 (0.6) 13.6 (0.8) 12.3 (0.7) 8.5 (1.0) 11.9 (0.4) 16.8 (2.2) 17.1 (4.8) 11.4 (4.8)
Spain 9.4 (0.6) 11.9 (0.8) 12.0 (0.7) 7.8 (1.3) 6.7 (1.0) 10.5 (0.6) 13.4 (3.0) 11.8 (1.8) 11.6 (2.1)
Sweden 5.2 (0.5) 6.2 (0.5) 9.5 (0.8) 3.4 (0.7) 2.2 (0.4) 5.1 (0.4) 3.6 (2.1) 2.8 (2.2) 9.4 (1.1)
United States 6.3 (0.8) 6.4 (0.6) 13.8 (1.6) 6.2 (0.8) 3.4 (0.7) 6.2 (0.7) 5.9 (2.4) 4.3 (1.4) 10.6 (1.2)
Flanders (Belgium) 4.7 (0.5) 4.7 (0.4) 8.1 (0.6) 2.8 (0.4) 2.2 (0.4) 4.5 (0.3) 9.6 (2.3) 5.9 (1.9) 11.7 (2.4)
England (UK) 4.1 (0.5) 5.0 (0.6) 8.1 (1.0) 3.5 (0.5) 2.2 (0.5) 4.4 (0.4) 3.6 (2.4) 5.2 (1.5) 6.2 (1.5)
Northern Ireland (UK) 2.1 (0.4) 2.5 (0.4) 3.8 (0.6) 1.6 (0.4) 0.6 (0.4) 2.4 (0.3) c c 2.0 (1.4) 0.8 (0.8)
England/N. Ireland (UK) 4.1 (0.5) 4.9 (0.5) 7.8 (1.0) 3.4 (0.5) 2.2 (0.5) 4.4 (0.4) 3.6 (2.4) 5.1 (1.5) 6.1 (1.5)
Average1
9.1 (0.1) 10.7 (0.2) 14.7 (0.3) 8.8 (0.2) 5.4 (0.2) 9.6 (0.1) 11.4 (0.9) 8.3 (0.6) 13.8 (0.8)
Average-222
9.5 (0.1) 11.0 (0.2) 14.8 (0.3) 8.8 (0.2) 5.6 (0.2) 9.9 (0.1) 12.0 (0.8) 9.2 (0.6) 14.4 (0.7)
Partners
Cyprus3
17.5 (0.8) 18.5 (0.8) 24.6 (1.0) 19.8 (1.2) 16.6 (1.3) 21.4 (0.6) c c 23.3 (2.9) 28.2 (4.1)
Russian Federation4
10.8 (1.7) 14.6 (2.4) 17.3 (2.2) 10.8 (1.7) 12.1 (2.1) m m m m m m m m
Notes: Results for the Russian Federation are missing as no language variables are available for the Russian Federation.

[Part 3/3]
Participation in adult education E-mail use Literacy proficiency
Did not
participate
in AET
Did participate
in AET
Low frequency
of e-mail use
(less than
monthly or no
use)
High frequency
of e-mail use
(at least monthly
use)
At or below
Level 1 Level 2 Level 3 Level 4/5
National entities
Australia 21.4 (1.0) 9.9 (0.7) 31.4 (1.5) 8.9 (0.6) 22.7 (2.3) 15.9 (1.3) 11.9 (0.9) 9.0 (1.2)
Austria 15.6 (0.8) 9.0 (0.7) 24.7 (1.1) 5.8 (0.4) 14.5 (1.6) 14.3 (0.9) 8.5 (0.8) 6.3 (1.6)
Canada 10.8 (0.5) 4.3 (0.3) 18.1 (0.8) 3.5 (0.2) 9.6 (0.9) 7.0 (0.5) 5.2 (0.5) 4.2 (0.7)
Czech Republic 16.7 (1.1) 9.8 (1.1) 23.2 (2.0) 8.1 (0.9) 9.4 (1.8) 13.1 (1.4) 12.3 (1.3) 10.8 (3.2)
Denmark 12.8 (0.7) 4.2 (0.3) 23.4 (1.4) 3.6 (0.2) 15.1 (1.2) 7.6 (0.7) 3.2 (0.4) 1.1 (0.6)
Estonia 22.1 (0.8) 13.7 (0.6) 31.7 (1.1) 10.7 (0.4) 11.7 (1.3) 15.8 (0.9) 17.0 (0.8) 16.5 (1.6)
Finland 20.0 (1.0) 6.1 (0.4) 31.0 (1.6) 5.0 (0.4) 15.5 (1.8) 13.4 (1.1) 8.5 (0.7) 4.8 (0.8)
France 15.5 (0.6) 8.0 (0.6) 22.5 (1.0) 7.5 (0.4) 11.1 (1.0) 12.2 (0.8) 11.0 (0.7) 14.0 (1.7)
Germany 9.5 (0.9) 4.3 (0.5) 16.7 (1.4) 2.4 (0.3) 8.2 (1.4) 7.6 (0.9) 4.8 (0.7) 2.9 (1.1)
Ireland 24.1 (1.0) 15.1 (1.0) 30.9 (1.1) 10.7 (0.8) 17.8 (1.8) 19.8 (1.3) 16.5 (1.0) 11.2 (1.9)
Italy 16.8 (1.1) 12.7 (1.3) 21.7 (1.3) 8.8 (0.9) 11.5 (1.4) 16.6 (1.2) 15.1 (1.5) 15.3 (3.9)
Japan 20.2 (1.2) 11.7 (1.0) 23.5 (1.3) 10.8 (1.0) 12.9 (2.8) 18.6 (1.6) 16.8 (1.1) 12.7 (1.4)
Korea 8.1 (0.6) 4.1 (0.4) 9.1 (0.6) 2.7 (0.3) 5.3 (1.0) 6.9 (0.6) 4.7 (0.5) 2.6 (1.0)
Netherlands 8.3 (0.8) 3.1 (0.3) 18.6 (1.8) 2.9 (0.2) 10.8 (1.4) 5.8 (0.9) 3.1 (0.4) 2.3 (0.6)
Norway 14.9 (1.0) 3.7 (0.4) 26.7 (1.7) 3.8 (0.3) 11.5 (1.6) 8.7 (0.8) 5.3 (0.6) 3.1 (1.0)
Poland 29.3 (0.8) 19.4 (1.2) 31.5 (1.0) 18.1 (0.9) 20.6 (1.6) 24.8 (1.4) 23.3 (1.2) 27.8 (3.1)
Slovak Republic 14.8 (0.7) 10.1 (0.8) 19.0 (0.9) 8.0 (0.5) 7.4 (1.3) 12.9 (0.8) 13.2 (0.8) 10.6 (2.4)
Spain 13.9 (0.9) 8.4 (0.6) 17.8 (1.1) 6.2 (0.5) 9.0 (1.0) 12.2 (0.9) 10.7 (1.1) 9.4 (2.9)
Sweden 11.4 (0.8) 3.7 (0.4) 23.4 (1.7) 2.4 (0.3) 14.0 (1.7) 7.7 (1.0) 3.4 (0.5) 0.9 (0.4)
United States 11.3 (1.0) 4.5 (0.7) 17.8 (1.5) 2.7 (0.4) 10.3 (1.4) 8.7 (1.0) 4.5 (0.7) 1.4 (0.6)
England (UK) 6.7 (0.7) 3.8 (0.5) 13.8 (1.3) 2.1 (0.3) 5.3 (1.0) 5.1 (0.7) 4.1 (0.6) 3.9 (1.1)
Average1
15.0 (0.2) 7.5 (0.2) 22.5 (0.3) 6.1 (0.1) 12.1 (0.4) 11.5 (0.2) 9.0 (0.2) 7.1 (0.4)
Average-222
15.1 (0.2) 7.8 (0.2) 22.3 (0.3) 6.2 (0.1) 11.9 (0.3) 11.8 (0.2) 9.4 (0.2) 7.9 (0.4)
Partners
Cyprus3
23.3 (1.0) 23.9 (1.1) 24.9 (1.0) 18.8 (0.9) 11.3 (1.9) 17.2 (1.2) 26.4 (1.2) 47.1 (4.0)
Russian Federation4
14.0 (1.7) 12.3 (3.4) 16.1 (1.8) 8.5 (1.7) 9.3 (3.3) 11.6 (1.8) 14.4 (2.5) 14.6 (3.8)

[Part 1/1]
Table B2.2 Percentage of individuals aged 16-74 using the Internet for seeking health-related information
OECD 2005 2013
Austria 16 49
Belgium 19 43
Czech Republic 3 41
Denmark 24 54
Estonia 16 39
Finland 39 60
France1
13 49
Germany1
34 58
Greece 2 34
Hungary 10 49
Iceland 39 65
Ireland 10 38
Italy 9 32
Luxembourg 41 58
Netherlands 41 57
Norway 26 54
Poland 7 27
Portugal 10 42
Slovenia 15 50
Spain 13 44
Sweden 23 56
Turkey 3 26
Average 19 46

[Part 1/3]
Table B3.1
Likelihood of adults scoring at Level 2 or 3 in problem solving in technology-rich environments,
by socio-demographic characteristics (Version 1)
Age
(reference 55-65 year-olds)
16-24 year-olds 25-34 year-olds 35-44 year-olds 45-54 year-olds
OECD ß S.E. p-value ß S.E. p-value ß S.E. p-value ß S.E. p-value
National entities
Australia 0.9 (0.2) 0.000 1.1 (0.1) 0.000 0.9 (0.1) 0.000 0.6 (0.2) 0.000
Austria 2.1 (0.2) 0.000 2.3 (0.2) 0.000 1.8 (0.2) 0.000 1.1 (0.2) 0.000
Canada 1.0 (0.2) 0.000 1.2 (0.1) 0.000 1.0 (0.1) 0.000 0.5 (0.1) 0.000
Czech Republic 1.4 (0.3) 0.000 1.7 (0.2) 0.000 0.9 (0.2) 0.000 0.3 (0.2) 0.246
Denmark 1.7 (0.2) 0.000 2.1 (0.1) 0.000 1.7 (0.1) 0.000 1.0 (0.1) 0.000
Estonia 2.3 (0.2) 0.000 2.2 (0.2) 0.000 1.5 (0.2) 0.000 0.8 (0.2) 0.000
Finland 2.7 (0.2) 0.000 2.8 (0.2) 0.000 2.1 (0.2) 0.000 1.2 (0.1) 0.000
Germany 2.0 (0.2) 0.000 2.1 (0.2) 0.000 1.5 (0.2) 0.000 0.8 (0.2) 0.000
Ireland 1.9 (0.3) 0.000 1.8 (0.2) 0.000 1.5 (0.2) 0.000 0.9 (0.2) 0.000
Japan 1.6 (0.2) 0.000 2.1 (0.2) 0.000 1.8 (0.2) 0.000 1.0 (0.1) 0.000
Korea 2.8 (0.3) 0.000 2.3 (0.2) 0.000 1.6 (0.2) 0.000 0.7 (0.2) 0.001
Netherlands 1.5 (0.2) 0.000 1.7 (0.1) 0.000 1.5 (0.2) 0.000 0.8 (0.1) 0.000
Norway 2.0 (0.2) 0.000 2.1 (0.1) 0.000 1.7 (0.2) 0.000 1.1 (0.1) 0.000
Poland 1.9 (0.3) 0.000 1.9 (0.3) 0.000 1.6 (0.3) 0.000 0.8 (0.3) 0.008
Slovak Republic 0.9 (0.3) 0.001 1.1 (0.2) 0.000 0.8 (0.2) 0.000 0.4 (0.2) 0.097
Sweden 1.7 (0.2) 0.000 1.7 (0.1) 0.000 1.4 (0.1) 0.000 0.8 (0.1) 0.000
United States 0.8 (0.3) 0.013 0.9 (0.2) 0.000 0.7 (0.2) 0.000 0.3 (0.2) 0.041
Flanders (Belgium) 1.8 (0.2) 0.000 1.7 (0.2) 0.000 1.2 (0.1) 0.000 0.6 (0.1) 0.000
England (UK) 0.6 (0.2) 0.009 1.2 (0.1) 0.000 0.9 (0.2) 0.000 0.5 (0.2) 0.004
Northern Ireland (UK) 1.4 (0.3) 0.000 1.4 (0.3) 0.000 1.0 (0.2) 0.000 0.5 (0.3) 0.068
England/N. Ireland (UK) 0.7 (0.2) 0.006 1.2 (0.1) 0.000 0.9 (0.2) 0.000 0.5 (0.2) 0.003
Average1
1.7 (0.1) 0.000 1.8 (0.0) 0.000 1.4 (0.0) 0.000 0.8 (0.0) 0.000
Average-222
Partners
Cyprus3
Russian Federation4
Note: Results for the Russian Federation are missing due to the lack of the language variables.

[Part 2/3]
Table B3.1
(reference foreign-born and foreign language)
(reference lower than upper secondary)
Native-born
and native language
Native-born
and foreign language
Foreign-born
and native language Upper secondary Tertiary
OECD ß S.E. p-value ß S.E. p-value ß S.E. p-value ß S.E. p-value ß S.E. p-value
National entities
Australia 1.2 (0.1) 0.000 1.0 (0.3) 0.001 1.1 (0.2) 0.000 0.6 (0.1) 0.000 1.4 (0.1) 0.000
Austria 1.4 (0.2) 0.000 0.9 (0.3) 0.004 1.5 (0.3) 0.000 1.1 (0.2) 0.000 1.8 (0.2) 0.000
Canada 1.0 (0.1) 0.000 1.0 (0.2) 0.000 0.5 (0.1) 0.000 0.8 (0.1) 0.000 1.6 (0.1) 0.000
Czech Republic 1.0 (0.5) 0.246 c c c 1.3 (0.7) 0.089 0.5 (0.2) 0.005 1.7 (0.2) 0.000
Denmark 1.7 (0.1) 0.000 1.3 (0.4) 0.002 1.4 (0.3) 0.000 0.8 (0.1) 0.000 1.7 (0.1) 0.000
Estonia 0.2 (0.4) 0.000 0.0 (0.4) 0.972 -0.2 (0.4) 0.627 0.8 (0.1) 0.000 1.5 (0.1) 0.000
Finland 1.5 (0.4) 0.000 0.8 (0.4) 0.082 1.7 (0.5) 0.001 0.7 (0.1) 0.000 2.0 (0.2) 0.000
France m m m m m m m m m m m m m m m
Germany 1.6 (0.2) 0.000 0.9 (0.4) 0.032 1.2 (0.3) 0.000 0.8 (0.2) 0.000 1.8 (0.2) 0.000
Ireland 0.9 (0.2) 0.000 0.4 (0.5) 0.388 1.0 (0.2) 0.000 1.0 (0.2) 0.000 2.0 (0.2) 0.000
Italy m m m m m m m m m m m m m m m
Japan c c 0.000 c c c c c c 0.9 (0.2) 0.000 1.7 (0.2) 0.000
Korea 4.8 (9.5) 0.001 c c c 3.9 (9.5) 0.680 0.7 (0.2) 0.000 1.8 (0.2) 0.000
Netherlands 1.5 (0.2) 0.000 0.4 (0.6) 0.449 1.2 (0.3) 0.000 1.0 (0.1) 0.000 2.0 (0.1) 0.000
Norway 1.6 (0.1) 0.000 0.8 (0.4) 0.024 1.2 (0.3) 0.001 0.7 (0.1) 0.000 1.9 (0.1) 0.000
Poland c c 0.008 c c c c c c 0.2 (0.1) 0.125 1.4 (0.2) 0.000
Slovak Republic 0.6 (0.6) 0.097 0.0 (0.7) 0.941 0.0 (0.8) 0.978 0.7 (0.1) 0.000 1.6 (0.2) 0.000
Spain m m m m m m m m m m m m m m m
Sweden 1.8 (0.2) 0.000 1.3 (0.3) 0.000 1.2 (0.3) 0.001 1.2 (0.2) 0.000 2.2 (0.2) 0.000
United States 1.3 (0.2) 0.041 1.3 (0.4) 0.001 0.6 (0.3) 0.094 0.9 (0.2) 0.000 2.0 (0.2) 0.000
Flanders (Belgium) 1.6 (0.3) 0.000 1.2 (0.4) 0.003 1.5 (0.4) 0.000 0.8 (0.2) 0.000 2.0 (0.2) 0.000
England (UK) 1.2 (0.2) 0.004 1.0 (0.4) 0.011 0.7 (0.3) 0.017 1.0 (0.1) 0.000 1.8 (0.2) 0.000
Northern Ireland (UK) 1.0 (0.4) 0.068 c c c 0.7 (0.6) 0.252 1.1 (0.3) 0.000 2.0 (0.3) 0.000
England/N. Ireland (UK) 1.2 (0.2) 0.003 1.0 (0.4) 0.010 0.7 (0.3) 0.014 1.0 (0.1) 0.000 1.8 (0.2) 0.000
Average1
1.5 (0.6) 0.000 0.8 (0.1) 0.000 1.2 (0.6) 0.040 0.8 (0.0) 0.000 1.8 (0.0) 0.000
Average-222
m m m m m m m m m m m m m m m
Partners
Cyprus3
Russian Federation4

