the future demand for employability skills: a new
TRANSCRIPT
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THE FUTURE DEMAND FOR EMPLOYABILITY SKILLS:
A NEW DIMENSION TO LABOR MARKET FORECASTING
IN AUSTRALIA
Alexis Esposto
Swinburne University of Technology
and
G.A.Meagher
Centre of Policy Studies, Monash University
1. INTRODUCTION
In recent years there has been considerable interest in employability skills, both in Australia and
overseas. In 2001, the Australian Chamber of Commerce and Industry and the Business Council
of Australia undertook a major research project designed to provide the Department of
Education, Science and Training with a detailed understanding of the employability skills needs
of industry. The research was published by the Commonwealth of Australia in 2002 as the
report Employability Skills for the Future.
In the report, employability skills are defined as “skills required not only to gain employment,
but also to progress within an enterprise to achieve one’s potential and contribute successfully to
enterprise strategic directions”. The report also identifies an Employability Skills Framework
which incorporates eight key skill groupings:
• communication skills,
• team work skills,
• problem-solving skills,
• initiative and enterprise skills,
• planning and organising skills,
• self-management skills,
• learning skills, and
• technology skills.
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A striking feature of the report is its implicit contention that the employability skills required for
the future can be determined in the absence of any detailed view of the industrial and
occupational structure of the economy. Thus, the report argues that employability skills should
equip the workforce to meet the “challenges facing Australian industry”, especially insofar as
those challenges have to do with “globalisation and the knowledge economy”, but no specific
connections are made. Further, “the required technical and job-specific skills, being subject to
ongoing change, are not readily predictable. What is important, therefore, is the capacity to
continually adapt and upgrade with the application of core or generic employability skills that
can be transferred across different settings."
The idea seems to be that workers should be provided with a set of generic skills which will
enable them to move from one job to another as the need arises. But suppose that “technical and
job-specific skills” can be associated with the occupations of the Australian Standard
Classification of Occupations (ASCO). While all eight skills in the Employability Skills
Framework may be required to some extent in all ASCO occupations, some skills will be more
relevant for some occupations than others. For example, Technology skills would seem to be
very important for 1224 Information technology managers, but not particularly important for
9932 Fast food cooks. Hence, if it is thought that more Information technology managers than
Fast food cooks will be needed to meet the future challenges facing Australian industry, the
training resources devoted to generic skills should be allocated appropriately. Generic
employability skills may well be more transferable than job-specific skills, but not (or, at least,
not obviously) to the extent that structural change can safely be ignored when determining future
requirements.
Furthermore, while it is certainly true that future employment by occupation is “not readily
predictable”, it is not true that it cannot be predicted at all. Many industrialised countries
routinely produce forecasts of employment by industry and occupation1. In Australia, the Centre
of Policy Studies (CoPS) at Monash University has done so for more than ten years.
The position adopted in Employability Skills for the Future and similar reports would appear to
owe more to necessity than to virtue. It seems likely that the link between the structure of the
1 See Neugart and Schomann (2002) for a recent survey.
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economy and the demand for particular generic skills is typically ignored, not because the link is
thought to be unimportant, but because there has been no suitable analytical tool for its
elaboration. It is this deficiency which the current paper seeks to redress.
In 1998, the U.S. Department of Labor introduced the Occupational Information Network.
Commonly referred to as the O*NET, it is a comprehensive database linking worker attributes
(or employability skills, in terms of the above discussion) and job characteristics (or job-specific
skills). Moreover, the links are specified in both qualitative and quantitative terms. Esposto
(2005) has adapted the O*NET to the Australian labour market and created a means whereby the
Monash occupational forecasts can be extended to employability skills. The paper presents the
first such forecasts and details the methodology involved.
The rest of the paper is organised as follows. Section 2 provides a review of the Australian
literature on employability skills. Section 3 describes the O*NET and its adaptation to
Australian data. Section 4 describes the Monash Forecasting System and its foreshadowed
extension. Section 5 presents forecasts of employment for various kinds of employability skills.
Section 6 contains concluding remarks.
