SKILLS FOR JOBS 2022
Mapping skill requirements in occupations
based on job postings data

Contents
Introduction										3Data description									4Methodology										5Discussion of results								7References										11Annex A. The O*NET+ taxonomy						12

Introduction
This note presents the new methodology used to
update the Skills for Jobs database in 2022. The
Skills for Jobs indicators are constructed in two
steps. First, surpluses and shortages at the levelof occupations are computed for each country.
Second, these occupational surpluses/shortages
are transformed into skill surpluses/shortages
using information on skill requirements in each
occupation.
Until now, this second step used data from
O*NET, a database created in 1998 by the U.S.
Department of Labor building on its predecessor
the Dictionary of Occupational Titles (DOT), andupdated on a regular basis. O*NET contains a
wealth of information on occupations, including
knowledge, skills, and abilities needed to work ineach of the almost 1 000 occupations. Most of
this information is collected from job incumbents
and occupational experts through surveys.
Knowledge, skill, and ability requirements ofoccupations are measured both in terms ofimportance and level. The former indicates whether
the particular skill or ability is important to performthe job. The latter indicates the level of masteryor proficiency in that skill or ability needed for thejob. The previous version of the Skills for Jobs
database used importance and level requirements(and more specifically a multiplication of thetwo indexes) to infer knowledge, skill and ability
requirements by occupation. The latest versionof the database contains 33 knowledge types,
35 skills and 52 abilities. O*NET defines skills
as “developed capacities that facilitate learningor the more rapid acquisition of knowledge”,
abilities as “enduring attributes of the individualthat influence performance”, and knowledge as“organized sets of principles and facts applying
in general domains”. The O*NET database has
been used extensively in labour market research.
For example, Deming (2017) uses the database
to measure the extent to which occupations use
non-routine analytical tasks, service tasks, andsocial skills.
As O*NET is developed by the Bureau of Labor
Statistics in the United States, it is geared towards

the occupational content of jobs in the U.S. labour
market. Yet, skill content of occupations might
differ across countries. Furthermore, updates of
O*NET happen regularly but only for approximately
10% of occupations each year while the skill
content of each occupation is likely to evolve
more rapidly over time, notably as the result ofmegatrend such as automation, globalisation,
and ageing. It is thus crucial to use more timely
data on the skill content of occupations, and data
which do not only reflect the U.S. context. Lastly,
the O*NET-based version of the Skills for Jobs
Database could not identify imbalances in digitalskills.
A relatively new dataset commercialised by Emsi
Burning Glass Technologies, hereafter referred
to as Emsi Burning Glass (EBG) data, wasidentified as a good candidate to update the way
Skills for Jobs indicators are constructed. EBG
is particularly rich, timely, and highly granular: it
contains data on skills required by employers
in job vacancies posted online since 2012 inseveral countries. Recognising its relevance andquality, these data have been used extensively byacademic researchers and policy makers to study
labour market dynamics and the evolution of skill
demand across occupations (Deming and Kahn,
2018; Hershbein and Kahn, 2018; Modestino,
Shoag and Ballance, 2020; Deming and Noray,2020). While this note argues the value of using
EBG data over O*NET to construct the Skills for
Jobs 2022 indicators, the new source of data
and methodological approach imply that the2022 indicators are not directly comparable totheir previous vintage.
This note briefly describes the Emsi Burning
Glass dataset and its value added over more
traditional data sources, as well as the way theskill information has been pre-processed to be
used in the Skills for Jobs 2022 Database. It thendescribes the methodology developed to replace
the occupations-skill mapping from O*NET bya similar mapping based on skill information
contained in the Emsi Burning Glass data. It
concludes with a discussion of the results.
Skills for Jobs © OECD 2022