[Part 3/3]
Table B3.1
Gender
(reference women)
(reference neither parent attained upper secondary)
and training
(reference did not participate)
Men
At least one parent attained
upper secondary
At least one parent
attained tertiary Participated
National entities
Australia 0.1 (0.1) 0.397 0.5 (0.1) 0.000 0.7 (0.1) 0.000 0.8 (0.1) 0.000
Austria 0.4 (0.1) 0.000 0.6 (0.1) 0.000 1.0 (0.1) 0.000 0.6 (0.1) 0.000
Canada 0.1 (0.1) 0.155 0.6 (0.1) 0.000 0.8 (0.1) 0.000 0.7 (0.1) 0.000
Czech Republic 0.2 (0.1) 0.049 0.8 (0.3) 0.006 1.4 (0.3) 0.000 0.6 (0.1) 0.000
Denmark 0.3 (0.1) 0.000 0.1 (0.1) 0.345 0.6 (0.1) 0.000 0.6 (0.1) 0.000
Estonia 0.2 (0.1) 0.032 0.5 (0.1) 0.001 1.1 (0.1) 0.000 0.9 (0.1) 0.000
Finland 0.3 (0.1) 0.001 0.5 (0.1) 0.000 1.0 (0.1) 0.000 0.5 (0.1) 0.000
Germany 0.3 (0.1) 0.002 0.8 (0.2) 0.000 1.2 (0.2) 0.000 0.7 (0.1) 0.000
Ireland 0.4 (0.1) 0.001 0.5 (0.1) 0.000 1.0 (0.1) 0.000 0.6 (0.1) 0.000
Japan 0.6 (0.1) 0.000 0.0 (0.1) 0.906 0.4 (0.1) 0.001 0.6 (0.1) 0.000
Korea 0.3 (0.1) 0.000 0.3 (0.1) 0.021 0.6 (0.1) 0.000 0.6 (0.1) 0.000
Netherlands 0.4 (0.1) 0.000 0.3 (0.1) 0.010 0.5 (0.1) 0.000 0.4 (0.1) 0.000
Norway 0.5 (0.1) 0.000 0.5 (0.1) 0.000 0.8 (0.1) 0.000 0.5 (0.1) 0.000
Poland 0.4 (0.1) 0.001 0.6 (0.2) 0.004 1.3 (0.2) 0.000 0.7 (0.1) 0.000
Slovak Republic 0.1 (0.1) 0.261 0.8 (0.1) 0.000 1.1 (0.2) 0.000 0.9 (0.1) 0.000
Sweden 0.3 (0.1) 0.004 0.5 (0.1) 0.000 0.8 (0.1) 0.000 0.7 (0.1) 0.000
United States 0.2 (0.1) 0.019 1.0 (0.2) 0.000 1.4 (0.2) 0.000 0.6 (0.1) 0.000
England (UK) 0.5 (0.1) 0.000 0.8 (0.1) 0.000 1.2 (0.1) 0.000 0.6 (0.1) 0.000
Average1
0.3 (0.0) 0.000 0.5 (0.0) 0.000 0.9 (0.0) 0.000 0.6 (0.0) 0.000
Average-222
Partners
Cyprus3
Russian Federation4

[Part 1/3]
Table B3.2
by socio-demographic characteristics and ICT use (Version 2)
Age
National entities
Australia 0.8 (0.2) 0.000 1.0 (0.1) 0.000 0.9 (0.1) 0.000 0.6 (0.2) 0.000
Austria 1.9 (0.2) 0.000 2.1 (0.2) 0.000 1.7 (0.2) 0.000 1.0 (0.2) 0.000
Canada 0.8 (0.2) 0.000 1.1 (0.1) 0.000 0.9 (0.1) 0.000 0.5 (0.1) 0.000
Czech Republic 1.0 (0.3) 0.001 1.4 (0.2) 0.000 0.7 (0.2) 0.007 0.2 (0.3) 0.510
Denmark 1.7 (0.2) 0.000 2.1 (0.1) 0.000 1.6 (0.1) 0.000 1.0 (0.1) 0.000
Estonia 1.9 (0.2) 0.000 1.9 (0.2) 0.000 1.3 (0.2) 0.000 0.6 (0.2) 0.000
Finland 2.5 (0.2) 0.000 2.6 (0.2) 0.000 2.0 (0.2) 0.000 1.2 (0.1) 0.000
Germany 1.6 (0.2) 0.000 1.9 (0.2) 0.000 1.3 (0.2) 0.000 0.7 (0.2) 0.000
Ireland 1.6 (0.3) 0.000 1.6 (0.2) 0.000 1.4 (0.2) 0.000 0.9 (0.2) 0.000
Japan 1.5 (0.2) 0.000 1.9 (0.2) 0.000 1.6 (0.2) 0.000 0.9 (0.1) 0.000
Korea 2.6 (0.3) 0.000 2.1 (0.2) 0.000 1.5 (0.2) 0.000 0.6 (0.2) 0.004
Netherlands 1.4 (0.2) 0.000 1.7 (0.1) 0.000 1.5 (0.2) 0.000 0.8 (0.1) 0.000
Norway 1.9 (0.2) 0.000 2.0 (0.2) 0.000 1.6 (0.2) 0.000 1.0 (0.1) 0.000
Poland 1.4 (0.3) 0.000 1.6 (0.3) 0.000 1.3 (0.3) 0.000 0.8 (0.3) 0.017
Slovak Republic 0.4 (0.3) 0.174 0.6 (0.2) 0.001 0.4 (0.2) 0.076 0.2 (0.2) 0.354
Sweden 1.6 (0.2) 0.000 1.6 (0.1) 0.000 1.3 (0.2) 0.000 0.8 (0.1) 0.000
United States 0.7 (0.3) 0.042 0.7 (0.2) 0.000 0.6 (0.2) 0.000 0.3 (0.2) 0.067
England (UK) 0.6 (0.2) 0.022 1.1 (0.2) 0.000 0.8 (0.2) 0.000 0.5 (0.2) 0.004
Average1
1.5 (0.1) 0.000 1.6 (0.0) 0.000 1.2 (0.0) 0.000 0.7 (0.0) 0.000
Average-222
Partners
Cyprus3
Russian Federation4

[Part 2/3]
Table B3.2
Native-born
and native language
Native-born
Foreign-born
National entities
Australia 1.2 (0.1) 0.000 1.0 (0.3) 0.001 1.0 (0.2) 0.000 0.4 (0.1) 0.001 1.1 (0.1) 0.000
Austria 1.4 (0.2) 0.000 0.8 (0.3) 0.008 1.4 (0.3) 0.000 0.9 (0.2) 0.000 1.5 (0.2) 0.000
Canada 1.0 (0.1) 0.000 0.9 (0.2) 0.000 0.5 (0.1) 0.000 0.7 (0.1) 0.000 1.4 (0.2) 0.000
Denmark 1.7 (0.1) 0.000 1.2 (0.4) 0.003 1.4 (0.3) 0.000 0.7 (0.1) 0.000 1.6 (0.1) 0.000
Estonia 0.2 (0.4) 0.600 0.0 (0.5) 0.990 -0.2 (0.4) 0.692 0.7 (0.1) 0.000 1.4 (0.1) 0.000
Finland 1.5 (0.4) 0.001 0.8 (0.4) 0.084 1.7 (0.5) 0.000 0.7 (0.2) 0.000 1.9 (0.2) 0.000
Germany 1.5 (0.2) 0.000 0.8 (0.4) 0.054 1.2 (0.3) 0.001 0.7 (0.2) 0.000 1.6 (0.2) 0.000
Ireland 1.0 (0.2) 0.000 0.7 (0.5) 0.181 1.0 (0.2) 0.000 0.8 (0.2) 0.000 1.7 (0.2) 0.000
Japan c c c c c c c c c 0.8 (0.2) 0.000 1.6 (0.2) 0.000
Korea 4.8 (9.5) 0.613 c c c 4.1 (9.5) 0.668 0.5 (0.2) 0.004 1.5 (0.2) 0.000
Netherlands 1.4 (0.2) 0.000 0.4 (0.6) 0.539 1.2 (0.3) 0.000 0.9 (0.1) 0.000 1.9 (0.1) 0.000
Norway 1.6 (0.1) 0.000 0.9 (0.4) 0.019 1.2 (0.4) 0.001 0.7 (0.1) 0.000 1.8 (0.1) 0.000
Poland c c c c c c c c c 0.1 (0.1) 0.682 1.0 (0.2) 0.000
Slovak Republic 0.6 (0.6) 0.314 0.1 (0.7) 0.911 0.0 (0.8) 0.994 0.4 (0.2) 0.008 1.1 (0.2) 0.000
Sweden 1.8 (0.2) 0.000 1.2 (0.3) 0.000 1.3 (0.3) 0.000 1.1 (0.2) 0.000 2.0 (0.2) 0.000
United States 1.3 (0.2) 0.000 1.3 (0.4) 0.001 0.5 (0.3) 0.142 0.7 (0.2) 0.000 1.6 (0.2) 0.000
England (UK) 1.3 (0.2) 0.000 1.0 (0.4) 0.018 0.6 (0.3) 0.021 0.9 (0.1) 0.000 1.5 (0.2) 0.000
Average1
1.5 (0.6) 0.009 0.8 (0.1) 0.000 1.2 (0.6) 0.039 0.7 (0.0) 0.000 1.5 (0.0) 0.000
Average-222
Partners
Cyprus3
Russian Federation4

[Part 3/3]
Table B3.2
Gender
(reference women)
Participation in adult
education
and training
(reference did not
participate)
E-mail use
(reference not high/regular
use of e-mail)
Men
upper secondary
At least one parent
attained tertiary Participated High/regular use of e-mail
National entities
Australia 0.2 (0.1) 0.097 0.4 (0.1) 0.002 0.6 (0.1) 0.000 0.7 (0.1) 0.000 1.4 (0.1) 0.000
Austria 0.4 (0.1) 0.000 0.5 (0.1) 0.000 0.8 (0.2) 0.000 0.5 (0.1) 0.000 1.8 (0.2) 0.000
Canada 0.1 (0.1) 0.026 0.5 (0.1) 0.000 0.7 (0.1) 0.000 0.6 (0.1) 0.000 1.5 (0.1) 0.000
Czech Republic 0.2 (0.1) 0.111 0.7 (0.3) 0.023 1.3 (0.3) 0.000 0.5 (0.1) 0.001 1.6 (0.2) 0.000
Denmark 0.3 (0.1) 0.000 0.1 (0.1) 0.492 0.5 (0.1) 0.000 0.5 (0.1) 0.000 1.5 (0.2) 0.000
Estonia 0.2 (0.1) 0.015 0.4 (0.2) 0.005 1.0 (0.1) 0.000 0.8 (0.1) 0.000 1.5 (0.2) 0.000
Finland 0.4 (0.1) 0.000 0.4 (0.1) 0.000 1.0 (0.1) 0.000 0.4 (0.1) 0.000 1.4 (0.2) 0.000
Germany 0.3 (0.1) 0.002 0.7 (0.2) 0.002 1.1 (0.2) 0.000 0.5 (0.1) 0.000 1.6 (0.1) 0.000
Ireland 0.4 (0.1) 0.001 0.4 (0.1) 0.000 0.9 (0.1) 0.000 0.5 (0.1) 0.000 1.5 (0.2) 0.000
Japan 0.5 (0.1) 0.000 0.0 (0.1) 0.735 0.3 (0.1) 0.028 0.5 (0.1) 0.000 1.2 (0.1) 0.000
Korea 0.3 (0.1) 0.001 0.2 (0.1) 0.069 0.5 (0.1) 0.000 0.5 (0.1) 0.000 0.8 (0.1) 0.000
Netherlands 0.4 (0.1) 0.000 0.3 (0.1) 0.016 0.5 (0.1) 0.000 0.3 (0.1) 0.003 1.8 (0.2) 0.000
Norway 0.5 (0.1) 0.000 0.4 (0.1) 0.000 0.8 (0.1) 0.000 0.5 (0.1) 0.000 1.2 (0.2) 0.000
Poland 0.4 (0.1) 0.001 0.3 (0.2) 0.131 0.9 (0.2) 0.000 0.5 (0.1) 0.000 1.7 (0.2) 0.000
Slovak Republic 0.1 (0.1) 0.157 0.5 (0.1) 0.000 0.9 (0.2) 0.000 0.7 (0.1) 0.000 1.7 (0.2) 0.000
Sweden 0.3 (0.1) 0.005 0.4 (0.1) 0.000 0.7 (0.1) 0.000 0.6 (0.1) 0.000 1.6 (0.1) 0.000
United States 0.3 (0.1) 0.003 0.9 (0.2) 0.000 1.3 (0.2) 0.000 0.5 (0.1) 0.000 1.5 (0.2) 0.000
England (UK) 0.5 (0.1) 0.000 0.8 (0.1) 0.000 1.0 (0.2) 0.000 0.5 (0.1) 0.000 1.7 (0.2) 0.000
Northern Ireland (UK) 0.6 (0.1) 0.000 0.6 (0.2) 0.001 1.0 (0.2) 0.000 0.4 (0.2) 0.009 1.4 (0.2) 0.000
Average1
0.3 (0.0) 0.000 0.4 (0.0) 0.000 0.8 (0.0) 0.000 0.5 (0.0) 0.000 1.5 (0.0) 0.000
Average-222
Partners
Cyprus3
Russian Federation4

[Part 1/4]
Table B3.3
by socio-demographic characteristics, e-mail use and cognitive skills (Version 3)
Age
National entities
Australia 1.3 (0.3) 0.000 1.1 (0.2) 0.000 0.8 (0.2) 0.000 0.6 (0.2) 0.006
Austria 1.7 (0.3) 0.000 1.8 (0.2) 0.000 1.3 (0.2) 0.000 0.8 (0.2) 0.001
Canada 1.4 (0.2) 0.000 1.2 (0.1) 0.000 1.0 (0.1) 0.000 0.6 (0.1) 0.000
Czech Republic 1.3 (0.4) 0.001 1.4 (0.3) 0.000 0.7 (0.3) 0.011 0.3 (0.3) 0.401
Denmark 1.7 (0.3) 0.000 2.0 (0.2) 0.000 1.4 (0.1) 0.000 0.8 (0.2) 0.000
Estonia 2.0 (0.3) 0.000 2.0 (0.2) 0.000 1.4 (0.2) 0.000 0.7 (0.2) 0.001
Finland 2.2 (0.3) 0.000 2.2 (0.2) 0.000 1.7 (0.2) 0.000 0.9 (0.2) 0.000
Germany 1.7 (0.3) 0.000 1.7 (0.2) 0.000 1.1 (0.2) 0.000 0.6 (0.2) 0.010
Ireland 2.0 (0.3) 0.000 1.7 (0.2) 0.000 1.3 (0.2) 0.000 0.9 (0.2) 0.000
Japan 1.3 (0.2) 0.000 1.7 (0.2) 0.000 1.4 (0.2) 0.000 0.7 (0.2) 0.000
Korea 2.6 (0.3) 0.000 1.9 (0.3) 0.000 1.3 (0.2) 0.000 0.6 (0.2) 0.009
Netherlands 1.4 (0.3) 0.000 1.4 (0.2) 0.000 1.0 (0.2) 0.000 0.5 (0.2) 0.001
Norway 2.2 (0.3) 0.000 1.8 (0.2) 0.000 1.3 (0.2) 0.000 0.8 (0.2) 0.000
Poland 1.6 (0.3) 0.000 1.7 (0.3) 0.000 1.4 (0.3) 0.000 0.7 (0.3) 0.035
Slovak Republic 0.7 (0.4) 0.055 0.7 (0.2) 0.001 0.4 (0.3) 0.181 0.3 (0.3) 0.267
Sweden 1.7 (0.3) 0.000 1.5 (0.2) 0.000 1.1 (0.2) 0.000 0.6 (0.2) 0.001
United States 1.2 (0.4) 0.006 0.9 (0.2) 0.000 0.8 (0.2) 0.000 0.3 (0.2) 0.121
England (UK) 1.3 (0.3) 0.000 1.4 (0.2) 0.000 0.9 (0.2) 0.000 0.5 (0.2) 0.025
Average1
1.6 (0.1) 0.000 1.6 (0.0) 0.000 1.1 (0.0) 0.000 0.6 (0.0) 0.000
Average-222
Partners
Cyprus3
Russian Federation4