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2. Employability Skills: A Review
The motivation for developing, defining and identifying employability skills has a historical
background that goes back to the beginning of the 1980s brought about by a desire by various
industry groups, policy makers and educators to remain competitive and to gain ground in local
and global markets (Allen Consulting, 2004; ACER, 2001; and DEST, 2002). The need to
compete internationally has forced industry and enterprises to adapt and change, while the skills,
knowledge and abilities required by individuals to remain competitive in the labour market have
had to be realigned in order to meet a new global competitive reality. Thus, the challenge for
industry, educational bodies and policy makers in Australia has been to identify the skill sets that
make individuals employable in a rapidly changing economy and labour market. To date, the
most important efforts to try to establish the skill sets that are required to enter, or access and
remain in the world of work have been the Karmel Report (1985), the Finn Report (1991), the
Mayer Report (1992) and the Employability Skills for the Future Report (2002).
The Karmel Report in 1985 stressed the requirement of the secondary school sector to support
the attainment by graduates of educational standards that would lead to long term employability.
The Finn Review Committee of 1991 was required to report on “… national curriculum
principles designed to … develop key competencies (Australian Education Council, Finn
Review, p. 2). The report stressed among other policy matters the inclusion of curriculum
principles that supported the development of key competencies (DEST, 2002, p. 21) and
recommended six key areas of competence, including Language and Communication,
Mathematics, Scientific and Technological Understanding, Cultural Understanding, Problem
Solving and Personal and Interpersonal Skills (ACER, 2001, p. 13). In 1992, the Mayer
Committee “…used its own expertise, consulted with industry and with educators in the school
and Vocational, Educational and Training (VET) sectors, and to a lesser extent with the higher
education sector, and finally undertook a validation exercise which involved further
consultations with industry” (ACER, p. 13). The committee recommended a set of
competencies, which are detailed in Table 1. The Committee created three levels of performance
for each competency “… which differentiated the levels of competency necessary to undertake
the activity, manage the activity, or evaluate or revise an activity undertaken” (DEST, 2002, p.
22).
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In 2002, Department of Education, Science and Training (DEST) commissioned the
‘Employability Skills for the Future Report’, which provided advice on the following 5 key
areas:
• possible new requirements for generic employability competencies that industry requires,
or will require, in the foreseeable future, since the Mayer key Competencies were
developed;
• clear definitions of what Australian and leading business enterprises mean by
‘employability’ skills and the consistency or otherwise between the various terms
similarly studied;
• a proposed suite of employability skills, including outlines of assessment, certification
and reporting of performance options that suit both industry and education;
• industry (small, medium and large business) reactions to the proposed suite and reporting
options;
• a report on the case studies involving 13 large enterprises; and
• a report on focus group research with small and medium-sized enterprises. (DEST, 2002,
p. 2).
The study was based upon a literature review by Australian Council for Educational Research
(ACER) (2001), case study research with large firms, and focus groups/interviews with small
and medium firms. As mentioned above, the report developed an Employability Skills
Framework consisting of personal attributes, skills and elements. The Report includes three key
terms that are described in Table 2 below and which reflect the views of employers operating in
small, medium and large sized enterprises. These have been developed in order to assist in both
the enterprise’s views and the framework.
The framework of the Employability Skills for the Future is underpinned by a number of critical
factors. Firstly, it is closely linked and builds on the Mayer Key competencies. Secondly,
employers (regardless of enterprise size) recognise the link between ‘Employability Skills’ with
the Mayer Competencies, and, thirdly, small, medium and large firms identify the same critical
mix of skills as being necessary for employability and continued employment for employees.
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Table 1. List of Mayer Key Competencies
Key Competencies Descriptors
1. Collecting, analysing and
organising information
The capacity to locate information, sift and sort the
information in order to select what is required and
present in a useful way, evaluate both the information
itself and the sources and methods used to obtain it.
2 Communicating ideas and
information
The capacity to communicate effectively with others
using a whole range of spoken, written, graphic and other
non-verbal means of expression.
3. Planning and organising
activities
The capacity to plan and organise one’s own work
activities, including making good use of time and
resources, sorting out priorities and monitoring
performance.