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Data description
General description of Emsi Burning Glass data used for this study
The Emsi Burning Glass dataset used for this study
includes information on more than 200 million job
ads gathered online since 2012 across six Englishspeaking countries: Australia, Canada, New
Zealand, Singapore, the United Kingdom, and theUnited States. For the United States, earlier data for
2007, 2010 and 2011 are also available, but are notused in this work.
The Emsi Burning Glass dataset used for the Skills
for Jobs database 2022 contains standardised
information retrieved from job postings using
more than 45 000 online sources. It includes job
characteristics such as detailed industry andoccupation codes, location, posting date, nameof the employer, and requirements in terms ofeducation, professional experience, and skills.
This considerable level of detail allows analysingchanges in job requirements within -- rather than
only across -- occupations, sectors, and locations.
Furthermore, since Emsi Burning Glass data are
updated in real or quasi-real time, they also allow
for an earlier detection of emerging trends thanpreviously possible.
A number of existing studies describe therepresentativeness of Emsi Burning Glass data.
Carnevale, Jayasundera and Repnikov (2014)
show that, for the United States in 2006-2013, theaggregate number of job postings in Emsi Burning
Glass strongly correlates with the number of job
openings reported in the Bureau of Labor Statistics
Job Openings and Labor Turnover Survey (JOLTS).
If Emsi Burning Glass data over-represent openings
for high-skilled jobs, this feature is constant over time
(Deming and Kahn, 2018). Carnevale, Jayasundera
and Repnikov (2014) estimate that, in the U.S.
files, state, city, occupation title, major occupationgroup, skills, and education are correctly reported
for at least 80% of observations with non-missing
information, while accuracy is lower for minor
occupation groups, and industry codes. Recent
OECD work (Cammeraat and Squicciarini, 2021)
has also analysed the representativeness of Emsi
Burning Glass data against official employment dataat the occupational level, showing that for the period
2010-2018, for the majority of countries, Emsi
Burning Glass data is of sufficiently good quality
to conduct policy analyses. Representativenessconcerns exist for Canada and New Zealand, butissues emerge mostly for the years prior to 2015
and representativeness has improved since. Araki
et al. (2022) further benchmark the dataset’s
representativeness on hiring data sourced fromcountry-specific labour force surveys, and finda high degree of overall and occupation-level
representativeness for 15 OECD countries andSingapore.

Pre-processing of the skill information contained in the Burning
Glass dataset
To identify skills required to perform the job,
Emsi Burning Glass analyses the text of eachjob vacancy. This information is processed andstandardised, e.g. by removing duplicates, or bytreating differences in spelling for the same skill.
The resulting list of skill keywords include skills inthe sense which is commonly understood (e.g.
“Analytical Skills”), but also knowledge (e.g. “FoodSafety” or “Environmental Policy”) and abilities (e.g.
“Detail-Oriented”).
The vast majority of job postings contains theseskill keywords, i.e. information on skills required toperform the job. The proportion of job ads for whichskill requirements are expressed is particularlyhigh in the U.S., where more than 98% of job ads
Skills for Jobs © OECD 2022

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mention at least one skill keyword (except for theyear 2018 where this proportion is slightly lower).
The percentage of observations with missing skill
keywords is the highest in Canada, ranging between
2% and 17% depending on the year considered.
In other countries, the share of observations withmissing skill information is stable at around 10%
across the years.
In total, there are more than 17 000 different unique
skill keywords across all years and countries.
To conduct meaningful empirical analyses andfacilitate the interpretation of the results, these
keywords need to be grouped into a lower numberof categories. This is precisely the purpose of an
earlier work conducted at the OECD (Lassébie etal., 2021). The paper presents a methodology toclassify skill keywords found in Emsi Burning Glassdata into a pre-existing expert-driven taxonomy ofbroader skill categories, largely inspired by O*NET(see Table 1 in Annex A). The approach uses a semisupervised Machine Learning algorithm (called BERTin the Machine Learning literature) that classifies
keywords according to their definition. It allows forthe classification of the thousands of unique skill
keywords contained in the Emsi Burning Glassdataset into 61 detailed categories, themselvesorganised in 16 broad categories. Compared toa manual classification, the proposed approachorganises large amounts of skill information in ananalytically tractable form, and with considerable
savings in time and human resources.

Methodology
Rationale
The first studies exploiting Emsi Burning Glass skill
information (Deming and Kahn, 2018; Hershbein
and Kahn, 2018) measured skill requirements as skill
frequencies, i.e. the frequency with which certain
skill keywords are mentioned in the job postings ofa firm or an occupation. However, skills mentioned
in job ads may not always accurately reflect the skill
content of jobs. First, some skills might be implicit in
the job title. If not explicitly mentioned in the text of
the ad, this might lead to an under-estimation of therequirements expressed for those skills. Second,some job posts may list skills that are not essential
to perform the job. This might lead to an overestimation of the importance of those skills. Thisissue might be more important for job ads posted
online than offline, as constraints on ad length areless important for the former than for the latter.
To mitigate these potential issues, several
researchers proposed an alternative measureof skill requirements, the Relative Comparative
Advantage (RCA) of skills within occupations. RCA
measures the importance of a skill in an occupation
based on whether the skill is more frequently foundin the job posts for that occupation compared toother skills. It was adapted by Dawson et al. (2019)
for online job ads data from Alabdulkareem et al.
(2018) who initially applied the concept of Relative
Comparative Advantage, well-known in the trade
literature, to skills using O*NET importance values.
Dawson, Williams and Rizoiu (2021) then used it tomeasure skill similarity between occupations andbuild recommender systems for identifying optimaltransition pathways between occupations. Giabelli
et al. (2021) computed RCA and normalised RCA
figures using skill frequencies in online job ads (seeformulas below).