[Part 2/4]
Table B3.3
Likelihood of adults scoring at Level 2 or 3 in problem solving in technology-rich environments
Native-born
and native language
Native-born
Foreign-born
National entities
Australia 0.6 (0.2) 0.002 0.6 (0.3) 0.110 0.4 (0.2) 0.061 0.0 (0.2) 0.902 0.5 (0.1) 0.003
Austria 0.8 (0.2) 0.000 0.7 (0.4) 0.089 0.9 (0.4) 0.025 0.6 (0.2) 0.005 0.6 (0.2) 0.013
Canada 0.4 (0.2) 0.015 0.4 (0.2) 0.058 0.3 (0.2) 0.093 0.0 (0.2) 0.853 0.3 (0.2) 0.249
Denmark 1.0 (0.2) 0.000 0.7 (0.6) 0.204 0.8 (0.4) 0.043 0.3 (0.1) 0.063 0.7 (0.2) 0.000
Estonia -0.2 (0.4) 0.573 -0.3 (0.5) 0.510 -0.1 (0.5) 0.871 0.3 (0.1) 0.034 0.6 (0.2) 0.001
Finland 0.5 (0.7) 0.473 0.0 (0.7) 0.964 0.7 (0.6) 0.248 0.3 (0.2) 0.171 1.0 (0.2) 0.000
Germany 1.0 (0.3) 0.001 0.7 (0.5) 0.199 0.8 (0.4) 0.033 0.1 (0.2) 0.617 0.5 (0.3) 0.051
Ireland 0.5 (0.2) 0.027 -0.1 (0.6) 0.915 0.6 (0.3) 0.030 0.4 (0.2) 0.103 0.9 (0.2) 0.000
Japan c c c c c c c c c 0.6 (0.2) 0.001 1.0 (0.2) 0.000
Korea 4.2 (9.8) 0.670 c c c 3.7 (9.8) 0.706 0.3 (0.2) 0.278 0.9 (0.2) 0.000
Netherlands 0.6 (0.2) 0.016 0.3 (0.7) 0.696 1.1 (0.4) 0.011 0.3 (0.2) 0.064 0.8 (0.1) 0.000
Norway 0.8 (0.2) 0.000 0.5 (0.5) 0.370 0.5 (0.5) 0.322 0.5 (0.2) 0.004 1.0 (0.2) 0.000
Poland c c c c c c c c c -0.1 (0.2) 0.469 0.4 (0.2) 0.092
Slovak Republic 0.6 (0.7) 0.412 0.1 (0.9) 0.910 0.0 (0.9) 0.965 0.1 (0.2) 0.524 0.6 (0.3) 0.048
Sweden 0.8 (0.2) 0.000 0.3 (0.4) 0.343 0.6 (0.5) 0.225 0.7 (0.2) 0.001 0.9 (0.2) 0.000
United States 0.8 (0.3) 0.010 0.8 (0.5) 0.102 0.1 (0.4) 0.789 0.2 (0.2) 0.319 0.5 (0.2) 0.030
England (UK) 0.6 (0.2) 0.008 0.7 (0.5) 0.188 0.2 (0.3) 0.507 0.2 (0.2) 0.277 0.7 (0.2) 0.002
Average1
0.9 (0.6) 0.137 0.4 (0.1) 0.006 0.8 (0.6) 0.187 0.3 (0.0) 0.000 0.7 (0.1) 0.000
Average-222
Partners
Cyprus3
Russian Federation4

[Part 3/4]
Table B3.3
Gender
(reference women)
Participation in adult
education
and training
(reference did not
participate)
E-mail use
(reference not high/regular
use of e-mail)
Men
upper secondary
At least one parent
attained tertiary Participated High/regular use of e-mail
National entities
Australia 0.1 (0.1) 0.391 0.3 (0.1) 0.024 0.3 (0.1) 0.011 0.5 (0.1) 0.000 1.0 (0.2) 0.000
Austria 0.5 (0.1) 0.001 0.2 (0.1) 0.092 0.4 (0.2) 0.070 0.4 (0.1) 0.010 1.6 (0.2) 0.000
Canada 0.1 (0.1) 0.482 0.4 (0.1) 0.001 0.4 (0.1) 0.001 0.4 (0.1) 0.000 1.2 (0.1) 0.000
Czech Republic 0.2 (0.1) 0.219 0.6 (0.3) 0.067 1.0 (0.4) 0.007 0.4 (0.1) 0.015 1.3 (0.2) 0.000
Denmark 0.3 (0.1) 0.005 0.0 (0.1) 0.804 0.1 (0.1) 0.259 0.4 (0.1) 0.004 1.1 (0.2) 0.000
Estonia 0.2 (0.1) 0.054 0.4 (0.2) 0.033 0.8 (0.1) 0.000 0.6 (0.1) 0.000 1.5 (0.2) 0.000
Finland 0.5 (0.1) 0.000 0.4 (0.1) 0.003 0.6 (0.2) 0.001 0.3 (0.1) 0.038 1.1 (0.2) 0.000
Germany 0.3 (0.1) 0.005 0.5 (0.3) 0.093 0.6 (0.3) 0.043 0.3 (0.1) 0.046 1.3 (0.2) 0.000
Ireland 0.3 (0.1) 0.033 0.3 (0.1) 0.083 0.6 (0.2) 0.001 0.3 (0.2) 0.025 1.3 (0.2) 0.000
Japan 0.6 (0.1) 0.000 -0.2 (0.1) 0.253 0.1 (0.1) 0.293 0.4 (0.1) 0.000 1.1 (0.1) 0.000
Korea 0.3 (0.1) 0.027 0.1 (0.1) 0.454 0.3 (0.1) 0.019 0.4 (0.1) 0.005 0.6 (0.1) 0.000
Netherlands 0.3 (0.1) 0.001 0.1 (0.1) 0.449 0.2 (0.1) 0.112 0.2 (0.2) 0.110 1.3 (0.3) 0.000
Norway 0.4 (0.1) 0.000 0.4 (0.1) 0.014 0.5 (0.2) 0.002 0.5 (0.2) 0.003 1.1 (0.2) 0.000
Poland 0.4 (0.1) 0.002 0.2 (0.2) 0.522 0.6 (0.3) 0.038 0.4 (0.1) 0.002 1.4 (0.3) 0.000
Slovak Republic 0.2 (0.1) 0.127 0.4 (0.2) 0.042 0.5 (0.2) 0.008 0.6 (0.1) 0.000 1.7 (0.2) 0.000
Sweden 0.3 (0.1) 0.047 0.4 (0.1) 0.007 0.5 (0.1) 0.000 0.6 (0.2) 0.000 1.4 (0.2) 0.000
United States 0.2 (0.1) 0.040 0.6 (0.3) 0.016 0.6 (0.2) 0.006 0.3 (0.1) 0.025 0.9 (0.2) 0.000
England (UK) 0.5 (0.1) 0.000 0.4 (0.2) 0.015 0.4 (0.2) 0.030 0.4 (0.1) 0.006 1.4 (0.2) 0.000
Northern Ireland (UK) 0.5 (0.2) 0.003 0.5 (0.2) 0.025 0.7 (0.2) 0.008 0.3 (0.2) 0.151 1.3 (0.2) 0.000
Average1
0.3 (0.0) 0.000 0.3 (0.0) 0.000 0.5 (0.0) 0.000 0.4 (0.0) 0.000 1.3 (0.0) 0.000
Average-222
Partners
Cyprus3
Russian Federation4

[Part 4/4]
Table B3.3
Literacy levels
(reference Level 2)
Below Level 1 and Level 1 Level 3 Level 4 and Level 5
OECD ß S.E. p-value ß S.E. p-value ß S.E. p-value
National entities
Australia -4.2 (6.6) 0.529 2.0 (0.1) 0.000 3.4 (0.2) 0.000
Austria -4.6 (6.8) 0.498 2.0 (0.2) 0.000 3.4 (0.3) 0.000
Canada -3.1 (0.5) 0.000 2.1 (0.1) 0.000 3.6 (0.2) 0.000
Czech Republic -2.8 (1.4) 0.041 1.8 (0.2) 0.000 2.8 (0.3) 0.000
Denmark -3.5 (3.6) 0.332 2.1 (0.1) 0.000 4.0 (0.3) 0.000
Estonia -2.3 (0.8) 0.005 2.0 (0.2) 0.000 3.3 (0.1) 0.000
Finland -2.9 (7.2) 0.692 2.1 (0.2) 0.000 3.8 (0.2) 0.000
Germany -3.0 (0.7) 0.000 1.9 (0.1) 0.000 3.4 (0.3) 0.000
Ireland -2.9 (1.0) 0.004 1.8 (0.2) 0.000 3.1 (0.2) 0.000
Japan -6.9 (12.7) 0.590 1.8 (0.2) 0.000 2.8 (0.2) 0.000
Korea -6.6 (11.0) 0.551 2.0 (0.1) 0.000 3.3 (0.2) 0.000
Netherlands -5.2 (10.0) 0.604 2.3 (0.1) 0.000 4.1 (0.2) 0.000
Norway -2.4 (0.6) 0.000 2.1 (0.2) 0.000 3.9 (0.3) 0.000
Poland -2.1 (0.6) 0.001 1.8 (0.1) 0.000 2.5 (0.2) 0.000
Slovak Republic -2.2 (0.7) 0.003 1.9 (0.2) 0.000 3.2 (0.3) 0.000
Sweden -2.8 (3.9) 0.478 2.0 (0.2) 0.000 4.1 (0.3) 0.000
United States -4.6 (5.7) 0.416 2.2 (0.2) 0.000 4.2 (0.3) 0.000
Flanders (Belgium) -3.0 (3.6) 0.407 2.1 (0.1) 0.000 3.9 (0.2) 0.000
England (UK) -2.2 (0.6) 0.000 1.9 (0.2) 0.000 3.5 (0.2) 0.000
Northern Ireland (UK) -2.3 (0.9) 0.009 2.0 (0.3) 0.000 3.7 (0.3) 0.000
England/N. Ireland (UK) -2.2 (0.6) 0.000 1.9 (0.2) 0.000 3.5 (0.2) 0.000
Average1
-3.6 (1.3) 0.006 2.0 (0.0) 0.000 3.5 (0.1) 0.000
Average-222
m m m m m m m m m
Partners
Cyprus3
m m m m m m m m m
Russian Federation4
m m m m m m m m m

[Part 1/3]
Table B3.4 Likelihood of adults having no computer experience, by socio-demographic characteristics (Version 1)
Age
National entities
Australia -2.0 (0.9) 0.031 -2.0 (0.4) 0.000 -1.5 (0.2) 0.000 -0.8 (0.2) 0.000
Austria -4.4 (12.0) 0.719 -2.7 (0.3) 0.000 -1.7 (0.2) 0.000 -0.9 (0.1) 0.000
Canada -2.7 (0.9) 0.002 -2.1 (0.3) 0.000 -1.6 (0.2) 0.000 -0.5 (0.1) 0.000
Czech Republic -3.1 (0.6) 0.000 -2.0 (0.4) 0.000 -2.3 (0.2) 0.000 -0.6 (0.2) 0.001
Denmark -8.0 (11.7) 0.499 -1.3 (0.4) 0.002 -1.5 (0.4) 0.000 -0.9 (0.2) 0.000
Estonia -7.8 (10.3) 0.451 -3.7 (0.3) 0.000 -1.8 (0.1) 0.000 -0.8 (0.1) 0.000
Finland -18.4 (17.2) 0.289 -17.8 (17.2) 0.305 -3.2 (0.9) 0.000 -0.6 (0.2) 0.021
Germany -3.7 (0.9) 0.000 -3.2 (0.5) 0.000 -1.7 (0.2) 0.000 -0.8 (0.1) 0.000
Ireland -2.5 (0.5) 0.000 -2.3 (0.2) 0.000 -1.3 (0.2) 0.000 -0.6 (0.2) 0.000
Japan -2.9 (0.5) 0.000 -2.6 (0.3) 0.000 -2.0 (0.2) 0.000 -0.9 (0.1) 0.000
Korea -4.1 (0.6) 0.000 -3.5 (0.3) 0.000 -2.2 (0.2) 0.000 -0.8 (0.1) 0.000
Netherlands -15.7 (16.9) 0.356 -2.1 (0.5) 0.000 -1.5 (0.3) 0.000 -0.7 (0.2) 0.000
Norway -2.4 (0.8) 0.005 -3.3 (14.0) 0.817 -1.9 (0.5) 0.000 -1.1 (0.3) 0.003
Poland -3.7 (0.3) 0.000 -2.3 (0.2) 0.000 -1.3 (0.2) 0.000 -0.4 (0.1) 0.002
Slovak Republic -2.2 (0.2) 0.000 -2.0 (0.1) 0.000 -1.1 (0.1) 0.000 -0.6 (0.1) 0.000
Sweden -1.2 (0.7) 0.078 -2.1 (0.6) 0.001 -4.5 (0.8) 0.000 -1.5 (0.4) 0.000
United States -2.6 (0.5) 0.000 -2.1 (0.3) 0.000 -1.0 (0.3) 0.000 -0.5 (0.2) 0.009
Flanders (Belgium) -3.3 (0.6) 0.000 -1.5 (0.3) 0.000 -1.4 (0.2) 0.000 -0.7 (0.1) 0.000
England (UK) -2.9 (1.6) 0.082 -3.5 (0.5) 0.000 -1.7 (0.3) 0.000 -0.5 (0.2) 0.026
Northern Ireland (UK) -1.9 (0.5) 0.000 -1.7 (0.3) 0.000 -1.0 (0.2) 0.000 -0.3 (0.2) 0.125
England/N. Ireland (UK) -2.7 (1.1) 0.013 -3.2 (0.4) 0.000 -1.6 (0.3) 0.000 -0.4 (0.2) 0.027
Average1
-4.9 (1.6) 0.003 -3.3 (1.2) 0.006 -1.9 (0.1) 0.000 -0.7 (0.0) 0.000
Average-222
Partners
Cyprus3
Russian Federation4

[Part 2/3]
Native-born
and native language
Native-born
Foreign-born
National entities
Australia -1.2 (0.2) 0.000 -1.2 (0.7) 0.102 -1.2 (0.3) 0.000 -0.7 (0.2) 0.000 -2.1 (0.3) 0.000
Austria -0.7 (0.2) 0.001 -2.1 (1.2) 0.074 -1.0 (0.5) 0.060 -1.2 (0.1) 0.000 -2.6 (0.3) 0.000
Canada -1.0 (0.1) 0.000 -1.5 (0.3) 0.000 -0.8 (0.2) 0.001 -1.3 (0.1) 0.000 -2.5 (0.2) 0.000
Czech Republic 0.3 (0.4) 0.484 c c c 1.1 (0.5) 0.050 -1.1 (0.2) 0.000 -4.0 (0.5) 0.000
Denmark -1.1 (0.2) 0.000 -0.3 (12.1) 0.978 -0.3 (1.0) 0.791 -1.3 (0.2) 0.000 -4.6 (1.1) 0.000
Estonia -0.5 (0.3) 0.078 -0.3 (0.5) 0.522 -0.3 (0.3) 0.295 -1.1 (0.1) 0.000 -2.6 (0.2) 0.000
Finland -0.5 (1.1) 0.624 0.3 (1.2) 0.819 0.7 (1.5) 0.627 -1.1 (0.2) 0.000 -4.3 (0.8) 0.000
Germany -0.6 (0.2) 0.023 -0.8 (0.7) 0.266 -0.2 (0.4) 0.635 -1.1 (0.2) 0.000 -2.1 (0.3) 0.000
Ireland 0.8 (0.4) 0.042 1.3 (0.5) 0.013 -0.1 (0.5) 0.906 -1.6 (0.1) 0.000 -3.0 (0.3) 0.000
Japan c c c c c c c c c -1.6 (0.1) 0.000 -2.5 (0.2) 0.000
Korea -2.1 (0.7) 0.002 c c c -0.9 (0.8) 0.248 -1.6 (0.1) 0.000 -3.3 (0.2) 0.000
Netherlands -1.4 (0.3) 0.000 0.3 (1.1) 0.761 -0.7 (0.5) 0.172 -1.7 (0.3) 0.000 -2.5 (0.5) 0.000
Norway -1.4 (0.4) 0.001 -15.8 (20.4) 0.442 -15.1 (20.2) 0.459 -1.4 (0.3) 0.000 -3.0 (0.6) 0.000
Poland c c c c c c c c c -1.4 (0.1) 0.000 -3.8 (0.3) 0.000
Slovak Republic -1.0 (0.5) 0.063 -1.0 (0.5) 0.067 -0.3 (0.6) 0.666 -1.8 (0.1) 0.000 -4.7 (0.3) 0.000
Sweden -1.8 (0.4) 0.000 -15.6 (21.6) 0.472 -1.2 (13.2) 0.926 -0.9 (0.3) 0.003 -2.4 (1.1) 0.032
United States -1.5 (0.3) 0.000 -0.6 (0.5) 0.192 -1.2 (0.6) 0.063 -1.8 (0.2) 0.000 -3.0 (0.2) 0.000
Flanders (Belgium) -0.9 (0.3) 0.001 -1.3 (0.4) 0.004 -1.6 (0.7) 0.017 -0.9 (0.1) 0.000 -2.7 (0.4) 0.000
England (UK) -1.0 (0.3) 0.004 -0.9 (13.0) 0.945 -0.5 (0.4) 0.190 -1.1 (0.2) 0.000 -1.9 (0.4) 0.000
Northern Ireland (UK) 0.2 (0.7) 0.774 c c c 0.3 (0.7) 0.717 -1.2 (0.2) 0.000 -2.9 (0.5) 0.000
England/N. Ireland (UK) -0.9 (0.3) 0.006 -0.8 (1.8) 0.643 -0.5 (0.4) 0.202 -1.1 (0.2) 0.000 -2.0 (0.3) 0.000
Average1
-0.9 (0.1) 0.000 -2.6 (2.1) 0.221 -1.4 (1.4) 0.332 -1.3 (0.0) 0.000 -3.0 (0.1) 0.000
Average-222
Partners
Cyprus3
Russian Federation4