4. Working with others and
in teams
The capacity to interact effectively with other people
both on a one-to-one basis and in groups, including
understanding and responding to the needs of others and
working effectively as a member of a team to achieve a
shared goal.
5. Using mathematical ideas
and techniques
The capacity to use mathematical idea, such as number
and space, and techniques, such as estimation and
approximation, for practical purposes.
6. Solving problems The capacity to apply problem-solving strategies in
purposeful ways, both in situations where the problem
and the desired solution are clearly and evident, and in
situations requiring critical thinking and a creative
approach to achieve an outcome.
7. Using technology The capacity to apply technology, combining the
physical and sensory skills needed to operate equipment
with the understanding of scientific and technological
principles needed to explore and adapt systems.
Source: (Australian Education Council. Mayer Committee, 1992, pp. 8-9).
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Table 2 Terminology used in explaining the Employability Skills Framework
Term Explanation
Personal attributes Term used to describe a set of non skill-based behaviours
and attributes that employers felt were as important as
the employability skills and other technical job-specific
skills.
Skills Term used to describe the learned capacity of the
individual. Skills has been used instead of competencies,
reflecting the language of enterprises interviewed and to
avoid any definitional confusion with the different ways
competencies is used.
Elements The elements are the facets of skill that employers
identified as important.
The mix and priority of these elements would vary from
job to job.
The list of elements is not exhaustive but rather reflects
the information provided by the interviewees in this
study.
The list of elements is indicative of the expectations of
employers.
The level of sophistication in the application of the
element will depend on the job level and requirements.
Source: DEST, 2002, p. 36.
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3. The O*NET
1.1 A Suggested Approach to Defining and Measuring Skill
One way of achieving a good understanding of the skill needs of a changing economy and labour
market is through the Occupational Information Network (O*NET). O*NET is an extensive and
comprehensive database that describes the attributes and characteristics of occupations and
workers. Its first version was launched in 1998 and is known as O*NET 98. The O*NET was
designed to replace the DOT and is considered to be the most comprehensive standard source of
occupational information in the US. An advantage of O*NET is that it offers statistical
information that can be applied to the Australian context to analyse labour market change.2 The
O*NET was developed by a consortium led by the Department of Labor in the US. It was built
to replace the Dictionary of Occupational Titles (DOT), which was conceived in the 1930s and
was last published in 1991. The DOT was developed in an industrial economy during a time that
emphasised blue-collar occupations. It was updated periodically and proved very useful for
more than six decades. Its usefulness, however, faded as economic changes shifted towards
information and services away from heavy industries and other blue-collar work. Because of
these changes, the need for more up to date occupational information that reflects modern jobs
spurred the creation of the O*NET. The O*NET, which was developed on the foundations of the
DOT, was a response to a growing demand for an expanding public employment service and was
intended to assist, among other things, in job placement activities. It follows a different strategy
from DOT, providing very detailed information on about 1,120 occupations rather than more
limited information on over 12,000 occupations. The O*NET is continually updated to reflect
the dynamic and ever-changing nature of employment. For example, the latest version is the
O*NET 8.3
2 For example, Pappas (2001) used the DOT to analyse the causes of increasing earnings inequality in Australia,
while Sheehan and Esposto (2001) used the O*NET to study the characteristics of Australian jobs. The information
that follows is mainly descriptive and draws heavily on the work conducted by Peterson et al. (1995, 1997, 1999,
2001) who were responsible for developing the O*NET. Information is also drawn from the O*NET 98 Viewer (US
Department of Labor, 1998) and the O*NET 98 training materials developed and distributed by the National
Occupational Information Coordinating Committee (NOICC).
3 The O*NET data used in this analysis is from the O*NET 98 version. The reason for this is that newer versions of
O*NET have not upgraded all the data on the variables used here for analysis, which are contained in the first
version of the O*NET 98.
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The occupational information in the O*NET is organised in a relational database which
identifies, defines and describes the comprehensive elements of job performance. It contains
hundreds of information items on job requirements, worker attributes and the content and context
of work, capturing what people do in their day-to-day activities.