RCA enables researchers to smooth out variationsof skill frequencies between occupations that are
due to employers’ tendency to under- or overstate the importance of certain skills while writing
job advertisements1. An indicator of RCA thus
helps mitigating the issues of implicit and irrelevant
skills discussed above, and is here chosen as thepreferred indicator to build a mapping of skills in
occupations.

Formulas and intuitions
More specifically, in the present work, the frequency
of skill category s in occupation,is defined as:

Where s is not the skill keyword but the skill
category as in Lassébie et al., (2021), to maximise
the tractability of the ensuing indicators2, and where
missing skill frequencies are set to 0. To smoothout short-term fluctuations and highlight longer-term
trends, 2-year moving averages of skill frequencies
are computed. To further reduce undesirable
volatility observed in specific years, countries and

1 The underlying hypotheses are that 1) implicit skills are mentioned by some
employers and are particular to an occupation (or not mentioned in otheroccupations), and that 2) irrelevant skills are not mentioned in a majority of job
ads. See the next subsection for further details.
2 These are therefore frequencies of the skill categories in job postings in agiven occupation. Two job postings containing, respectively, one and two
skill keywords falling under the same skill category therefore have the same
frequency as defined here above.
Skills for Jobs © OECD 20225

occupations, the figures are pooled across the six
countries considered for this study. Skill frequencies
for broad categories are created as an unweighted
average of the skill frequencies of the detailed
categories3.
The Relative Comparative Advantage (RCA) of a skill
s in an occupation j is then defined as:

where
is the skill frequency,is thesum of skill frequencies across all skills foris the sum of skill
occupation j,frequencies of sill s across all occupations, andis the sum of skill frequencies over all
skills and all occupations.
The numerator assesses the relative importanceof a skill category in an occupation. The weightof some job ads that require irrelevant skills isdecreased, since their share in the total numberof job ads is low. The denominator corresponds
to the relative importance of the skill category in allother occupations. The denominator highlights whatskill categories are particular to an occupation anddecreases the importance of categories that are
universally important, or more important for otheroccupations. For example, the skill frequency ofthe occupation “Labourers in Mining, Construction,
Manufacturing and Transport” that require “Digital
Skills” will be weighted by the total number of job ads
mentioning “Digital Skills”, which includes the job adsof occupations that rely more heavily on Digital Skills(e.g. “Science and Engineering Professionals”).
Therefore, the ratio can be interpreted as a measureof relative importance of the skill category in an
occupation, compared to the importance of thesame skill category in all other occupations.
As can be inferred from the formula above, very low
skill frequencies result in extremely high RCAs. To
avoid this undesirable property, the RCA of some

3 This ensures, by construction of the RCA, that when all detailed categories
within a single broad category are in shortage (resp. surplus) for a given
occupation, then also the broad skill category is flagged as in shortage (resp.
surplus).
Skills for Jobs © OECD 2022

6

low-frequency skill categories is set to 0. In addition,
two skill categories, Visual Abilities, and Equipment
Selection, are excluded from the analysis, because
they show very low frequencies in all years, countries,
and occupations. If included, these two categories
would make the denominator in the formula below
close to 0, artificially inflating the RCA for those skill
categories. As a result, 56 detailed skill categories
are used in the Skills for Jobs database 2022.
The resulting indicator ranges between 0 and + ∞.
When the RCA is greater than 1, the skill category
is more important for this occupation than for allother occupations, indicating a relative comparativeadvantage. However, it is important to note that,while RCA values of different skill categories canbe compared within occupations, values are notcomparable across occupations because the spanof RCA values is not bounded and changes fromoccupation to occupation. As a solution, Giabelli etal. (2021) compute a normalised RCA, by dividing
the RCA by the maximum RCA value obtained
across skill categories for a given occupation. By
doing so, the most important skill category for each
occupation has a normalised RCA equal to 1:

whereis the maximum RCA in agiven occupation across the different skill
categories. Normalised RCAs thus range between
0 and 1 and can be compared both within andacross occupations.