[Part 3/3]
Gender
(reference women)
and training
Men
upper secondary
At least one parent
National entities
Australia -1.0 (0.3) 0.002 -1.0 (0.3) 0.002 -0.6 (0.3) 0.049 -1.6 (0.3) 0.000
Austria -0.9 (0.2) 0.000 -0.9 (0.2) 0.000 -1.7 (0.4) 0.000 -1.4 (0.2) 0.000
Canada -0.5 (0.1) 0.000 -0.5 (0.1) 0.000 -1.1 (0.2) 0.000 -1.2 (0.1) 0.000
Czech Republic -0.5 (0.2) 0.018 -0.5 (0.2) 0.018 -0.7 (0.4) 0.094 -1.1 (0.2) 0.000
Denmark -0.1 (0.2) 0.552 -0.1 (0.2) 0.552 -1.2 (0.4) 0.003 -1.6 (0.2) 0.000
Estonia -0.6 (0.1) 0.000 -0.6 (0.1) 0.000 -0.8 (0.2) 0.000 -1.8 (0.1) 0.000
Finland -0.2 (0.3) 0.501 -0.2 (0.3) 0.501 -1.8 (0.7) 0.015 -1.5 (0.3) 0.000
Germany -0.6 (0.2) 0.004 -0.6 (0.2) 0.004 -1.3 (0.2) 0.000 -1.4 (0.2) 0.000
Ireland -0.9 (0.2) 0.000 -0.9 (0.2) 0.000 -1.4 (0.4) 0.001 -1.1 (0.1) 0.000
Japan -0.1 (0.2) 0.582 -0.1 (0.2) 0.582 -0.6 (0.2) 0.015 -1.4 (0.2) 0.000
Korea -0.2 (0.1) 0.143 -0.2 (0.1) 0.143 -0.5 (0.2) 0.029 -0.9 (0.1) 0.000
Netherlands -1.0 (0.3) 0.006 -1.0 (0.3) 0.006 -0.4 (0.5) 0.420 -1.4 (0.3) 0.000
Norway -0.1 (0.3) 0.863 -0.1 (0.3) 0.863 0.1 (0.4) 0.765 -1.2 (0.3) 0.000
Poland -1.0 (0.1) 0.000 -1.0 (0.1) 0.000 -1.6 (0.3) 0.000 -1.2 (0.2) 0.000
Slovak Republic -0.9 (0.1) 0.000 -0.9 (0.1) 0.000 -2.0 (0.3) 0.000 -1.0 (0.1) 0.000
Sweden 0.1 (0.5) 0.777 0.1 (0.5) 0.777 -1.1 (1.7) 0.549 -2.1 (0.4) 0.000
United States -0.6 (0.2) 0.004 -0.6 (0.2) 0.004 -1.2 (0.3) 0.000 -1.4 (0.2) 0.000
England (UK) -0.3 (0.2) 0.167 -0.3 (0.2) 0.167 -0.5 (0.5) 0.333 -1.5 (0.3) 0.000
England/N. Ireland (UK) 0.0 (0.2) 0.902 -0.4 (0.2) 0.078 -0.5 (0.4) 0.251 -1.5 (0.2) 0.000
Average1
-0.5 (0.1) 0.000 -0.6 (0.1) 0.000 -1.0 (0.1) 0.000 -1.4 (0.1) 0.000
Average-222
Partners
Cyprus3
Russian Federation4

[Part 1/4]
Table B3.5
Likelihood of adults having no computer experience, by socio-demographic characteristics and cognitive
skills (Version 3)
Age
National entities
Australia -2.2 (0.9) 0.016 -2.0 (0.4) 0.000 -1.4 (0.2) 0.000 -0.7 (0.2) 0.001
Austria -4.3 (12.0) 0.719 -2.6 (0.3) 0.000 -1.7 (0.2) 0.000 -0.8 (0.1) 0.000
Canada -2.8 (0.9) 0.002 -2.1 (0.3) 0.000 -1.6 (0.2) 0.000 -0.5 (0.1) 0.000
Czech Republic -3.1 (0.6) 0.000 -2.0 (0.4) 0.000 -2.3 (0.2) 0.000 -0.7 (0.2) 0.000
Denmark -7.9 (12.5) 0.528 -1.1 (0.4) 0.007 -1.3 (0.4) 0.001 -0.8 (0.2) 0.003
Estonia -7.9 (10.2) 0.440 -3.7 (0.3) 0.000 -1.8 (0.1) 0.000 -0.8 (0.1) 0.000
Finland -17.8 (18.7) 0.344 -17.4 (18.7) 0.355 -3.1 (0.9) 0.001 -0.5 (0.3) 0.071
Germany -3.6 (0.9) 0.000 -3.1 (0.5) 0.000 -1.7 (0.3) 0.000 -0.7 (0.1) 0.000
Ireland -2.5 (0.5) 0.000 -2.3 (0.2) 0.000 -1.3 (0.2) 0.000 -0.6 (0.2) 0.000
Japan -2.8 (0.5) 0.000 -2.4 (0.3) 0.000 -1.8 (0.2) 0.000 -0.7 (0.2) 0.000
Korea -4.1 (0.6) 0.000 -3.5 (0.3) 0.000 -2.2 (0.2) 0.000 -0.8 (0.1) 0.000
Netherlands -16.3 (19.0) 0.393 -2.0 (0.5) 0.001 -1.4 (0.3) 0.000 -0.6 (0.2) 0.002
Norway -2.5 (0.8) 0.004 -3.2 (14.0) 0.819 -1.8 (0.5) 0.001 -1.0 (0.4) 0.008
Poland -3.7 (0.3) 0.000 -2.3 (0.2) 0.000 -1.3 (0.2) 0.000 -0.4 (0.1) 0.004
Slovak Republic -2.4 (0.2) 0.000 -2.1 (0.1) 0.000 -1.2 (0.1) 0.000 -0.6 (0.1) 0.000
Sweden -1.0 (0.6) 0.142 -2.0 (0.6) 0.003 -4.4 (0.8) 0.000 -1.4 (0.4) 0.001
United States -2.4 (0.5) 0.000 -2.1 (0.3) 0.000 -1.0 (0.3) 0.000 -0.6 (0.2) 0.010
England (UK) -3.1 (1.6) 0.064 -3.6 (0.5) 0.000 -1.7 (0.3) 0.000 -0.5 (0.2) 0.017
England/N. Ireland (UK) -2.9 (1.1) 0.008 -3.3 (0.4) 0.000 -1.6 (0.3) 0.000 -0.5 (0.2) 0.018
Average1
-4.9 (1.8) 0.005 -3.2 (1.2) 0.010 -1.8 (0.1) 0.000 -0.7 (0.0) 0.000
Average-222
Partners
Cyprus3
Russian Federation4

[Part 2/4]
Table B3.5
skills (Version 3)
Native-born
and native language
Native-born
Foreign-born
National entities
Australia -0.6 (0.2) 0.003 -1.0 (0.7) 0.175 -0.8 (0.3) 0.029 -0.4 (0.2) 0.036 -1.4 (0.3) 0.000
Austria -0.5 (0.2) 0.032 -2.0 (1.2) 0.086 -0.8 (0.5) 0.120 -1.1 (0.1) 0.000 -2.4 (0.3) 0.000
Canada -0.8 (0.1) 0.000 -1.2 (0.3) 0.000 -0.7 (0.2) 0.005 -1.0 (0.1) 0.000 -2.1 (0.2) 0.000
Czech Republic 0.3 (0.5) 0.485 c c c 1.0 (0.5) 0.060 -1.0 (0.2) 0.000 -3.6 (0.5) 0.000
Denmark -0.7 (0.2) 0.003 0.1 (12.1) 0.992 0.0 (1.0) 0.984 -1.1 (0.2) 0.000 -4.1 (1.1) 0.000
Estonia -0.4 (0.3) 0.166 -0.2 (0.4) 0.621 -0.3 (0.3) 0.295 -1.0 (0.1) 0.000 -2.4 (0.2) 0.000
Finland -0.2 (1.1) 0.864 0.5 (1.2) 0.680 1.3 (1.4) 0.370 -1.0 (0.2) 0.000 -3.9 (0.8) 0.000
Germany -0.4 (0.3) 0.089 -0.7 (0.7) 0.315 -0.1 (0.4) 0.800 -0.9 (0.2) 0.000 -1.8 (0.3) 0.000
Ireland 1.1 (0.4) 0.012 1.6 (0.5) 0.003 0.2 (0.5) 0.742 -1.4 (0.1) 0.000 -2.8 (0.3) 0.000
Japan c c c c c c c c c -1.4 (0.2) 0.000 -2.1 (0.2) 0.000
Korea -1.8 (0.7) 0.007 c c c -0.8 (0.8) 0.326 -1.4 (0.1) 0.000 -3.0 (0.2) 0.000
Netherlands -0.9 (0.3) 0.001 0.4 (1.1) 0.756 -0.5 (0.5) 0.300 -1.4 (0.3) 0.000 -1.9 (0.6) 0.001
Norway -1.1 (0.4) 0.007 -15.9 (22.3) 0.478 -15.0 (22.1) 0.501 -1.3 (0.3) 0.000 -2.8 (0.6) 0.000
Poland c c c c c c c c c -1.3 (0.1) 0.000 -3.5 (0.3) 0.000
Slovak Republic -1.0 (0.5) 0.070 -1.1 (0.6) 0.060 -0.3 (0.7) 0.704 -1.7 (0.1) 0.000 -4.5 (0.4) 0.000
Sweden -1.2 (0.5) 0.011 -15.8 (23.2) 0.496 -0.8 (13.6) 0.954 -0.6 (0.3) 0.037 -1.9 (1.2) 0.116
United States -1.2 (0.3) 0.000 -0.3 (0.4) 0.459 -0.9 (0.6) 0.168 -1.5 (0.2) 0.000 -2.2 (0.3) 0.000
Flanders (Belgium) -0.5 (0.3) 0.036 -1.1 (0.5) 0.014 -1.3 (0.7) 0.050 -0.7 (0.1) 0.000 -2.3 (0.4) 0.000
England (UK) -0.5 (0.3) 0.100 -0.7 (13.0) 0.959 -0.1 (0.4) 0.731 -0.8 (0.2) 0.000 -1.5 (0.4) 0.000
Northern Ireland (UK) 0.2 (0.7) 0.715 c c c 0.3 (0.7) 0.670 -1.1 (0.2) 0.000 -2.7 (0.5) 0.000
England/N. Ireland (UK) -0.5 (0.3) 0.126 -0.6 (1.8) 0.726 -0.1 (0.4) 0.735 -0.8 (0.2) 0.000 -1.5 (0.3) 0.000
Average1
-0.6 (0.1) 0.000 -2.5 (2.3) 0.278 -1.2 (1.5) 0.449 -1.1 (0.0) 0.000 -2.6 (0.1) 0.000
Average-222
Partners
Cyprus3
Russian Federation4

[Part 3/4]
Table B3.5
skills (Version 3)
Gender
(reference women)
and training
Men
upper secondary
At least one parent
National entities
Australia -0.9 (0.3) 0.005 -0.9 (0.3) 0.005 -0.5 (0.3) 0.102 -1.4 (0.3) 0.000
Austria -0.9 (0.2) 0.000 -0.9 (0.2) 0.000 -1.6 (0.4) 0.000 -1.3 (0.2) 0.000
Canada -0.5 (0.1) 0.001 -0.5 (0.1) 0.001 -1.0 (0.2) 0.000 -1.1 (0.1) 0.000
Czech Republic -0.5 (0.2) 0.031 -0.5 (0.2) 0.031 -0.6 (0.4) 0.114 -1.1 (0.2) 0.000
Denmark -0.1 (0.2) 0.710 -0.1 (0.2) 0.710 -1.0 (0.4) 0.019 -1.4 (0.2) 0.000
Estonia -0.6 (0.1) 0.000 -0.6 (0.1) 0.000 -0.8 (0.2) 0.000 -1.7 (0.1) 0.000
Finland -0.1 (0.3) 0.726 -0.1 (0.3) 0.726 -1.7 (0.7) 0.019 -1.4 (0.3) 0.000
Germany -0.6 (0.2) 0.009 -0.6 (0.2) 0.009 -1.2 (0.3) 0.000 -1.3 (0.2) 0.000
Ireland -0.9 (0.2) 0.000 -0.9 (0.2) 0.000 -1.3 (0.4) 0.002 -1.1 (0.1) 0.000
Japan 0.0 (0.2) 0.902 0.0 (0.2) 0.902 -0.5 (0.3) 0.075 -1.3 (0.2) 0.000
Korea -0.2 (0.1) 0.191 -0.2 (0.1) 0.191 -0.5 (0.2) 0.043 -0.8 (0.1) 0.000
Netherlands -0.8 (0.4) 0.019 -0.8 (0.4) 0.019 -0.2 (0.6) 0.743 -1.3 (0.3) 0.000
Norway 0.1 (0.3) 0.835 0.1 (0.3) 0.835 0.3 (0.4) 0.477 -1.1 (0.3) 0.000
Poland -0.9 (0.1) 0.000 -0.9 (0.1) 0.000 -1.6 (0.3) 0.000 -1.2 (0.2) 0.000
Slovak Republic -0.8 (0.1) 0.000 -0.8 (0.1) 0.000 -1.9 (0.3) 0.000 -1.0 (0.1) 0.000
Sweden 0.4 (0.5) 0.428 0.4 (0.5) 0.428 -0.8 (1.8) 0.664 -1.9 (0.4) 0.000
United States -0.4 (0.2) 0.050 -0.4 (0.2) 0.050 -0.9 (0.3) 0.012 -1.4 (0.2) 0.000
England (UK) -0.2 (0.2) 0.499 -0.2 (0.2) 0.499 -0.2 (0.4) 0.692 -1.4 (0.3) 0.000
England/N. Ireland (UK) 0.0 (0.2) 0.899 -0.2 (0.2) 0.299 -0.2 (0.4) 0.549 -1.4 (0.2) 0.000
Average1
-0.5 (0.1) 0.000 -0.5 (0.1) 0.000 -0.9 (0.1) 0.000 -1.3 (0.1) 0.000
Average-222
Partners
Cyprus3
Russian Federation4

[Part 4/4]
Table B3.5
skills (Version 3)
Literacy levels
(reference Level 2)
Below Level 1 and Level 1 Level 3 Level 4 and Level 5
OECD ß S.E. p-value ß S.E. p-value ß S.E. p-value
National entities
Australia 1.2 (0.2) 0.000 -0.9 (0.3) 0.002 -2.0 (3.7) 0.583
Austria 0.5 (0.2) 0.038 -0.3 (0.3) 0.283 -2.5 (8.8) 0.780
Canada 0.5 (0.1) 0.000 -0.6 (0.2) 0.009 -1.0 (0.6) 0.126
Czech Republic 0.3 (0.3) 0.327 -0.5 (0.2) 0.039 -1.9 (1.1) 0.083
Denmark 0.9 (0.3) 0.001 -1.0 (0.6) 0.145 -9.4 (25.3) 0.712
Estonia 0.4 (0.1) 0.005 -0.4 (0.1) 0.008 -0.7 (0.3) 0.031
Finland 0.7 (0.3) 0.009 -0.3 (0.3) 0.276 -5.4 (13.3) 0.687
Germany 0.4 (0.2) 0.047 -0.3 (0.3) 0.284 -1.0 (0.8) 0.212
Ireland 0.5 (0.2) 0.008 -0.2 (0.2) 0.366 -1.1 (1.0) 0.277
Japan 0.9 (0.2) 0.000 -0.6 (0.2) 0.001 -1.2 (0.3) 0.000
Korea 0.7 (0.2) 0.000 -0.2 (0.2) 0.250 -0.2 (0.5) 0.770
Netherlands 0.8 (0.3) 0.008 -0.8 (0.4) 0.061 -2.7 (11.0) 0.807
Norway 0.8 (0.3) 0.025 -0.3 (0.5) 0.578 -3.7 (13.9) 0.791
Poland 0.5 (0.2) 0.001 -0.3 (0.2) 0.063 -0.9 (0.6) 0.148
Slovak Republic 0.7 (0.2) 0.000 -0.3 (0.1) 0.019 -0.3 (0.4) 0.443
Sweden 0.9 (0.5) 0.094 -1.8 (6.3) 0.776 -14.8 (23.1) 0.524
United States 1.0 (0.2) 0.000 -1.0 (0.6) 0.065 -12.8 (14.0) 0.363
Flanders (Belgium) 0.6 (0.2) 0.003 -0.5 (0.2) 0.074 -2.5 (7.1) 0.730
England (UK) 0.7 (0.2) 0.001 -0.7 (0.3) 0.035 -2.1 (5.7) 0.708
Northern Ireland (UK) 0.0 (0.2) 0.995 -0.4 (0.3) 0.197 -0.5 (1.2) 0.659
England/N. Ireland (UK) 0.7 (0.2) 0.001 -0.7 (0.3) 0.025 -2.0 (1.2) 0.092
Average1
0.7 (0.1) 0.000 -0.6 (0.3) 0.091 -3.5 (2.4) 0.141
Average-222
m m m m m m m m m
Partners
Cyprus3
m m m m m m m m m
Russian Federation4
m m m m m m m m m