The origins of the O*NET go back to the 1993 report to the US Secretary of Labor’s Advisory
Panel on the DOT (APDOT). The report noted that the framework underlying the then current
version of the DOT was not adequate for analysis and description of occupations. This was
because the descriptions of occupations in the DOT were predominantly based on industries of
an earlier era. These descriptors were deficient in dealing with the large shift towards services
and the emerging information industries in the US economy. Furthermore, it was highlighted
that the DOT did not meet the demands of the emerging labour force of the 21st century
(Mumford and Peterson, 1999, p. 22).
The DOT’s shortcomings indicated that it could not be used for rapid assessment of skills,
abilities and other characteristics required by a job family or for showing how skill levels are
changing or may be related, for example, across occupations.
The O*NET was developed with the aim of becoming an information system consisting of a
framework made up of a variety of components. Firstly, it possesses occupational information
that allows jobs to be described in terms of more general descriptors that reflect the modern
labour market. Secondly, the O*NET is closely linked to labour market data which are updated
on a continual basis. Although it was not originally intended for this purpose, an advantage of
the O*NET is that the detailed data collected on worker characteristics and job requirements can
be used to examine and analyse changes in the labour market. Data are gathered on over 200
occupations each year, with the aim of totally upgrading the database every five years (O*NET
Consortium, 2004). As distinct from the DOT, the O*NET contains cross-occupation descriptive
information that includes the kind of work, conditions under which it is done, and the
requirements imposed on the people doing the work. Finally, the O*NET takes into account the
variety of applications of the information that is collected. All the occupations in the database
are related to a common framework that describes job requirements and worker attributes, as
well as the content and context of work using nearly 300 descriptors. All the information
collected in the database is organised into a framework known as the Content Model.
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Table 3. Similarities between O*NET and employability skills
Mayer Key
Competencies
Employability
skills
framework
O*NET Skills O*NET
Knowledge
O*NET Worker
Activities
Collecting,
analysing and
organising
information
Personal
Attributes
Content skills Business and
Management
Looking for
and receiving
job-related
information
Communicating
ideas and
information
Planning and
organisation
Process skills Manufacturing
and Production
Identifying
/evaluating job-
relevant
information
Planning and
organising
activities
Communication Social skills Engineering
and Technology
Information
/data
Processing
Working with
others and in
teams
Planning and
organising
Complex
problem
solving skills
Mathematics
and Science
Reasoning
/decision
making
Using
mathematical
ideas and
techniques
Teamwork Technical skills Health Services Performing
physical and
manual work
activities
Solving
problems
Problem
solving
Systems skills Education and
Training
Performing
complex
/technical
activities
Using
technology
Technology Resource
Management
skills
Arts and
Humanities
Communicating
/interacting
Learning Law and Public
Safety
Coordinating
/developing
/managing
/advising others
Self-
management
Transportation Administering
Source: Author’s compilation.
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3.3 The O*NET Content Model
The Content Model is the conceptual foundation of the O*NET. It was developed by Mumford
and Peterson (1995b), using research on job and organisational analysis, and embodies a
framework that reflects the character of occupations (i.e. using job-oriented descriptors) and
people (i.e. using worker-oriented descriptors). The Content Model also allows occupational
information to be applied across jobs, industry sectors (by using cross-occupational descriptors)
and within occupations (using occupation-specific descriptors). The Content Model classifies
data into six domains that provide detailed information related to the attributes of occupations
and to the characteristics required of people who actually do the job. It includes the specific
domains and elements in the O*NET database that might be used to describe jobs. These
components are based on psychological and job analysis research carried out by the O*NET
consortium.
Figure 1 summarises each of the six domains and their corresponding components. The six
domains are Worker Characteristics, Worker Requirements, Experience Requirements,
Occupation Requirements, Occupation Characteristics and Occupation Specific Information.
The organisation of the Content Model allows the user to concentrate on relevant information
that details the attributes and characteristics of jobs and workers.