Discussion of results
Comparison of skills mappings obtained from Emsi Burning Glassand from O*NETIn the OECD Skills for Jobs database 2022, skill
requirements by importance sourced from O*NETis substituted with similar information extracted from
Emsi Burning Glass. Yet, and as highlighted above,
the two datasets do not contain the exact same
information. In particular, information in Emsi Burning
Glass is more up-to-date and granular than O*NET,and based on several English-speaking countries
rather than on the U.S. alone. Furthermore, O*NET
reflects the skill content of occupations in the entire
economy, while Emsi Burning Glass data capture asnapshot of the online vacancy market, which may
not be representative of all existing jobs. Moreover,there is some imprecision in mapping the O*NET
occupations according to the US SOC classification
system (SOC) to the international one (ISCO) used
for the EGB data. For these reasons, the twodatasets are not expected to deliver the exact same
occupations-skills mapping.
Figure 1 shows the correlation between the two
mappings. More specifically, given that only the

relative values from O*NET – i.e. the ranking ofskills importance by occupation – matter for theconstruction of the Skills for Jobs database, the figure
shows the rank correlation (Spearman correlation
statistics) between O*NET importance values andthe normalised RCA measures computed using
Emsi Burning Glass, by occupation (ISCO-08, 2
digits). The correlation between Emsi Burning Glassskill frequencies (sf_sj) and O*NET importancevalues is also reported for comparison. The figure
shows that the normalised RCA-based rankings are
more strongly correlated with O*NET-based rankingsin most occupations, while the skill frequency’s rankcorrelates only weakly with O*NET. Yet, and as
expected, there are a number of occupations forwhich the correlation is low. This may be because
these are low-skilled occupations for which job
vacancies are less likely to be posted online (e.g.
“Assemblers”), or because these occupations
rely more heavily on “Digital Skills”, which are notincluded in O*NET.

Figure 1. Spearman rank correlation for the U.S. in 2019
Rank correlation O*NET and skill frequencies

Rank correlation O*NET and normalised RCA

0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

Note: Skills in an occupation can be ranked by “importance”, based on expert judgement (in O*NET), or on information on skills as reported in the job ads for theoccupation (skills frequency vs RCA). This figure correlates the ranking of skills when O*NET is used with the ranking when skill frequencies are used (horizontal marker),or when the normalised RCA is used (triangle marker). Correlations are by 2-digit ISCO-08 occupations and range between 0 and 1. Only skill categories which originate
from O*NET appear in the correlation.
Source: OECD calculations on U.S. O*NET and Emsi Burning Glass data.
Skills for Jobs © OECD 2022

7

To further check whether it is reasonable to associatenormalised RCAs with skill importance, Table A B.1(in Annex B) shows, for each occupation, the fiveskill categories with the highest normalised RCA
values, with sensible and reassuring overall results.
For instance, management and negotiation skills
are assessed as important for “Legislators, senior
officials and managers”, and the same applies tospecialised knowledge fields for “Professionals” and“Associate Professionals and Technicians”. Clericalsupport workers mostly require administrative skills
such as “Clerical”, “Office Tools And Collaboration

Software”, and “Reading Comprehension”. “Service
and sales workers” mostly need skills to deal withcustomers: “Customer and personal service”,“Sales And Marketing” but also “Public Safety And
Security”. For “Skilled agricultural, forestry andfishery workers”, skills “Biology”, “Building andConstruction”, “Installation and Maintenance” areespecially important.

Final skill imbalances
Defining a mapping of skills in occupations is but
one step in the process of measuring skill shortages
at the country level. As reported in Formula (3)below, calculating an index of skill imbalance(shortage or surplus) for country c and a given skill
category s requires three fundamental pieces of
information: i) the importance of that skill category
in each occupation j based on the normalised
RCA (which relies here on cross-country pooleddata), ii) a measure of the size of the occupational
imbalance in the country (i.e. whether occupation j
is in shortage or surplus), iii) and the relative size ofthe occupation in the country’s total employment.
Further details on this methodology, and in particularon (ii), can be found in (OECD, 2017).

The mapping (i) given by the normalised RCA ranges
between 0 and 1, the occupational imbalance index(ii) ranges between -2.5 (surplus) and 2.5 (shortage),and employment shares (iii) range between 0 and 1.
By construction, each country displays occupationsin shortage as well as in surplus (OECD, 2017).

The mapping (i) given by the normalised RCA ranges
between 0 and 1, the occupational imbalance index(ii) ranges between -2.5 (surplus) and 2.5 (shortage),and employment shares (iii) range between 0
and 1. By construction, each country displays anapproximately equal number of occupations inshortage or surplus (OECD, 2017).