[Part 1/1]
Table B3.6
no computer experience, by participation in adult education and training (formal and non-formal)
Did not participate in adult education and training Did participate in adult education and training
OECD % S.E. % S.E. % S.E. % S.E.
National entities
Australia 9.0 (0.7) 21.3 (1.4) 0.9 0.2 47.9 (1.3)
Austria 18.8 (1.0) 18.6 (1.0) 2.4 0.4 42.9 (1.3)
Canada 10.4 (0.4) 19.7 (0.8) 1.4 0.1 44.3 (0.8)
Czech Republic 19.2 (1.1) 19.8 (1.5) 3.7 0.6 39.7 (1.6)
Denmark 7.0 (0.6) 19.8 (1.1) 0.6 0.1 44.3 (1.0)
Estonia 21.8 (0.7) 11.0 (0.8) 2.0 0.2 34.2 (1.1)
Finland 10.1 (0.8) 20.0 (1.1) 0.8 0.2 47.6 (1.1)
France 16.9 (0.6) m m 3.0 0.3 m m
Germany 16.6 (1.1) 19.9 (1.1) 2.3 0.4 44.9 (1.4)
Ireland 19.3 (0.8) 12.4 (0.9) 4.1 0.5 32.4 (1.3)
Italy 34.0 (1.0) m m 6.3 1.0 m m
Japan 17.0 (0.8) 24.3 (0.9) 3.1 0.4 45.7 (1.3)
Korea 28.9 (0.9) 13.9 (0.9) 6.9 0.4 35.6 (1.3)
Netherlands 8.2 (0.8) 23.5 (1.3) 0.8 0.2 48.0 (1.2)
Norway 4.1 (0.5) 23.1 (1.4) 0.6 0.1 47.9 (1.0)
Poland 31.8 (0.9) 7.8 (0.6) 4.9 0.6 28.9 (1.7)
Slovak Republic 33.7 (1.0) 14.0 (0.8) 7.2 0.8 39.8 (1.6)
Spain 29.7 (0.9) m m 5.4 0.5 m m
Sweden 4.6 (0.7) 23.1 (1.5) c c 50.4 (1.2)
United States 13.1 (1.0) 17.3 (1.1) 1.6 0.3 40.1 (1.5)
Flanders (Belgium) 15.5 (0.7) 20.9 (1.1) 1.6 0.3 45.1 (1.4)
England (UK) 8.9 (0.7) 20.7 (1.1) 1.2 0.3 43.3 (1.3)
Northern Ireland (UK) 19.2 (1.1) 15.2 (1.4) 3.7 0.6 37.4 (2.0)
England/N. Ireland (UK) 9.3 (0.7) 20.5 (1.1) 1.3 0.3 43.1 (1.2)
Average1
15.7 (0.2) 18.5 (0.3) 2.6 (0.1) 42.3 (0.3)
Average-222
17.2 (0.2) m m 2.9 (0.1) m m
Partners
Cyprus3
36.2 (0.9) m m 7.8 0.8 m m
Russian Federation4
24.9 (2.3) 21.3 (2.1) 5.1 1.1 33.1 (3.1)

[Part 1/1]
Table B3.7
Percentage of adults scoringe at Level 2 or 3 in problem solving in technology-rich environments or
have no computer experience, by parents’ educational attainment
Neither parent attained upper secondary At least one parent attained upper secondary At least one parent attained tertiary
No computer
No computer
No computer
National entities
Australia 6.8 (0.7) 26.7 (1.3) 1.2 (0.3) 45.2 (2.1) 0.9 0.3 56.7 (1.8)
Austria 23.8 (1.3) 13.8 (1.1) 5.2 (0.5) 36.6 (1.2) 1.6 0.5 52.1 (2.2)
Canada 11.9 (0.6) 17.0 (1.0) 2.7 (0.3) 37.8 (1.1) 0.7 0.1 50.9 (0.9)
Czech Republic 29.3 (3.5) 7.9 (1.8) 8.9 (0.6) 32.4 (1.3) 2.8 0.9 59.7 (3.2)
Denmark 5.2 (0.5) 23.2 (1.2) 2.1 (0.4) 36.5 (1.3) 0.3 0.1 56.4 (1.3)
Estonia 24.4 (1.1) 7.4 (0.8) 5.4 (0.4) 26.9 (1.3) 2.4 0.3 46.2 (1.4)
Finland 7.2 (0.6) 20.7 (0.9) 1.4 (0.3) 50.0 (1.4) c c 67.8 (2.0)
France 19.5 (0.7) m m 3.5 (0.4) m m 1.2 0.3 m m
Germany 25.4 (2.5) 9.4 (1.7) 7.8 (0.8) 33.8 (1.2) 2.1 0.4 53.0 (1.3)
Ireland 17.8 (0.7) 13.3 (0.9) 2.7 (0.4) 31.8 (1.8) 0.6 0.2 47.8 (1.9)
Italy 32.8 (1.1) m m 3.5 (0.8) m m c c m m
Japan 22.0 (1.6) 17.7 (1.4) 7.9 (0.7) 32.9 (1.2) 2.4 0.4 52.3 (1.4)
Korea 26.0 (0.7) 16.0 (0.9) 5.3 (0.5) 41.2 (1.6) 2.3 0.4 54.3 (1.9)
Netherlands 5.1 (0.4) 29.5 (1.2) 0.7 (0.2) 49.5 (1.6) 0.5 0.2 63.5 (1.6)
Norway 3.6 (0.6) 19.9 (1.2) 1.2 (0.3) 41.9 (1.3) 0.6 0.2 59.6 (1.5)
Poland 48.2 (1.3) 3.9 (0.7) 9.3 (0.6) 20.7 (1.0) 2.3 0.7 45.2 (2.4)
Slovak Republic 51.4 (1.4) 7.7 (0.7) 12.3 (0.6) 29.2 (1.1) 1.6 0.4 50.6 (2.5)
Spain 21.9 (0.6) m m 4.2 (0.8) m m 1.6 0.5 m m
Sweden 3.2 (0.5) 24.8 (1.1) 1.0 (0.4) 50.9 (1.7) c c 62.6 (1.4)
United States 18.3 (2.0) 8.1 (1.4) 3.3 (0.5) 31.2 (1.7) 0.9 0.2 47.8 (1.8)
Flanders (Belgium) 16.3 (0.8) 17.0 (1.3) 2.2 (0.3) 42.6 (1.5) 0.7 0.3 61.3 (1.5)
England (UK) 9.5 (0.9) 15.6 (1.4) 2.6 (0.5) 43.5 (1.5) 1.2 0.5 57.6 (2.2)
Northern Ireland (UK) 20.9 (1.4) 12.4 (1.3) 4.4 (0.6) 36.3 (2.0) 1.2 0.5 57.0 (3.6)
England/N. Ireland (UK) 10.1 (0.8) 15.4 (1.4) 2.7 (0.5) 43.2 (1.5) 1.2 0.4 57.6 (2.2)
Average1
18.7 (0.3) 15.8 (0.3) 4.4 (0.1) 37.6 (0.3) 1.4 (0.1) 55.0 (0.4)
Average-222
19.5 (0.3) m m 4.3 (0.1) m m 1.4 (0.1) m m
Partners
Cyprus3
35.3 (0.9) m m 6.2 (0.9) m m 2.4 0.8 m m
Russian Federation4
40.5 (3.0) 11.4 (2.3) 14.5 (1.3) 26.7 (2.8) 4.1 1.0 36.0 (3.4)

[Part 1/1]
Table B3.8
no computer experience, by frequency of e-mail use
Low frequency of e-mail use (less than monthly or no use) High frequency of e-mail use (at least monthly use)
OECD % S.E. % S.E. % S.E. % S.E.
National entities
Australia 17.9 (1.2) 10.6 (1.2) a a 46.9 (1.2)
Austria 32.5 (1.5) 6.2 (0.8) a a 44.7 (1.1)
Canada 23.1 (0.8) 8.4 (0.9) a a 43.9 (0.7)
Czech Republic 39.0 (1.9) 6.7 (1.3) a a 43.0 (1.4)
Denmark 17.7 (1.3) 7.7 (1.3) a a 43.8 (0.8)
Estonia 40.6 (1.1) 2.9 (0.5) a a 35.8 (0.9)
Finland 19.4 (1.4) 7.4 (1.0) a a 49.2 (0.9)
France 37.4 (1.0) m m a a m m
Germany 31.0 (1.9) 7.6 (0.9) a a 46.7 (1.1)
Ireland 30.0 (1.2) 5.2 (0.6) a a 35.7 (1.2)
Italy 53.2 (1.4) m m a a m m
Japan 24.8 (1.1) 15.2 (1.0) a a 49.2 (1.3)
Korea 36.8 (0.9) 11.3 (0.9) a a 44.6 (1.2)
Netherlands 28.3 (2.0) 5.1 (1.1) a a 47.0 (0.8)
Norway 12.4 (1.3) 10.2 (1.3) a a 46.7 (0.9)
Poland 46.0 (1.1) 2.3 (0.5) a a 31.6 (1.2)
Slovak Republic 57.2 (1.4) 4.6 (0.7) a a 39.0 (1.0)
Spain 43.5 (1.1) m m a a m m
Sweden 10.1 (1.4) 9.4 (1.1) a a 50.4 (0.8)
United States 21.3 (1.6) 7.0 (1.1) a a 41.4 (1.3)
Flanders (Belgium) 40.2 (1.5) 4.5 (0.9) a a 44.1 (1.0)
England (UK) 19.2 (1.2) 7.2 (1.1) a a 43.4 (1.1)
Northern Ireland (UK) 29.4 (1.4) 7.4 (1.2) a a 41.2 (1.7)
England/N. Ireland (UK) 19.7 (1.2) 7.2 (1.1) a a 43.3 (1.0)
Average1
28.8 (0.3) 7.3 (0.2) a a 43.5 (0.2)
Average-222
31.0 (0.3) m m a a m m
Partners
Cyprus3
44.3 (1.1) m m a a m m
Russian Federation4
32.5 (3.8) 12.3 (1.5) a a 43.5 (2.9)

[Part 1/1]
Table B4.1
Percentage of adults scoring at Level 2 or 3 in problem solving in technology-rich environments or having
no computer experience, by occupation type
Skilled occupations
Semi-skilled white-collar
occupations
Semi-skilled blue-collar
occupations Elementary occupations
No computer
No computer
No computer
No computer
National entities
Australia 0.5 (0.1) 55.6 (1.4) 1.4 (0.3) 37.7 (2.0) 7.2 (1.0) 22.1 (2.0) 4.9 (0.9) 25.4 (3.4)
Austria 1.2 (0.3) 49.5 (1.5) 3.4 (0.5) 31.3 (1.6) 17.8 (1.3) 20.0 (1.6) 27.2 (2.9) 11.8 (2.0)
Canada 1.0 (0.2) 49.2 (0.9) 3.4 (0.4) 34.0 (1.2) 8.7 (0.7) 20.7 (1.3) 9.4 (1.0) 25.0 (1.8)
Czech Republic 1.0 (0.2) 50.2 (2.3) 4.2 (0.9) 33.1 (2.6) 14.8 (1.5) 19.3 (2.0) 21.4 (3.1) 19.3 (2.9)
Denmark 0.1 (0.1) 53.7 (1.0) 1.2 (0.3) 37.5 (1.4) 4.3 (0.6) 23.9 (1.7) 3.6 (0.7) 27.9 (2.4)
Estonia 0.6 (0.1) 42.0 (1.2) 5.1 (0.6) 26.7 (1.5) 14.6 (0.9) 12.5 (1.0) 19.3 (1.4) 18.3 (1.9)
Finland 0.1 (0.1) 57.9 (1.2) 1.4 (0.4) 40.5 (1.6) 5.3 (0.7) 26.4 (1.8) 5.0 (1.2) 33.4 (2.3)
France 2.0 (0.3) m m 5.3 (0.6) m m 17.0 (1.0) m m 23.4 (1.4) m m
Germany 1.4 (0.4) 54.8 (1.8) 5.1 (0.6) 34.3 (1.5) 10.3 (1.2) 22.0 (1.8) 20.4 (2.5) 17.4 (2.3)
Ireland 1.9 (0.3) 40.7 (1.5) 5.9 (0.7) 25.8 (1.7) 15.5 (1.4) 14.3 (1.5) 15.0 (1.7) 13.8 (2.1)
Italy 3.7 (0.7) m m 14.9 (1.5) m m 31.7 (2.2) m m 44.7 (2.8) m m
Japan 1.7 (0.3) 51.9 (1.7) 6.6 (0.7) 34.1 (1.4) 17.7 (1.3) 23.7 (1.8) 22.5 (3.2) 18.8 (2.7)
Korea 2.7 (0.5) 44.8 (1.9) 9.5 (0.7) 32.0 (1.3) 26.9 (1.3) 15.9 (1.3) 33.9 (2.1) 16.0 (1.9)
Netherlands 0.2 (0.1) 57.2 (1.2) 0.7 (0.2) 40.7 (1.6) 6.4 (1.0) 24.5 (2.4) 7.8 (1.4) 26.9 (2.7)
Norway 0.3 (0.1) 57.8 (1.5) 0.8 (0.3) 37.1 (1.5) 2.3 (0.6) 28.2 (1.9) 2.7 (1.2) 22.9 (3.3)
Poland 2.7 (0.5) 33.4 (1.7) 8.5 (0.9) 19.0 (1.5) 28.5 (1.2) 8.9 (0.9) 30.0 (2.3) 12.5 (1.7)
Slovak Republic 3.3 (0.5) 38.9 (1.6) 14.7 (1.2) 25.9 (2.3) 31.4 (1.4) 16.0 (1.3) 47.7 (2.6) 14.6 (2.6)
Spain 2.5 (0.6) m m 9.8 (0.7) m m 27.1 (1.2) m m 26.4 (1.8) m m
Sweden 0.1 (0.1) 60.5 (1.3) 1.2 (0.4) 41.0 (1.8) 1.6 (0.5) 29.2 (2.1) 3.2 (1.5) 27.5 (3.3)
United States 0.6 (0.2) 47.9 (1.6) 3.0 (0.7) 29.1 (1.6) 10.7 (1.3) 17.2 (1.9) 13.6 (2.4) 16.8 (2.9)
England (UK) 0.6 (0.2) 57.3 (1.7) 2.3 (0.5) 33.1 (1.5) 4.8 (0.9) 19.4 (2.2) 7.6 (1.4) 17.5 (2.5)
Average1
1.1 (0.1) 50.3 (0.4) 4.3 (0.1) 32.9 (0.4) 12.7 (0.2) 20.2 (0.4) 16.5 (0.5) 20.0 (0.6)
Average-222
1.3 (0.1) 50.3 (0.4) 5.1 (0.1) 32.9 (0.4) 14.5 (0.2) 20.2 (0.4) 18.5 (0.4) 20.0 (0.6)
Partners
Cyprus3
6.3 (0.8) m m 17.4 (1.3) m m 43.8 (2.1) m m 55.2 (3.1) m m
Russian Federation4
6.7 (1.0) 33.4 (2.3) 16.4 (2.8) 24.3 (2.3) 23.2 (2.7) 18.9 (2.6) 39.4 (4.1) 16.9 (4.6)

[Part 1/1]
Table B4.2 Frequency of e-mail use at work and in everyday life
Regular use at work
and in everyday life
Regular use at work
and irregular use
in everyday life
Irregular use at work
and regular use
in everyday life
and in everyday life Missing
OECD % S.E. % S.E. % S.E. % S.E. % S.E.
National entities
Australia 55.6 (0.8) 6.0 (0.3) 23.6 (0.8) 12.6 (0.5) 2.3 (0.2)
Austria 50.0 (0.8) 5.8 (0.4) 21.7 (0.6) 20.0 (0.6) 2.4 (0.2)
Canada 55.2 (0.6) 4.6 (0.2) 26.4 (0.5) 12.5 (0.4) 1.2 (0.1)
Czech Republic 48.8 (1.2) 4.8 (0.6) 28.3 (1.2) 17.2 (1.0) 0.9 (0.3)
Denmark 62.9 (0.6) 3.6 (0.3) 25.6 (0.6) 7.3 (0.4) 0.6 (0.1)
Estonia 49.1 (0.7) 3.6 (0.2) 30.6 (0.6) 15.9 (0.4) 0.8 (0.1)
Finland 63.0 (0.6) 5.0 (0.3) 23.0 (0.6) 8.9 (0.4) 0.1 (0.1)
France 46.8 (0.6) 5.4 (0.3) 27.2 (0.5) 19.4 (0.5) 1.2 (0.1)
Germany 49.2 (0.8) 4.8 (0.4) 25.7 (0.8) 18.4 (0.7) 1.9 (0.2)
Ireland 44.3 (1.0) 7.1 (0.4) 26.8 (1.0) 21.1 (0.7) 0.6 (0.2)
Italy 34.0 (0.8) 6.2 (0.5) 25.1 (1.0) 33.6 (1.1) 1.1 (0.3)
Japan 35.4 (0.8) 11.0 (0.6) 23.2 (0.7) 28.6 (0.7) 1.9 (0.2)
Korea 40.2 (0.7) 6.4 (0.3) 18.6 (0.6) 34.3 (0.7) 0.5 (0.1)
Netherlands 66.3 (0.6) 2.1 (0.2) 23.2 (0.6) 5.6 (0.3) 2.9 (0.2)
Norway 65.6 (0.6) 4.1 (0.3) 21.4 (0.5) 6.3 (0.3) 2.7 (0.2)
Poland 39.1 (0.8) 3.6 (0.4) 25.0 (0.7) 31.9 (0.7) 0.4 (0.1)
Slovak Republic 40.7 (1.0) 4.8 (0.4) 26.3 (0.8) 27.6 (0.8) 0.5 (0.1)
Spain 37.5 (0.7) 5.7 (0.4) 26.9 (0.7) 28.4 (0.8) 1.5 (0.2)
Sweden 62.4 (0.8) 5.9 (0.4) 23.8 (0.7) 7.6 (0.4) 0.3 (0.1)
United States 51.7 (1.2) 5.7 (0.4) 21.4 (0.8) 16.1 (0.8) 5.2 (0.7)
England (UK) 56.0 (0.9) 5.6 (0.4) 24.1 (0.8) 12.4 (0.6) 1.9 (0.2)
Average1
52.2 (0.2) 5.2 (0.1) 24.2 (0.2) 16.6 (0.1) 1.8 (0.1)
Average-222
50.4 (0.2) 5.3 (0.1) 24.5 (0.2) 18.1 (0.1) 1.7 (0.1)
Partners
Cyprus3
23.8 (0.6) 6.7 (0.5) 14.2 (0.6) 31.5 (0.8) 23.8 (0.5)
Russian Federation4
23.9 (1.9) 5.4 (0.5) 23.4 (1.2) 47.0 (2.3) 0.3 (0.1)