3.4 O*NET Variables Used in Forecast Analysis
As noted, distinctive feature of the O*NET’s Content Model is that it provides a detailed
description of occupations in terms of cross-job descriptors. Furthermore, the O*NET contains
data that can assist in measuring and comparing the content of occupations and how these have
changed over time.
Esposto (2005) showed that many of the proxies of skill used in econometric work are not very
effective and leave much to be desired. For example, numerous studies have used years of
education or experience as a proxy for skill, but these two concepts are not necessarily the same
thing. A number of studies of manufacturing have used the ratio of production to non-production
employees as a measure of skill, but replacing skilled tradesmen with salesmen or general office
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Figure 1. Six domains of the O*NET Content Model
O*NET
Experience Requirements Training
Experience
Licensure
Occupation Specific Requirements
Occupational Knowledges
Occupational Skills
Tasks
Duties
Machines, Tools and Equipment
Occupational Requirements
Generalised Work Activities
Work Context
Organisational Context
Worker Requirements
Basic Skills
Cross-Functional Skills
Knowledges
Education
Worker Characteristics
Abilities
Occupation Values and
Interests
Work Style
Occupation Characteristics
Labor Market Information
Occupational Outlook
Wages
Source: Mumford and Peterson (1999, p. 25, Figure 3.2).
staff may reduce rather than increase the overall level of skill in a firm. Other studies have used
earnings as a proxy for skill. Although this has some advantages, it does little to throw light on
the role of skill changes in the pattern of earnings, or to increase our understanding of the
changing nature of skill. In summary, economies and labour markets in the industrialised world
have experienced dramatic changes in the last three decades. As a result, the skill composition of
many occupations may also be changing rapidly, and the measures and proxies for skill used to
date may not provide us with enough insights to comprehend these changes.
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3.5 O*NET Data Collection Method4
Data on the content model’s descriptors were collected from a variety of sources. The most
important source of information was questionnaires collected from job incumbents. A variety of
rating scales was used in order to describe the attribute of the occupation under examination. In
trying to ensure that the data was valid, a wide variety of ratings were utilised, such as level,
importance and frequency. Where possible, moreover, behavioural based rating scales were
employed (Hubbard, et al., 2000).
The data were obtained from a variety of sources. One such source employed incumbent-based
data collection methods in order to minimise the costs of using the analyst based approach.
Second, the O*NET measures were developed in order to extract as much information as
possible from incumbents. This allowed incumbents to provide reliable and accurate
information. The body of knowledge contained in the field of industrial psychology was also
used in order to assess a wide range of job, occupational and organisational characteristics based
on highly reliable questionnaires and other job related information data gathering tools (Peterson,
et. al. 2001).
Information for the model domains was collected by a series of questionnaires completed by job
incumbents. The respondents to O*NET data collections were guided through a series of
questionnaires designed to solicit ratings of jobs or workers represented by the following Content
Model elements, including, worker characteristics, worker requirements, occupational
requirements, occupational specific requirements, and occupational characteristics. For the data
used in this analysis, the questionnaires used several types of rating scales for measuring
frequency, level and importance.
3.6 O*NET Descriptors Used for this Analysis
This section describes in more detail the three O*NET indicators used to investigate changes in
the content of Australian occupations. The three indicators of skill, knowledge and GWAs form
4 For a detailed discussion on data collection methods for the O*NET see Peterson et.al., 1999, Chapters 3 and 4.
This section draws heavily on their work.
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part of the domains of worker characteristics, worker requirements and occupational
requirements described in the previous section.
3.6.1 Skills
The approach taken by O*NET to define skill is that of Mumford and Peterson (1995a). They
define skill as a set of general procedures that underlie the effective acquisition and application
of knowledge in different areas of endeavour (chap. 3, p. 4). The implication of this definition is
threefold. Firstly, skills are innately linked to knowledge, learning, practice, education and
experience. For example, a person cannot acquire or apply skills without learning, practising,
being exposed to education, experiencing or by acquiring knowledge. Secondly, skills can be
seen as general procedures that are necessary for the performance of multiple tasks. These tasks,
however, must form part of a given domain of skills such as social skills, basic skills or problem
solving skills. Finally, skills are not constant attributes of individuals that remain unchanged
over time. These attributes can be acquired (sometimes they can be lost) and developed as a
result of new learning, experience or newly acquired knowledge.