As a way of example, one can imagine an economy
composed by two occupations (a and b) being
respectively in large surplus and small shortage
and accounting for respectively 70% and 30% of
the country’s employment. In this economy there
are two skill categories (x and y), with x displaying
a higher RCA in occupation B than in A, and theviceversa for y.

Occupation

Occupation’s
imbalance

Occupation’s
employment share

a

-1.6

0.7

b

0.4

0.3

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As a way of example, one can imagine an economy
composed by two occupations (a and b) being
respectively in large surplus and small shortage
and accounting for respectively 70% and 30% of
the country’s employment. In this economy there
are two skill categories (x and y), with x displaying ahigher RCA in occupation b than in a, and the viceversa for y.

Skill category

Normalised RCA

x

0.1

y

0.8

x

0.8

y

0.1

Then the country’s imbalance for skill categories x and y is:

Skill category x is therefore in balance (or mild
surplus) in this economy, despite the fact thatoccupation a is in large surplus and represents alarge share of total employment. This is especially
due to the fact that skill category x is not very
important for occupation a, as flagged by the low
normalised RCA (0.1), while it is very important in
occupation b (0.8), which is conversely in shortage.
The relative importance of skill category y acrossoccupations, conversely, goes in parallel with therelative occupational imbalance, resulting in a large
skill surplus at the aggregate level.
As the country-level skill-specific imbalance is the
product of three different items referring to different
concepts, the exact magnitude of the imbalance
has no straight-forward interpretation. One can,however, compare the imbalance for two distinct
skill categories, and mention that skill y is in larger
surplus than skill x. Similarly, one can place theimbalance for skill x in the distribution of imbalances
created by all the skill categories of the O*NET+
classification.
The example also shows that skill categories witha similar average importance across occupations(by construction here 1/2* 0.1+1/2*0.8= 0.45) can
display significantly different aggregate imbalances
depending on the relative importance of these skills
across occupations in shortage or surplus.
In the example, both skill categories are in surplus.
Even in a more complex economy with more skills
and more occupations, and a balanced number ofoccupations in shortage or surplus by construction,some countries can display a lot (if not the totality)of skills in surplus (respectively, in shortage), as long
as:
•

•
•

Occupations in shortage (resp., surplus) display
small shortages (resp., surpluses), or
For the occupations in shortage (resp., surplus)
the skill is not very important (i.e., has a low
normalised RCA).

Similarly, the same skill category can be in shortageand in surplus in two different moments of time
due to changes in any of the three components
described above (skill requirements in occupations,occupational imbalances, employment shares). For
example, a skill can switch from being in shortage
to being in surplus if:
•
•

•

•

The skill becomes more important for
occupations that are and were in surplus, or
The skill has always been important for
occupations that grow substantially in surplus(while being in small surplus or even in shortageat the beginning of the period), or
The importance of the skill for the occupationand the size of the occupational imbalancestay approximately unchanged throughout theperiod, but the share of employment in surplus
occupations increases, or
Any combination of the three options above.

Lastly, the skill imbalances in the Skills for Jobs
database are often expressed not in their absolutevalue (-0.016 and 0.884 in the example) but after
rescaling. The rescaling divides the skill imbalancesby the highest skill imbalance available in the dataset(across all countries, years and skill categories). This
extra rescaling is used for presentational purposes
only, and aims to provide an indication of the relative
size of the imbalance in a given country, skill or
moment in time.

Occupations in shortage (resp., surplus) aresmall (i.e., low proportion of total employment),or

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Limitations
The methodological section above discussed
several shortcomings usually associated with EBG
data that are mitigated by the use of normalised
RCA measures. Other issues, conversely, cannot
be solved with the use of normalised RCA measuresand should be kept in mind when interpreting thefinal results.
A number of existing studies have investigated
the representativeness of EBG data along
several dimensions and found satisfying results(Cammeraat and Squicciarini, 2021; Carnevale,
Jayasundera and Repnikov, 2014; Deming, 2017).
Yet, vacancies for a specific occupation may not be
representative of all existing jobs in that occupation,and their skill requirements might be biased towards
some types of skills. For example, employers may
be seeking candidates with up-to-date knowledge
and skills related to new technologies whereas mostincumbent workers may not have these skills. If thisis the case, the mapping constructed using EBG
data will be more representative of new jobs createdthan of the whole labour market, and hence, in asense, more future-oriented.. If this the case, theRCA would be biased in an ex-ante unknown
direction. It is important to acknowledge, however,that there exist no empirical test for such selectionon unobservable characteristics to date.
Furthermore, there is a concern that the increasing
number of skills mentioned in job postings overtime, and improving data scraping technologies
with which the advertisement texts are stored into a
dataset, can bias the mapping of skill requirements
in occupations if using RCAs. For this to be the case,however, two conditions should simultaneously
apply: (i) that what increases is not just the
number of skill keywords in an occupation, but the
number of skill categories (i.e. broader concepts),and (ii) that the newly-added skill categories arevery frequent across postings and receive highnormalised RCAs. Both conditions are possible but
unlikely, and in particular (ii), since a whole new skill
category emerging for an occupation is very unlikely
to become ubiquitous in a short amount of time.
Lastly, online job postings data in the Skills for
Jobs database 2022 are used to measure skill
requirements in occupations, as opposed toactual skill imbalances directly. This methodologicalapproach is consistent with the previous vintageof the Skills for Jobs database, but implies
Skills for Jobs © OECD 2022