[Part 1/1]
Table B4.3 Frequency of Internet use to better understand issues related to work and to everyday life
Regular use at work
Regular use at work
and irregular use
in everyday life
and regular use
in everyday life
OECD % S.E. % S.E. % S.E. % S.E. % S.E.
National entities
Australia 50.7 (0.8) 7.1 (0.4) 23.8 (0.8) 16.1 (0.5) 2.3 (0.2)
Austria 46.5 (0.8) 4.6 (0.4) 27.5 (0.7) 19.1 (0.6) 2.4 (0.2)
Canada 49.2 (0.5) 6.7 (0.3) 27.0 (0.4) 16.0 (0.4) 1.2 (0.1)
Czech Republic 45.4 (1.4) 3.8 (0.4) 33.9 (1.4) 16.0 (1.1) 0.9 (0.3)
Denmark 56.1 (0.7) 6.1 (0.3) 27.1 (0.6) 10.1 (0.4) 0.6 (0.1)
Estonia 45.8 (0.7) 6.1 (0.3) 30.2 (0.6) 17.1 (0.5) 0.8 (0.1)
Finland 57.6 (0.7) 4.9 (0.3) 26.5 (0.6) 10.9 (0.5) 0.2 (0.1)
France 39.6 (0.6) 3.8 (0.2) 34.2 (0.6) 21.3 (0.6) 1.2 (0.1)
Germany 45.3 (0.9) 5.1 (0.4) 30.1 (0.7) 17.5 (0.7) 2.0 (0.2)
Ireland 39.1 (0.9) 8.5 (0.4) 27.9 (0.8) 23.9 (0.7) 0.6 (0.2)
Italy 29.1 (1.0) 8.1 (0.6) 26.4 (1.0) 35.3 (1.2) 1.1 (0.3)
Japan 33.5 (0.8) 16.3 (0.7) 16.7 (0.6) 31.7 (0.7) 1.9 (0.2)
Korea 42.4 (0.7) 8.8 (0.4) 19.9 (0.7) 28.3 (0.7) 0.5 (0.1)
Netherlands 53.1 (0.7) 6.7 (0.3) 26.3 (0.6) 11.1 (0.4) 2.9 (0.2)
Norway 58.9 (0.6) 5.6 (0.3) 24.3 (0.5) 8.5 (0.4) 2.7 (0.2)
Poland 38.4 (0.7) 4.7 (0.4) 27.3 (0.8) 29.2 (0.8) 0.5 (0.1)
Slovak Republic 36.5 (0.9) 5.7 (0.4) 27.9 (0.8) 29.3 (0.9) 0.6 (0.1)
Spain 33.1 (0.8) 7.1 (0.5) 28.3 (0.7) 30.1 (0.7) 1.5 (0.2)
Sweden 53.1 (0.8) 6.5 (0.4) 29.0 (0.7) 11.0 (0.6) 0.4 (0.1)
United States 46.8 (1.0) 7.8 (0.5) 22.1 (0.6) 18.0 (0.9) 5.2 (0.7)
England (UK) 48.1 (0.9) 8.1 (0.5) 25.2 (0.9) 16.7 (0.7) 1.9 (0.2)
Average1
47.0 (0.2) 6.8 (0.1) 26.2 (0.2) 18.2 (0.2) 1.8 (0.1)
Average-222
45.2 (0.2) 6.7 (0.1) 26.7 (0.2) 19.6 (0.1) 1.7 (0.1)
Partners
Cyprus3
21.7 (0.6) 5.6 (0.5) 17.4 (0.7) 31.5 (0.8) 23.8 (0.5)
Russian Federation4
23.0 (1.3) 6.8 (0.6) 29.7 (1.4) 40.2 (1.2) 0.3 (0.1)
Note: Regular use is defined as a frequency of at least monthly use. Irregular use is defined as a frequency of less than monthly use.

[Part 1/1]
Table B4.4
Frequency of Internet use for conducting transactions (e.g. buying or selling products or services, or
banking) at work and in everyday life
Regular use at work
Regular use at work
and irregular use
in everyday life
and regular use
in everyday life
OECD % S.E. % S.E. % S.E. % S.E. % S.E.
National entities
Australia 25.3 (0.8) 4.2 (0.3) 40.6 (0.7) 27.7 (0.7) 2.3 (0.2)
Austria 15.9 (0.5) 4.4 (0.3) 32.6 (0.7) 44.7 (0.7) 2.4 (0.2)
Canada 20.4 (0.5) 4.3 (0.2) 42.2 (0.5) 31.9 (0.5) 1.2 (0.1)
Czech Republic 17.0 (1.0) 4.8 (0.5) 36.6 (1.4) 40.6 (1.3) 0.9 (0.3)
Denmark 25.3 (0.5) 3.2 (0.3) 51.6 (0.7) 19.3 (0.5) 0.6 (0.1)
Estonia 26.3 (0.6) 2.7 (0.2) 47.6 (0.6) 22.7 (0.6) 0.8 (0.1)
Finland 24.5 (0.6) 1.9 (0.2) 60.3 (0.7) 13.1 (0.5) 0.2 (0.1)
France 9.3 (0.4) 4.2 (0.3) 36.0 (0.7) 49.3 (0.7) 1.2 (0.1)
Germany 14.4 (0.6) 3.5 (0.3) 38.7 (0.9) 41.4 (0.8) 1.9 (0.2)
Ireland 17.2 (0.7) 4.4 (0.4) 36.9 (0.9) 40.9 (0.9) 0.6 (0.2)
Italy 8.0 (0.5) 5.1 (0.5) 15.7 (0.8) 70.1 (1.0) 1.1 (0.3)
Japan 7.5 (0.5) 4.3 (0.3) 22.4 (0.7) 63.9 (0.8) 1.9 (0.2)
Korea 27.9 (0.7) 4.8 (0.3) 28.5 (0.7) 38.3 (0.8) 0.5 (0.1)
Netherlands 21.1 (0.6) 2.4 (0.2) 57.1 (0.8) 16.4 (0.6) 2.9 (0.2)
Norway 25.7 (0.6) 2.1 (0.2) 56.8 (0.7) 12.7 (0.5) 2.7 (0.2)
Poland 14.4 (0.7) 3.1 (0.3) 29.9 (0.8) 52.1 (0.8) 0.4 (0.1)
Slovak Republic 15.6 (0.6) 4.2 (0.4) 27.5 (0.7) 52.2 (0.8) 0.5 (0.1)
Spain 9.0 (0.4) 4.4 (0.3) 20.3 (0.6) 64.9 (0.8) 1.5 (0.2)
Sweden 21.0 (0.6) 2.3 (0.2) 58.6 (0.8) 17.8 (0.6) 0.3 (0.1)
United States 22.5 (0.8) 5.4 (0.4) 35.0 (0.8) 31.9 (0.9) 5.2 (0.7)
England (UK) 22.9 (0.9) 3.2 (0.3) 45.1 (1.0) 26.8 (0.8) 1.9 (0.2)
Average1
20.1 (0.2) 3.6 (0.1) 41.7 (0.2) 32.8 (0.2) 1.8 (0.1)
Average-222
18.6 (0.1) 3.8 (0.1) 39.3 (0.2) 36.7 (0.2) 1.7 (0.1)
Partners
Cyprus3
5.5 (0.3) 3.3 (0.3) 11.9 (0.6) 55.5 (0.8) 23.8 (0.5)
Russian Federation4
4.0 (0.3) 4.3 (0.6) 6.8 (0.5) 84.5 (0.8) 0.5 (0.1)

[Part 1/1]
Table B4.5 Frequency of spreadsheet software use (e.g. Excel) at work and in everyday life
Regular use at work
Regular use at work
and irregular use
in everyday life
and regular use
in everyday life
OECD % S.E. % S.E. % S.E. % S.E. % S.E.
National entities
Australia 15.0 (0.6) 30.5 (0.7) 4.1 (0.4) 47.9 (0.8) 2.4 (0.2)
Austria 15.8 (0.5) 22.6 (0.7) 5.4 (0.4) 53.7 (0.8) 2.4 (0.2)
Canada 16.5 (0.5) 25.9 (0.4) 6.8 (0.3) 49.5 (0.6) 1.2 (0.1)
Czech Republic 17.7 (1.2) 25.0 (1.2) 6.4 (0.6) 50.0 (1.3) 1.0 (0.3)
Denmark 18.4 (0.5) 20.4 (0.6) 9.6 (0.5) 51.0 (0.6) 0.7 (0.1)
Estonia 14.9 (0.5) 23.6 (0.5) 7.3 (0.3) 53.4 (0.7) 0.8 (0.1)
Finland 13.7 (0.5) 23.7 (0.5) 6.2 (0.4) 56.1 (0.7) 0.3 (0.1)
France 12.5 (0.4) 24.4 (0.5) 5.0 (0.3) 56.9 (0.7) 1.3 (0.1)
Germany 16.9 (0.7) 21.1 (0.7) 6.7 (0.5) 53.3 (0.8) 2.0 (0.2)
Ireland 9.6 (0.5) 26.6 (0.7) 5.7 (0.4) 57.4 (0.9) 0.6 (0.2)
Italy 11.9 (0.6) 18.9 (0.8) 5.2 (0.4) 62.8 (0.9) 1.2 (0.3)
Japan 10.5 (0.5) 32.9 (0.6) 3.5 (0.3) 51.2 (0.8) 1.9 (0.2)
Korea 16.5 (0.5) 22.9 (0.7) 5.5 (0.3) 54.6 (0.7) 0.5 (0.1)
Netherlands 19.4 (0.6) 27.1 (0.7) 8.2 (0.4) 42.4 (0.8) 2.9 (0.2)
Norway 15.6 (0.6) 22.7 (0.7) 6.5 (0.4) 52.5 (0.7) 2.7 (0.2)
Poland 12.6 (0.6) 16.1 (0.6) 5.2 (0.3) 65.7 (0.7) 0.4 (0.1)
Slovak Republic 16.7 (0.7) 20.0 (0.8) 5.9 (0.4) 56.8 (1.1) 0.6 (0.1)
Spain 11.3 (0.6) 19.2 (0.6) 5.5 (0.3) 62.5 (0.8) 1.5 (0.2)
Sweden 14.0 (0.6) 24.4 (0.7) 5.9 (0.4) 55.5 (0.7) 0.3 (0.1)
United States 14.8 (0.6) 25.1 (0.7) 6.3 (0.3) 48.5 (1.0) 5.3 (0.7)
England (UK) 16.1 (0.7) 29.7 (0.9) 5.1 (0.4) 47.3 (0.9) 1.8 (0.2)
Average1
15.4 (0.1) 24.4 (0.2) 6.1 (0.1) 52.2 (0.2) 1.8 (0.1)
Average-222
14.9 (0.1) 23.9 (0.2) 6.0 (0.1) 53.4 (0.2) 1.8 (0.1)
Partners
Cyprus3
6.6 (0.4) 15.5 (0.6) 2.6 (0.3) 51.5 (0.8) 23.8 (0.5)
Russian Federation4
8.8 (0.9) 17.0 (0.8) 5.1 (0.7) 68.7 (1.1) 0.3 (0.1)

[Part 1/1]
Table B4.6 Frequency of a word processor use (e.g. Word) at work and in everyday life
Regular use at work
Regular use at work
and irregular use
in everyday life
and regular use
in everyday life
OECD % S.E. % S.E. % S.E. % S.E. % S.E.
National entities
Australia 30.7 (0.8) 20.8 (0.6) 13.1 (0.7) 33.1 (0.8) 2.3 (0.2)
Austria 32.1 (0.7) 17.0 (0.6) 13.7 (0.5) 34.9 (0.8) 2.4 (0.2)
Canada 29.8 (0.6) 19.9 (0.4) 15.8 (0.4) 33.2 (0.6) 1.2 (0.1)
Czech Republic 31.0 (1.2) 17.3 (0.8) 13.9 (0.8) 36.9 (1.2) 1.0 (0.3)
Denmark 41.1 (0.7) 15.5 (0.5) 19.6 (0.6) 23.1 (0.6) 0.7 (0.1)
Estonia 25.8 (0.5) 17.3 (0.6) 12.1 (0.4) 44.0 (0.7) 0.8 (0.1)
Finland 30.4 (0.7) 23.2 (0.6) 12.9 (0.5) 33.1 (0.5) 0.3 (0.1)
France 20.6 (0.4) 21.5 (0.5) 10.2 (0.4) 46.4 (0.6) 1.2 (0.1)
Germany 36.2 (0.9) 14.4 (0.5) 15.6 (0.7) 31.8 (0.8) 2.0 (0.2)
Ireland 22.2 (0.8) 22.4 (0.7) 13.2 (0.7) 41.5 (1.0) 0.7 (0.2)
Italy 20.5 (0.8) 15.4 (0.7) 11.7 (0.6) 51.3 (1.0) 1.1 (0.3)
Japan 10.6 (0.5) 31.5 (0.6) 5.7 (0.4) 50.4 (0.8) 1.9 (0.2)
Korea 22.1 (0.7) 20.3 (0.7) 8.5 (0.4) 48.7 (0.6) 0.5 (0.1)
Netherlands 43.4 (0.8) 15.4 (0.6) 16.7 (0.5) 21.6 (0.6) 2.9 (0.2)
Norway 37.6 (0.6) 19.5 (0.6) 16.1 (0.5) 24.1 (0.5) 2.7 (0.2)
Poland 28.0 (0.7) 11.0 (0.6) 12.5 (0.5) 48.0 (0.8) 0.5 (0.1)
Slovak Republic 30.3 (0.9) 13.2 (0.7) 13.7 (0.7) 42.1 (1.0) 0.6 (0.1)
Spain 23.7 (0.8) 14.4 (0.7) 13.1 (0.5) 47.4 (0.8) 1.5 (0.2)
Sweden 30.3 (0.8) 22.4 (0.6) 16.5 (0.7) 30.6 (0.8) 0.2 (0.1)
United States 29.5 (0.9) 17.2 (0.5) 14.5 (0.6) 33.6 (0.9) 5.2 (0.7)
England (UK) 32.5 (0.8) 22.2 (0.7) 12.5 (0.7) 31.0 (0.8) 1.9 (0.2)
Average1
30.3 (0.2) 18.9 (0.1) 13.5 (0.1) 35.5 (0.2) 1.8 (0.1)
Average-222
29.1 (0.2) 18.7 (0.1) 13.3 (0.1) 37.2 (0.2) 1.7 (0.1)
Partners
Cyprus3
15.1 (0.5) 14.2 (0.6) 6.2 (0.5) 40.7 (0.9) 23.8 (0.5)
Russian Federation4
19.0 (1.4) 14.6 (0.7) 12.1 (0.8) 54.0 (1.4) 0.3 (0.1)

[Part 1/1]
Table B4.7 Percentage of workers, by frequency of complex problem solving
Less than monthly or never At least monthly
OECD % S.E. % S.E.
National entities
Australia 52.7 (0.8) 44.9 (0.9)
Austria 65.3 (0.7) 32.3 (0.7)
Canada 60.7 (0.5) 37.9 (0.5)
Czech Republic 60.5 (1.5) 38.5 (1.4)
Denmark 64.9 (0.7) 34.4 (0.7)
Estonia 69.3 (0.6) 29.7 (0.6)
Finland 69.3 (0.7) 29.9 (0.7)
France 65.8 (0.7) 32.0 (0.7)
Germany 64.3 (0.8) 33.7 (0.8)
Ireland 62.5 (0.9) 36.8 (0.8)
Italy 60.0 (1.3) 38.9 (1.3)
Japan 76.5 (0.6) 21.4 (0.6)
Korea 76.3 (0.8) 23.1 (0.7)
Netherlands 67.6 (0.7) 29.4 (0.7)
Norway 64.9 (0.7) 32.1 (0.7)
Poland 70.6 (0.8) 28.5 (0.8)
Slovak Republic 60.5 (0.9) 38.5 (0.9)
Spain 64.4 (0.8) 33.9 (0.8)
Sweden 65.5 (0.8) 33.8 (0.8)
United States 51.7 (0.7) 43.0 (0.9)
Flanders (Belgium) 60.6 (0.8) 32.3 (0.8)
England (UK) 54.7 (0.9) 43.4 (0.9)
Northern Ireland (UK) 58.9 (1.2) 37.9 (1.1)
England/N. Ireland (UK) 54.8 (0.9) 43.2 (0.9)
Average1
64.1 (0.2) 33.9 (0.2)
Average-222
64.0 (0.2) 34.0 (0.2)
Partners
Cyprus3
49.3 (0.7) 26.7 (0.7)
Russian Federation4
59.0 (1.2) 39.5 (1.4)
Note: Complex problems are defined as problems that take at least 30 minutes to find a good solution.