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that differences in skill-level imbalances acrossoccupations are averaged away in the aggregate.
Alternative approaches can be explored in future
work, whereby previously-unavailable granular dataon skill-level demand and supply can be usedinstead.

References
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Dawson, N. et al. (2019), “Adaptively selecting occupations to detect skill shortages from online job ads”, 2019 IEEE International
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Annex A. The O*NET+ taxonomy
Table A.1. Skill categories used in the Skills for Jobs database
Broad category
Attitudes

Arts and Humanities

Business Processes

Category label

SourceAdaptability/resilience

ESCO

Motivation/commitment

ESCO

Self-management/rigor

ESCO

ValuesESCO

Fine ArtsO*NETHistory and Archaeology

O*NET

Philosophy and Theology

O*NET

ClericalO*NETSales and Marketing

O*NET

Customer and Personal Service

O*NET merged

Production andTelecommunications

O*NET

Technology

Building and Construction

O*NETEngineering, Mechanics and Technology

O*NET

DesignO*NETFood Production

O*NET

Production and Processing

O*NETTransportation

O*NET

Medicine

Law and Public Safety

SciencePhysical Skills

Cognitive Skills

Communication

DigitalResource Management

Social Skills

Training and Education

Quality Control Analysis

O*NET

Installation and Maintenance

O*NET merged

Medicine and Dentistry

O*NET

Psychology, Therapy, Counselling

O*NET merged

Law and Government

O*NET

Public Safety and Security

O*NET

Biology

O*NET

Chemistry

O*NET

Geography

O*NET

Physics

O*NET

Sociology and Anthropology

O*NET

Psychomotor Abilities

O*NET

Auditory and Speech Abilities

O*NET

Physical Abilities

O*NET merged

Originality

O*NET

Quantitative Abilities

O*NET

Reasoning and Problem-solving

O*NET merged

Learning

O*NET merged

Active Listening

O*NET

Reading Comprehension

O*NET

Speaking

O*NET

Writing

O*NET

Communications and MediaO*NET

Office Tools and Collaboration Software

New category, based on ESCO

Digital Content Creation

ESCO

Digital Data Processing

ESCO

ICT Safety, Networks and Servers

New category, based on ESCO

Computer ProgrammingO*NET

Web Development and Cloud Technologies

New category

Time Management

O*NETManagement of Material Resources

O*NETManagement of Financial Resources

O*NET mergedManagement of Personnel Resources

O*NET merged

Administration and Management

O*NET

Coordination

O*NET

Persuasion and Negotiation

O*NET

Social Perceptiveness

O*NET

Judgment and Decision Making

O*NET merged

Training and Education

O*NET

Note: The mention “O*NET merged” in the last column indicates when the category is the result of merging two or more O*NET original categories. The list only contains
the O*NET+ categories from Lassébie et al. (2021) that are used in the Skills for Jobs database.
Source: OECD elaborations on O*NET and ESCO hierarchies.
Skills for Jobs © OECD 2022

12

Annex B. The most important skill categories by occupation
Table B.1. Five skill categories with the highest normalised RCA value, by occupation
Occupation

Category 1

11 Chief executives, senior

Persuasion And Negotiation

RCA

Category 2
1

RCA

Reading Comprehension

Category 3
0.81

officials and legislators
12

Administrative

andProduction

specialized

Category 4
0.77

Management
Persuasion And Negotiation

1

Sales And Marketing

0.64

commercial managers
13

RCA

Administration And
Administration And

andSocial Perceptiveness1

Sociology And Anthropology

0.95

Psychology, Therapy,

Category 5

RCA

0.58

Law And Government

0.58

0.56

Judgment And Decision

0.56

Resources
0.58

Management

services

RCAManagement Of Financial
Management Of Financial
Resources

0.40

Counselling

Judgment And Decision

Making0.35

Medicine And Dentistry

0.33

0.63

Management Of Financial

0.49

Makingmanagers
14 Hospitality, retail and other

Philosophy And Theology

1

Food Production

0.95

services managers
Geography

1

Digital Content Creation

0.82

professionals

Engineering, Mechanics And

Management Of Material
Resources

Resources

0.68

Chemistry

0.67

Design0.51

0.34

Learning

0.24

Training And Education

0.19

Technology

22 Health professionals

Psychology, Therapy,

1

Medicine And Dentistry

0.61

Counselling
23 Teaching professionals
BusinessandInformation

andJudgment And Decision
MakingLearning

1

Training And Education

0.65

Values0.36

History And Archaeology

0.25

Fine Arts

0.23

Persuasion And Negotiation

1

Sales And Marketing

0.76

Management Of Financial

0.65

Auditory And Speech Abilities

0.58

Originality

0.49

Computer Programming1

Web Development And Cloud

0.74

Ict Safety, Networks And

0.58

Digital Data Processing

0.50

Adaptability/Resilience

0.34

administration professionals
25

0.85

Management

21 Science and engineering

24

Administration And

Resources

communications technology

Technologies

Servers

professionals
26 Legal, social and culturalprofessionals

Psychology, Therapy,

1

Sociology And Anthropology

0.97

Reading Comprehension

0.92

Law And Government

0.60

Geography

0.52

1

Chemistry

0.77

Quality Control Analysis

0.61

Production And Processing

0.60

Engineering, Mechanics And

0.57

Counselling

31 Science and engineering

Digital Content Creation

associate professionals
32

HealthTechnology

associate

Chemistry

1

Medicine And Dentistry

0.96

professionals
33

0.60

Reading Comprehension

0.52

Biology

0.50

0.36

Sales And Marketing

0.30

Auditory And Speech Abilities

0.26

History And Archaeology

0.37

Office Tools And

0.32

Counselling

Business

administration

Psychology, Therapy,

andPhilosophy And Theology

1Persuasion And Negotiation

0.53

associate

Management Of Financial
Resources

professionals
34 Legal, social, cultural andrelatedFood Production

1

Law And Government

0.48

Originality

0.47

Adaptability/Resilience

0.44

1

Computer Programming

0.71

Telecommunications

0.68

Web Development And Cloud

0.38

associate

professionals
35

and

Ict Safety, Networks And

communications technicians

Information

Servers

41 General and keyboard

ClericalTechnologies
1

Digital Data Processing

0.49

clerks

Office Tools And

Collaboration Software

0.39

History And Archaeology

0.33

Writing

0.32

Collaboration Software

42 Customer services clerks

Active Listening

1

Customer And Personal

0.86

Clerical

0.60

Reading Comprehension

0.56

Social Perceptiveness

0.50

0.94

Geography

0.93

Quantitative Abilities

0.81

Reading Comprehension

0.79

0.69

Quantitative Abilities

0.69

Management Of Personnel

0.51

Telecommunications

0.42

Service
43 Numerical and material

Transportation

1

recording clerks

Management Of Material
Resources

44 Other clerical support

Clerical1

workers

Judgment And Decision
Making

51 Personal service workers

Motivation/Commitment

1

Food Production

Resources
0.97

Customer And Personal

0.91

Installation And Maintenance

0.61

Physical Abilities

0.61

0.61

Customer And Personal

0.50

Design0.44

0.73

Learning

0.51

0.39

Law And Government

0.36Service52 Sales workers

Management Of Material

1

Sales And Marketing

0.78

Active Listening

Resources
53 Personal care workers

Training And Education

Skills for Jobs © OECD 2022

Service1

Sociology And Anthropology

0.88

Medicine And Dentistry

0.76

Psychology, Therapy,
Counselling

54

Protective

services

Public Safety And Security

1

Ict Safety, Networks And

0.44

Auditory And Speech Abilities

0.43

Learning

35

and

Ict Safety, Networks And

communications technicians

Information

Servers

41 General and keyboard

Clerical1

Computer Programming