[Part 1/1]
Table B4.8 Percentage of workers who reported lack of computer skills to do their job well, by age
OECD % S.E. % S.E. % S.E. % S.E. % S.E.
National entities
Australia 1.0 (0.4) 4.0 (0.7) 6.9 (0.9) 9.0 (1.0) 11.4 (1.3)
Austria 1.0 (0.4) 1.9 (0.5) 1.7 (0.4) 5.3 (0.7) 5.5 (1.1)
Canada 1.1 (0.3) 2.4 (0.4) 3.7 (0.4) 7.7 (0.6) 7.0 (0.7)
Czech Republic 0.1 (0.1) 1.9 (0.4) 2.3 (0.8) 4.3 (1.2) 2.8 (0.6)
Denmark 1.1 (0.4) 4.4 (0.9) 9.6 (0.9) 11.7 (1.2) 11.8 (0.9)
Estonia 2.2 (0.6) 4.3 (0.6) 7.6 (0.7) 10.5 (0.9) 9.2 (0.8)
Finland 0.8 (0.4) 4.1 (0.7) 9.0 (1.0) 14.6 (1.3) 18.9 (1.3)
France 1.8 (0.5) 5.5 (0.5) 9.5 (0.8) 12.1 (0.8) 11.1 (1.0)
Germany 0.6 (0.3) 2.9 (0.7) 3.6 (0.6) 6.1 (1.0) 4.4 (0.8)
Ireland 1.0 (0.6) 1.7 (0.4) 7.8 (0.8) 7.5 (1.0) 8.7 (1.4)
Italy 0.2 (0.3) 2.3 (0.8) 3.9 (0.6) 4.4 (0.8) 8.4 (1.7)
Japan 14.2 (1.9) 26.1 (1.7) 29.2 (1.3) 28.7 (1.5) 24.1 (1.2)
Korea 9.4 (1.5) 13.0 (1.1) 15.9 (1.1) 15.3 (1.1) 10.0 (1.2)
Netherlands 1.9 (0.6) 3.4 (0.7) 4.9 (0.6) 6.5 (0.8) 6.7 (0.8)
Norway 2.5 (0.6) 8.4 (0.9) 15.0 (1.0) 20.2 (1.3) 19.4 (1.6)
Poland 1.1 (0.2) 2.4 (0.6) 3.8 (0.8) 7.5 (1.3) 7.7 (1.5)
Slovak Republic 1.4 (0.6) 1.8 (0.5) 2.5 (0.5) 3.3 (0.6) 5.1 (1.0)
Spain 1.6 (0.7) 2.2 (0.5) 5.0 (0.8) 6.3 (0.7) 9.6 (1.5)
Sweden 1.3 (0.5) 3.7 (0.7) 7.2 (0.9) 10.5 (1.0) 13.7 (1.2)
United States 1.3 (0.6) 2.3 (0.5) 4.0 (0.8) 5.7 (0.8) 8.6 (0.9)
England (UK) 0.6 (0.3) 3.1 (0.6) 7.2 (1.0) 9.2 (1.2) 7.7 (1.1)
Average1
2.3 (0.2) 5.0 (0.2) 7.9 (0.2) 10.1 (0.2) 10.1 (0.3)
Average-222
2.2 (0.1) 4.8 (0.2) 7.6 (0.2) 9.8 (0.2) 10.0 (0.2)
Partners
Cyprus3
0.4 (0.2) 1.4 (0.3) 5.9 (0.8) 5.0 (0.9) 3.8 (0.8)
Russian Federation4
1.8 (0.6) 2.5 (0.6) 4.2 (0.9) 4.1 (0.8) 3.0 (0.5)

[Part 1/1]
Table B4.9
Percentage of workers whose lack of computer skills have affected their chances of getting a job,
promotion or pay raise, by age
OECD % S.E. % S.E. % S.E. % S.E. % S.E.
National entities
Australia 2.8 (0.7) 5.7 (0.8) 6.9 (0.9) 8.7 (1.0) 6.9 (0.9)
Austria 2.2 (0.6) 2.8 (0.6) 3.6 (0.6) 4.0 (0.6) 1.3 (0.6)
Canada 2.9 (0.5) 5.5 (0.5) 8.0 (0.6) 7.2 (0.5) 6.1 (0.7)
Czech Republic 3.0 (0.9) 4.2 (1.0) 2.0 (0.7) 1.3 (0.4) 2.3 (0.8)
Denmark 1.6 (0.5) 3.8 (0.7) 5.6 (0.8) 4.2 (0.6) 3.3 (0.4)
Estonia 5.3 (0.7) 4.8 (0.6) 5.2 (0.6) 6.1 (0.6) 5.6 (0.7)
Finland 1.5 (0.5) 4.7 (0.8) 4.4 (0.6) 2.6 (0.5) 3.5 (0.6)
France 3.0 (0.7) 5.4 (0.7) 4.8 (0.6) 5.7 (0.6) 3.5 (0.6)
Germany 1.5 (0.5) 3.2 (0.7) 3.5 (0.7) 2.9 (0.6) 2.3 (0.5)
Ireland 2.2 (0.7) 2.8 (0.5) 6.3 (0.7) 4.7 (0.8) 6.1 (0.9)
Italy 3.7 (1.2) 4.3 (0.9) 3.9 (0.6) 2.7 (0.6) 3.4 (0.8)
Japan 14.1 (1.8) 17.4 (1.3) 19.0 (1.1) 16.5 (1.2) 13.1 (1.3)
Korea 0.4 (0.2) 1.9 (0.4) 2.8 (0.6) 1.4 (0.3) 0.7 (0.3)
Netherlands 1.8 (0.5) 3.1 (0.6) 3.7 (0.6) 3.0 (0.6) 3.0 (0.7)
Norway 2.5 (0.6) 4.8 (0.7) 5.2 (0.5) 5.7 (0.7) 4.0 (0.8)
Poland 5.4 (0.5) 6.8 (0.7) 5.8 (0.9) 4.1 (0.8) 3.5 (0.9)
Slovak Republic 2.7 (0.9) 3.9 (0.6) 3.2 (0.6) 2.4 (0.4) 2.5 (0.8)
Spain 3.0 (1.0) 2.8 (0.5) 4.5 (0.6) 3.9 (0.7) 4.0 (1.1)
Sweden 1.8 (0.6) 4.7 (0.9) 3.0 (0.7) 3.4 (0.6) 4.2 (0.7)
United States 4.2 (1.0) 5.5 (0.8) 7.7 (0.9) 8.6 (0.9) 8.0 (1.1)
England (UK) 1.0 (0.5) 5.1 (0.9) 6.1 (0.8) 5.6 (0.8) 4.7 (0.9)
Average1
3.1 (0.2) 5.0 (0.2) 5.6 (0.2) 5.1 (0.2) 4.5 (0.2)
Average-222
3.1 (0.2) 4.9 (0.2) 5.4 (0.2) 4.9 (0.1) 4.4 (0.2)
Partners
Cyprus3
4.5 (1.5) 4.1 (0.7) 5.9 (0.8) 3.9 (0.8) 2.9 (0.8)
Russian Federation4
6.6 (1.4) 6.5 (1.2) 5.9 (1.0) 3.1 (0.9) 2.6 (0.7)

[Part 1/1]
Table B4.10
Percentage of workers who reported that they lack the computer skills to do the job well, by
proficiency in problem solving in technology-rich environments
No computer
National entities
Australia a a 3.7 (1.3) 9.4 (1.5) 10.4 (1.8) 7.8 (0.9) 4.3 (0.6)
Austria a a 6.0 (1.9) 4.6 (1.0) 5.1 (1.3) 3.1 (0.6) 2.4 (0.5)
Canada a a 8.1 (1.3) 9.4 (1.3) 6.9 (1.0) 4.6 (0.6) 2.8 (0.4)
Czech Republic a a 1.9 (1.0) 5.0 (1.7) 6.6 (1.8) 2.1 (0.8) 0.9 (0.4)
Denmark a a 7.5 (1.5) 10.1 (2.1) 15.2 (1.5) 8.8 (0.8) 5.6 (0.7)
Estonia a a 7.9 (1.9) 9.6 (0.9) 10.1 (1.2) 7.5 (0.7) 4.7 (0.7)
Finland a a 12.1 (2.4) 18.2 (2.1) 20.3 (2.4) 11.8 (1.2) 5.2 (0.6)
France a a 11.3 (1.7) 10.2 (1.0) m m m m m m
Germany a a 6.9 (2.3) 9.0 (2.1) 8.0 (1.4) 4.3 (0.6) 2.0 (0.4)
Ireland a a 4.7 (1.9) 10.2 (1.4) 10.1 (1.8) 4.9 (0.8) 2.2 (0.5)
Italy a a 2.5 (1.9) 7.9 (1.2) m m m m m m
Japan a a 34.8 (2.3) 34.1 (1.9) 30.1 (2.9) 29.3 (2.0) 23.5 (1.3)
Korea a a 17.6 (1.9) 27.9 (3.3) 22.2 (2.7) 17.1 (1.2) 9.7 (1.2)
Netherlands a a 8.6 (2.8) 14.2 (3.1) 5.9 (1.3) 5.4 (0.7) 3.7 (0.6)
Norway a a 6.2 (1.8) 18.5 (2.5) 20.3 (2.3) 16.2 (1.1) 11.1 (0.7)
Poland a a 4.8 (1.5) 6.4 (1.1) 5.9 (1.3) 4.4 (1.0) 3.8 (0.9)
Slovak Republic a a 2.0 (1.5) 6.2 (1.4) 3.7 (1.6) 3.6 (0.7) 1.7 (0.5)
Spain a a 8.5 (1.5) 7.9 (1.5) m m m m m m
Sweden a a 12.1 (3.0) 12.4 (2.4) 12.4 (2.1) 9.7 (1.0) 4.5 (0.5)
United States a a 8.9 (2.7) 8.5 (2.0) 5.2 (1.1) 5.3 (0.7) 3.1 (0.5)
Flanders (Belgium) a a 4.3 (1.9) 10.0 (2.7) 11.2 (1.5) 7.1 (0.9) 5.9 (0.8)
England (UK) a a 7.7 (2.5) 11.6 (3.5) 8.5 (1.6) 6.2 (0.8) 4.4 (0.7)
Northern Ireland (UK) a a 4.5 (2.1) 8.8 (4.5) 8.9 (2.2) 5.3 (0.9) 3.3 (0.7)
England/N. Ireland (UK) a a 7.6 (2.4) 11.5 (3.4) 8.5 (1.6) 6.1 (0.8) 4.4 (0.7)
Average1
a a 8.7 (0.5) 12.4 (0.5) 11.5 (0.4) 8.4 (0.2) 5.3 (0.2)
Average-222
a a 8.5 (0.4) 11.9 (0.4) m m m m m m
Partners
Cyprus3
a a 9.8 (3.5) 8.6 (1.0) m m m m m m
Russian Federation4
a a 1.4 (1.2) 5.3 (1.2) 5.1 (1.1) 3.5 (1.1) 3.0 (1.2)

[Part 1/1]
Table B4.11
Percentage of workers who reported that their lack of computer skills has affected the chances of
getting a job, promotion or pay raise, by proficiency in problem solving in technology-rich environments
No computer
National entities
Australia a a 5.3 (1.9) 9.4 (1.4) 9.2 (1.9) 7.1 (0.9) 4.9 (0.6)
Austria a a 3.4 (1.7) 2.5 (0.8) 5.4 (1.7) 3.5 (0.7) 2.9 (0.5)
Canada a a 9.3 (1.5) 7.7 (1.2) 8.4 (0.9) 6.5 (0.5) 5.0 (0.5)
Czech Republic a a 0.4 (0.4) 2.0 (0.8) 1.5 (0.8) 2.4 (0.8) 4.0 (0.9)
Denmark a a 3.7 (1.0) 3.8 (1.4) 4.6 (1.0) 4.0 (0.5) 3.6 (0.5)
Estonia a a 4.3 (1.5) 4.5 (0.7) 7.1 (0.9) 5.9 (0.6) 5.7 (0.6)
Finland a a 3.7 (1.7) 2.0 (0.9) 3.9 (1.0) 2.5 (0.5) 4.3 (0.5)
France a a 2.3 (0.9) 4.3 (0.8) m m m m m m
Germany a a 1.8 (1.1) 2.4 (1.0) 4.2 (1.1) 3.1 (0.6) 2.8 (0.4)
Ireland a a 6.6 (2.2) 5.8 (1.0) 7.0 (1.6) 4.7 (0.8) 3.0 (0.5)
Italy a a 4.0 (2.2) 1.7 (0.4) m m m m m m
Japan a a 14.1 (1.8) 10.9 (1.3) 20.5 (2.4) 19.4 (1.6) 20.9 (1.1)
Korea a a 1.3 (0.5) 3.7 (1.5) 2.9 (0.8) 2.0 (0.5) 1.5 (0.4)
Netherlands a a 5.5 (2.5) 3.1 (1.6) 3.0 (1.1) 3.4 (0.6) 2.8 (0.5)
Norway a a 8.0 (2.0) 6.5 (1.7) 7.0 (1.3) 5.1 (0.7) 3.4 (0.4)
Poland a a 6.5 (1.8) 4.9 (1.0) 5.9 (1.2) 5.9 (0.9) 8.0 (1.0)
Slovak Republic a a 4.5 (3.1) 2.3 (0.8) 4.5 (1.3) 3.8 (0.6) 3.7 (0.7)
Spain a a 4.4 (1.4) 3.5 (1.1) m m m m m m
Sweden a a 6.2 (2.1) 4.5 (1.8) 3.1 (1.2) 4.1 (0.8) 2.9 (0.5)
United States a a 16.7 (3.9) 6.2 (1.7) 9.1 (1.5) 7.8 (0.9) 5.8 (0.8)
Flanders (Belgium) a a 7.4 (2.2) 4.0 (1.6) 6.3 (1.3) 4.6 (0.8) 3.6 (0.6)
England (UK) a a 8.1 (2.7) 2.9 (1.2) 5.3 (1.2) 5.0 (0.8) 4.6 (0.7)
Northern Ireland (UK) a a 3.0 (1.7) 9.5 (5.1) 6.3 (1.7) 4.3 (0.8) 2.6 (0.8)
England/N. Ireland (UK) a a 7.9 (2.6) 2.9 (1.2) 5.3 (1.2) 4.9 (0.8) 4.6 (0.7)
Average1
a a 6.1 (0.5) 4.7 (0.3) 6.3 (0.3) 5.3 (0.2) 4.9 (0.1)
Average-222
a a 5.8 (0.4) 4.5 (0.3) m m m m m m
Partners
Cyprus3
a a 13.9 (3.8) 7.4 (1.1) m m m m m m
Russian Federation4
a a 12.4 (3.8) 7.4 (2.1) 4.2 (1.2) 6.0 (1.3) 5.5 (1.1)