0.71

Telecommunications

0.68

Web Development And Cloud

0.38

Technologies
1

Digital Data Processing

0.49

clerks

Office Tools And

Office Tools And

0.32

Collaboration Software

0.39

History And Archaeology

0.33

Writing

0.32

Collaboration Software

42 Customer services clerks

Active Listening

1

Customer And Personal

0.86

Clerical

0.60

Reading Comprehension

0.56

Social Perceptiveness

0.50

0.94

Geography

0.93

Quantitative Abilities

0.81

Reading Comprehension

0.79

0.69

Quantitative Abilities

0.69

Management Of Personnel

0.51

Telecommunications

0.42

Service
43 Numerical and material

Transportation

1

recording clerks

Management Of Material
Resources

44 Other clerical support

Clerical1

workers

Judgment And Decision
Making

51 Personal service workers

Motivation/Commitment

1

Food Production

Resources
0.97

Customer And Personal

0.91

Installation And Maintenance

0.61

Physical Abilities

0.61

0.61

Customer And Personal

0.50

Design0.44

0.73

Learning

0.51

Service52 Sales workers

Management Of Material

1

Sales And Marketing

0.78

Active Listening

Resources
53 Personal care workers

Training And Education

Service1

Sociology And Anthropology

0.88

Medicine And Dentistry

0.76

Psychology, Therapy,
Counselling

54

Protective

services

Public Safety And Security

1

workers

Ict Safety, Networks And

0.44

Auditory And Speech Abilities

0.43

Learning

0.39

Law And Government

0.36Servers

61 Market-oriented skilled

Psychomotor Abilities

1

Biology

0.41

Values0.33

Public Safety And Security

0.30

Physical Abilities

0.21

Biology1

Food Production

0.68

Physical Abilities

0.56

Production And Processing

0.33

Installation And Maintenance

0.24

Building And Construction

1

Chemistry

0.28

Installation And Maintenance

0.27

Engineering, Mechanics And

0.22

History And Archaeology

0.20

agricultural workers
62 Market-oriented skilledforestry, fishery and hunting
workers
63

Subsistence

fishers,

farmers,

hunters

andTechnology

gatherers
71

Building

tradesandworkers,

relatedProduction And Processing

1

Building And Construction

0.97

excludingEngineering, Mechanics And

0.76

Installation And Maintenance

0.63

Design0.60

Technology

electricians
72 Metal, machinery andDigital Content Creation

1

Psychomotor Abilities

0.87

Physics

0.69

Clerical

0.64

Physical Abilities

0.62

Telecommunications

1

Engineering, Mechanics And

0.97

Installation And Maintenance

0.69

Quality Control Analysis

0.49

Physics

0.48

0.82

Physics

0.71

Engineering, Mechanics And

0.61

Chemistry

0.57

related trades workers
74 Electrical and electronic
trades workers
81

Technology

Stationary

plant

and

Quality Control Analysis

1

Production And Processing

machine operators

Technology

82 Assemblers

Design1

Building And Construction

0.83

Psychomotor Abilities

0.58

Quantitative Abilities

0.56

Chemistry

0.50

83 Drivers and mobile plant

Physics1

Psychomotor Abilities

0.69

Engineering, Mechanics And

0.42

Building And Construction

0.38

Design0.31

Transportation

1

Values0.35

Physical Abilities

0.23

Installation And Maintenance

0.22

Public Safety And Security

0.21

Installation And Maintenance1

Customer And Personal

0.49

Active Listening

0.46

Motivation/Commitment

0.43

Philosophy And Theology

0.42

Values1Biology

0.54

Psychomotor Abilities

0.53

Physical Abilities

0.20

Building And Construction

0.20

Transportation

1

Quantitative Abilities

0.90

Physical Abilities

0.68

Management Of Material

0.57

Psychomotor Abilities

0.55

operators

Technology

91 Cleaners and helpers
92 Agricultural, forestry andfishery labourers
93

Labourers

construction,

Serviceinmining,manufacturing

and transport
94

Food

preparation

assistants

Resources

95 Street and related sales

Food Production

1and service workers
96 Refuse workers and other

Management Of Material

0.22

Motivation/Commitment

0.20

Active Listening

0.19

Coordination

0.15

0.82

Installation And Maintenance

0.73

Adaptability/Resilience

0.70

Quality Control Analysis

0.55

Resources
Building And Construction

1

elementary workers

Note: Occupations refer to the 2-digit ISCO2008 nomenclature.
Source: OECD calculations on U.S. O*NET and Emsi Burning Glass data.

Skills for Jobs © OECD 2022

Engineering, Mechanics And
Technology

Contacts:
Glenda Quintini glenda.quintini@oecd.org
Luca Marcolin luca.marcolin@oecd.org
Julie Lassebie julie.lassebie@oecd.org
Design and editorial support:
Monica Meza-Essid

www.oecdskillsforjobsdatabase.org