[Part 1/2]
Table B4.12
Likelihood of participating in the labour force, by proficiency in problem solving in technology-rich
environments and use of e-mail in everyday life
No computer
experience
Failed ICT
No computer
experience
Failed ICT
National entities
Australia -1.1 *** -0.6 * -0.1 0.1 0.5 * -1.0 *** -0.6 * -0.1 0.0 0.3
Austria -0.7 ** -0.3 -0.4 0.2 0.3 -0.7 *** -0.4 -0.5 * -0.1 -0.1
Canada -0.6 *** -0.3 ** -0.2 0.2 0.5 *** -0.5 *** -0.3 ** -0.2 0.0 0.2
Czech Republic -0.8 *** 0.5 0.4 0.0 0.3 -0.8 *** 0.5 0.3 -0.1 0.2
Denmark -1.1 *** -0.3 -0.5 ** 0.4 ** 0.9 *** -1.1 *** -0.3 -0.5 ** 0.1 0.4
Estonia -1.4 *** -0.4 * -0.4 *** 0.2 0.5 ** -1.5 *** -0.5 * -0.5 *** 0.0 0.2
Finland -1.1 *** -0.6 *** -0.3 ** 0.6 *** 1.2 *** -1.1 *** -0.7 *** -0.6 *** 0.3 ** 0.6 **
Germany -0.3 0.1 -0.3 0.3 0.5 ** -0.3 0.2 -0.4 * -0.1 -0.1
Ireland -0.5 *** 0.2 -0.2 0.4 * 0.8 *** -0.5 ** 0.2 -0.2 0.3 0.7 **
Japan -0.4 * -0.2 -0.1 0.0 0.2 -0.4 * -0.1 -0.1 0.0 0.2
Korea -0.4 *** -0.3 -0.1 -0.1 0.3 -0.4 ** -0.3 -0.1 -0.1 0.3
Netherlands -0.4 * -0.1 -0.4 * 0.4 ** 0.7 *** -0.4 0.0 -0.3 0.5 ** 0.9 ***
Norway -1.3 *** 0.0 -0.4 * 0.5 ** 1.0 *** -1.3 *** 0.1 -0.4 * 0.3 0.5 *
Poland -0.6 *** -0.2 -0.1 0.2 0.7 *** -0.5 *** -0.2 -0.1 0.2 0.5 *
Slovak Republic -0.5 ** -0.4 0.1 0.2 0.4 * -0.5 ** -0.5 0.0 0.0 0.0
Sweden -1.1 ** -0.5 -0.4 * 0.3 0.7 *** -0.9 ** -0.2 -0.7 ** -0.2 -0.3
United States -1.0 *** -0.7 ** -0.5 *** 0.2 0.3 -0.9 *** -0.6 ** -0.6 *** 0.0 0.0
Flanders (Belgium) -0.9 *** -0.4 -0.2 0.1 0.3 -0.9 *** -0.4 -0.3 -0.1 -0.1
England (UK) -1.4 *** -0.2 0.0 0.3 0.7 *** -1.3 *** -0.2 0.0 0.2 0.5 *
Northern Ireland (UK) -0.4 0.1 -0.7 0.2 0.7 ** -0.4 0.0 -0.8 * -0.1 0.2
England/N. Ireland (UK) -1.3 *** -0.2 0.0 0.3 0.7 *** -1.3 *** -0.2 0.0 0.2 0.5 *
Average1
-0.8 * -0.2 *** -0.2 0.2 *** 0.58 *** -0.8 *** -0.2 *** -0.3 0.1 0.3 ***
Average-222
m m m m m m m m m m
Partners
Cyprus3
m m m m m m m m m m
Russian Federation4
0.2 -0.5 0.1 0.3 0.4 0.0 -0.6 * -0.1 0.1 0.0

[Part 2/2]
Table B4.12
Likelihood of participating in the labour force, by proficiency in problem solving in technology-rich
environments and use of e-mail in everyday life
No
computer
experience
Failed ICT
Frequent
use of
e-mail
No
computer
experience
Failed ICT
Frequent
use of
e-mail
OECD ß ß ß ß ß ß ß ß ß ß ß ß
National entities
Australia -0.9 *** -0.6 * -0.1 0.0 0.3 0.1 -1.2 *** -0.5 -0.2 0.0 0.3 0.5 ***
Austria -0.8 *** -0.4 -0.5 * 0.0 -0.1 -0.1 -1.1 *** -0.5 -0.6 ** -0.1 -0.1 0.1
Canada -0.6 *** -0.3 ** -0.3 0.0 0.2 0.0 -0.8 *** -0.3 * -0.3 * 0.0 0.2 0.2 *
Czech Republic -0.7 ** 0.5 0.4 -0.1 0.2 0.1 -0.7 * 0.7 0.3 -0.1 0.3 0.4
Denmark -1.2 *** -0.3 -0.5 ** 0.1 0.4 -0.1 -1.1 *** -0.3 -0.5 ** 0.2 0.5 0.2
Estonia -1.2 *** -0.4 -0.4 * 0.0 0.2 0.3 *** -1.1 *** -0.6 * -0.4 0.0 0.2 0.4 ***
Finland -0.9 *** -0.6 *** -0.5 *** 0.3 * 0.6 ** 0.3 ** -0.9 *** -0.8 *** -0.4 ** 0.3 0.5 * 0.6 ***
Germany -0.4 0.2 -0.5 * -0.1 -0.1 -0.1 -0.4 0.2 -0.5 * 0.0 0.1 0.0
Ireland -0.4 ** 0.2 -0.2 0.3 0.6 ** 0.1 -0.4 * 0.3 -0.3 0.2 0.7 ** 0.3 *
Japan -0.5 ** -0.2 -0.1 0.0 0.3 -0.1 -0.6 ** -0.2 -0.2 -0.1 0.2 -0.2
Korea -0.3 ** -0.3 -0.1 -0.1 0.3 0.1 -0.3 -0.1 -0.2 -0.2 0.2 0.2
Netherlands -0.1 0.0 -0.2 0.5 ** 0.9 *** 0.4 ** 0.1 -0.1 -0.3 0.4 1.0 *** 0.7 **
Norway -1.2 *** 0.1 -0.4 * 0.3 0.5 * 0.1 -1.4 *** 0.4 -0.2 0.5 * 0.8 ** 0.2
Poland -0.4 ** -0.1 0.0 0.1 0.5 * 0.4 *** -0.5 * -0.2 0.0 0.1 0.5 0.6 ***
Slovak Republic -0.2 -0.4 0.1 0.0 0.0 0.4 ** -0.2 -0.5 0.1 -0.1 0.0 0.4 **
Sweden -1.1 ** -0.2 -0.8 *** -0.1 -0.2 -0.3 * -0.4 -0.1 -0.7 ** -0.2 -0.2 -0.2
United States -1.0 *** -0.6 ** -0.7 *** 0.1 0.0 -0.2 -1.5 *** -0.8 ** -0.7 *** 0.0 -0.1 0.1
Flanders (Belgium) -1.2 *** -0.5 * -0.4 * -0.1 0.0 -0.4 ** -1.1 *** -0.6 -0.6 * -0.1 0.0 -0.2
England (UK) -1.2 *** -0.2 0.1 0.2 0.5 * 0.1 -1.5 *** -0.1 0.2 0.3 0.7 ** 0.4 **
Northern Ireland (UK) -0.3 0.1 -0.8 -0.1 0.2 0.2 -0.4 0.0 -0.6 -0.1 0.2 0.4 **
England/N. Ireland (UK) -1.2 *** -0.2 0.1 0.2 0.5 * 0.1 -1.4 *** -0.1 0.2 0.3 0.7 ** 0.4 **
Average1
-0.8 *** -0.2 -0.3 0.1 0.3 *** 0.1 *** -0.79 *** -0.2 ** -0.3 0.1 0.3 *** 0.2 ***
Average-222
Partners
Cyprus3
Russian Federation4
0.0 -0.6 * -0.1 0.1 0.0 0.0 0.4 -0.6 0.0 0.3 0.2 0.2

[Part 1/2]
Table B4.13
Likelihood of being unemployed, by proficiency in problem solving in technology-rich environments
and e-mail use in everyday life
No computer
experience
Failed ICT
No computer
experience
Failed ICT
National entities
Australia -1.0 -0.4 -0.1 -0.5 -1.0 ** -1.0 -0.4 -0.2 -0.6 -1.2 **
Austria -0.5 -0.4 -0.5 0.0 -0.5 -0.4 -0.3 -0.3 0.1 -0.3
Canada 0.7 * 0.5 * -0.1 0.0 -0.1 0.5 0.5 -0.1 0.2 0.3
Czech Republic 0.6 0.4 0.8 0.5 0.2 0.6 0.3 0.9 0.8 0.8
Denmark -0.9 0.0 0.5 0.1 0.5 -1.0 0.0 0.5 0.5 1.2 **
Estonia 0.9 *** 0.1 0.5 ** 0.2 -0.4 0.9 *** 0.2 0.6 *** 0.4 * 0.0
Finland 0.3 0.5 -0.1 0.0 0.0 0.3 0.2 -0.4 0.2 0.5
Germany 0.2 0.3 0.8 ** 0.2 -0.4 -0.1 -0.1 0.8 * 0.5 0.2
Ireland 0.1 -0.3 -0.1 0.1 -0.3 0.1 -0.3 0.0 0.3 0.1
Japan -1.0 -0.1 -0.6 -0.3 -0.8 -1.6 ** -1.2 -1.4 * -0.9 -1.7 **
Korea 0.0 0.2 0.0 0.0 0.0 -0.2 0.0 -0.4 0.0 0.0
Netherlands -1.3 -0.8 0.0 -0.6 -0.9 ** -1.5 -0.8 -0.2 -0.7 -1.0
Norway -12.3 0.5 -0.7 0.3 -0.2 -12.1 0.2 -0.6 0.6 0.5
Poland 0.6 * 0.2 0.3 0.1 -0.3 0.6 * 0.2 0.3 0.2 -0.2
Slovak Republic 0.5 * 0.0 -0.2 -0.5 -0.4 0.4 0.1 -0.2 -0.4 -0.2
Sweden 1.4 0.7 0.8 -0.1 -0.9 ** 1.4 0.6 0.8 * 0.1 -0.6
United States -1.2 *** -0.2 -0.2 -0.2 -0.5 -1.4 *** -0.3 -0.1 0.1 0.1
Flanders (Belgium) -1.0 -0.7 -0.2 -0.3 -0.4 -1.1 -0.6 0.0 -0.1 -0.2
England (UK) 0.0 0.4 -0.6 -0.3 -0.9 ** -0.1 0.3 -0.6 0.0 -0.3
Northern Ireland (UK) 0.3 -0.7 -1.2 -0.6 -1.0 * 0.3 -0.7 -1.3 -0.4 -0.5
England/N. Ireland (UK) 0.0 0.4 -0.6 -0.3 -0.9 ** -0.1 0.3 -0.6 0.0 -0.3
Average1
-0.7 0.1 0.0 -0.1 -0.4 -0.8 -0.1 0.0 0.1 -0.1
Average-222
m m m m m m m m m m
Partners
Cyprus3
m m m m m m m m m m
Russian Federation4
0.6 -0.6 0.1 0.6 1.0 0.0 -0.9 -0.6 0.1 -0.1

[Part 2/2]
Table B4.13
Likelihood of being unemployed, by proficiency in problem solving in technology-rich environments
and e-mail use in everyday life
No
computer
experience
Failed ICT
Frequent
use of
e-mail
No
computer
experience
Failed ICT
Frequent
use of
e-mail
OECD ß ß ß ß ß ß ß ß ß ß ß ß
National entities
Australia -0.8 -0.4 -0.2 -0.6 -1.2 ** 0.2 -0.1 -0.3 0.1 -0.7 -1.1 * 0.0
Austria -0.4 -0.3 -0.3 0.1 -0.3 0.0 -0.2 -0.3 -0.4 0.0 -0.5 -0.5
Canada 0.8 * 0.5 0.0 0.1 0.2 0.5 ** 1.4 ** 0.8 ** 0.3 0.2 0.3 0.1
Czech Republic 0.6 0.3 0.9 0.8 0.8 0.0 0.7 0.4 0.8 0.8 0.7 -0.3
Denmark -1.0 0.0 0.5 0.5 1.2 ** 0.0 -12.7 0.0 0.3 0.6 1.2 ** -0.5
Estonia 1.1 *** 0.2 0.7 *** 0.4 * 0.0 0.2 1.2 *** 0.3 0.8 *** 0.5 ** 0.2 -0.1
Finland 0.7 0.2 -0.2 0.2 0.5 0.6 ** 1.3 * 0.4 -0.1 0.3 0.5 -0.1
Germany -0.1 -0.1 0.8 * 0.5 0.2 0.0 0.3 0.1 0.8 * 0.4 0.0 0.0
Ireland 0.2 -0.3 0.0 0.3 0.0 0.2 0.4 -0.4 0.1 0.3 0.0 -0.3
Japan -1.5 ** -1.2 -1.4 * -0.9 -1.7 ** 0.1 -1.9 ** -1.6 ** -1.7 ** -1.0 -2.0 ** -0.1
Korea -0.1 -0.1 -0.3 0.0 0.0 0.3 0.2 -0.1 -0.6 -0.1 -0.1 0.0
Netherlands -0.7 -0.7 0.0 -0.7 -1.0 1.0 ** -13.1 -0.9 0.1 -0.5 -0.9 0.4
Norway -12.0 0.2 -0.6 0.6 0.4 0.1 -10.1 0.3 -0.3 0.7 0.4 0.1
Poland 0.5 0.2 0.3 0.2 -0.2 -0.1 0.9 ** 0.2 0.3 0.1 -0.2 -0.3
Slovak Republic 0.3 0.1 -0.3 -0.4 -0.2 -0.2 0.6 * 0.2 -0.3 -0.3 -0.1 -0.3
Sweden 1.8 0.6 1.0 * 0.0 -0.7 0.6 -12.3 0.8 0.8 -0.2 -1.0 * 0.6
United States -1.2 ** -0.3 0.0 0.1 0.1 0.4 * -0.5 -0.1 0.4 0.3 0.5 -0.2
Flanders (Belgium) -0.8 -0.5 0.1 -0.2 -0.3 0.4 -1.0 -0.9 0.8 -0.2 -0.2 0.2
England (UK) -0.1 0.3 -0.5 0.0 -0.4 0.1 1.0 0.7 * -0.1 0.0 -0.3 -0.2
Northern Ireland (UK) 0.2 -0.8 -1.3 -0.4 -0.5 -0.2 0.2 -0.7 -1.4 -0.5 -0.6 -0.8 **
England/N. Ireland (UK) 0.0 0.3 -0.5 0.0 -0.4 0.1 0.9 0.7 * -0.2 0.0 -0.3 -0.2
Average1
-0.66 -0.07 0.03 0.06 -0.13 0.24 *** -2.34 -0.02 0.10 0.06 -0.14 ** -0.06
Average-222
Partners
Cyprus3
Russian Federation4
0.2 -0.9 -0.5 0.0 -0.1 0.5 -0.1 -1.1 -3.3 0.4 -0.2 0.4

[Part 1/1]
Table B4.14 Percentage of adults aged 16-65 who worked during previous five years, by type of occupation
Skilled occupations
Semi-skilled
white-collar occupations
Semi-skilled
blue-collar occupations Elementary occupations Missing
OECD % S.E. % S.E. % S.E. % S.E. % S.E.
National entities
Australia 41.6 (0.8) 27.4 (0.6) 18.6 (0.6) 9.7 (0.5) 2.6 (0.3)
Austria 38.3 (0.8) 27.4 (0.7) 21.8 (0.7) 8.6 (0.4) 4.0 (0.3)
Canada 49.4 (0.5) 25.0 (0.4) 15.9 (0.4) 7.6 (0.3) 2.0 (0.1)
Czech Republic 33.8 (0.9) 24.3 (0.9) 31.8 (0.9) 8.6 (0.5) 1.5 (0.3)
Denmark 41.8 (0.6) 27.2 (0.6) 17.5 (0.5) 11.7 (0.5) 1.8 (0.2)
Estonia 40.8 (0.6) 19.4 (0.5) 28.1 (0.5) 10.2 (0.4) 1.4 (0.1)
Finland 38.0 (0.6) 28.6 (0.6) 23.6 (0.6) 9.1 (0.4) 0.8 (0.1)
France 38.0 (0.5) 25.7 (0.5) 22.9 (0.5) 11.6 (0.4) 1.8 (0.1)
Germany 35.8 (0.6) 30.1 (0.7) 22.4 (0.6) 8.7 (0.5) 3.1 (0.3)
Ireland 34.7 (0.7) 33.4 (0.7) 21.6 (0.7) 9.3 (0.5) 1.0 (0.2)
Italy 29.5 (0.7) 28.8 (0.9) 28.0 (1.0) 11.8 (0.7) 1.9 (0.3)
Japan 31.2 (0.7) 34.6 (0.6) 18.9 (0.7) 6.0 (0.3) 9.4 (0.4)
Korea 27.5 (0.6) 39.1 (0.8) 20.6 (0.6) 11.4 (0.5) 1.4 (0.2)
Netherlands 48.6 (0.6) 28.3 (0.6) 11.1 (0.4) 8.9 (0.4) 3.1 (0.2)
Norway 38.9 (0.6) 29.8 (0.6) 14.2 (0.5) 4.7 (0.3) 12.5 (0.4)
Poland 34.7 (0.7) 23.4 (0.6) 31.2 (0.6) 9.3 (0.5) 1.4 (0.2)
Slovak Republic 38.5 (0.8) 22.4 (0.7) 28.7 (0.7) 8.8 (0.5) 1.5 (0.2)
Spain 29.3 (0.7) 32.4 (0.7) 21.3 (0.6) 15.3 (0.5) 1.7 (0.2)
Sweden 41.9 (0.5) 29.6 (0.7) 20.6 (0.5) 6.3 (0.4) 1.6 (0.2)
United States 41.2 (0.8) 29.3 (0.6) 15.1 (0.7) 8.6 (0.5) 5.8 (0.7)
England (UK) 36.4 (0.7) 34.5 (0.7) 15.5 (0.6) 10.5 (0.5) 3.1 (0.3)
Average1
38.7 (0.2) 28.3 (0.1) 20.8 (0.1) 8.8 (0.1) 3.5 (0.1)
Average-222
37.8 (0.1) 28.4 (0.1) 21.2 (0.1) 9.3 (0.1) 3.3 (0.1)
Partners
Cyprus3
28.5 (0.6) 28.5 (0.7) 13.0 (0.5) 5.8 (0.4) 24.2 (0.5)
Russian Federation4
35.9 (1.5) 19.1 (0.7) 23.2 (0.8) 4.3 (0.3) 17.6 (1.6)

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