Given the above, Mumford and Peterson (1995a) argue that skills are not one-dimensional and
require a variety of taxonomies. They divide the taxonomy of skill into two broad categories.
The first is referred to as basic skills. These are defined as the developed capacities that facilitate
learning or the attainment of new knowledge. Basic skills are subdivided into two further
categories described as content and process skills. These are made up of six and four skill
variables respectively, out of a total of 46 skills that comprise the complete O*NET skill
taxonomy. Content skills can be broadly defined in terms of those capabilities that allow people
to acquire information and convey it to others. They represent the structures required to work
with and acquire other skills. This category includes skills such as reading, writing, listening,
speaking, mathematics and science. These skills are also widely seen as fundamental in the
provision of any sound educational system.
Process skills, on the other hand, are seen as those skills that facilitate the acquisition of content
across domains. The ability to think critically is thus part and parcel of process skills. This skill
is closely related to a second kind of general learning skill, referred to as active learning.
Another process-oriented skill takes the form of learning strategy. This uses a variety of
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approaches when learning new things. Finally, monitoring represents an ongoing appraisal of
the success of an individual’s efforts because it assists them in assessing how well they are
learning something or doing a particular task. The second classification of skills is defined in the
O*NET as the capacities that facilitate individuals to perform effectively in a variety of job
settings. This skill definition is also known as cross-functional skills and in the O*NET Content
Model is based on the notion of socio-technical systems theory.
3.6.2 Knowledges
The O*NET defines knowledge as a collection of discrete but related facts, information and
principles about a particular area of work. Knowledge is acquired through formal education
and/or training or can be built upon a collection of a variety of experiences. Knowledge is
acquired incrementally and can be used to acquire further knowledge and to develop skills. It is
closely related to skills because it can assist in the development of skills, while, at the same time,
skills can aid the process of knowledge acquisition.
In the O*NET taxonomy, the 33 knowledges can be regarded as belonging to general categories.
Some of these categories are general and are regarded as being essential elements in the
successful performance of occupational tasks. Others are narrower and can only be applied to a
fine range of occupational groups, while others can be seen as being occupation specific.
In identifying a taxonomy of knowledge indicators, Fleishman et al. (1995) developed a
knowledge classification by analysing job descriptors and looking for tasks and behaviours that
were representative of underlying knowledges in occupations. These underlying knowledges had
to be general enough so that they could be transferable across different occupations. Lists of
knowledge descriptors were created and these were further divided into categories or clusters of
knowledges. Fleishman et al. (1995) arrived at a parsimonious set of 33 knowledges.
3.6.3 Generalised work activities (GWAs)
GWAs in the O*NET provide a taxonomic structure that can be used to describe the work that
people do in their particular occupations. They describe the work activities that are needed or
required for successful job performance. The distinction between GWA, knowledge and skills
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lies in the fact that our work activity measures are closely linked to the requirement of
occupations, whereas knowledge and skill are measures that are closely related to requirements
of individuals who perform a given job.
GWAs are useful in the O*NET system because they are closely linked to the knowledge and
skill of individuals and can provide useful strategies when matching jobs to people.
Furthermore, the data that describe and measure each of the 42 GWAs can be used to analyse
changes in the labour market in terms of occupation specific requirements. This information can
also be used to analyse changes in the composition of employment in terms of occupational
requirements and can be compared to changes in the skill and knowledge composition of
Australian occupations.
In the O*NET a GWA is defined as the aggregation of similar job attributes that brings about or
promotes the accomplishment of major work functions and tasks.
3.6.4 An illustrative example of O*NET occupational data
The three O*NET measures described above contain a total of 121 variables which are assigned
to a total of 340 Australian occupations in ASCO 2nd
edition. Conceptualisation of how the data
are organised in the O*NET and what they represent can be confusing without further
explanation. To demonstrate how the O*NET data are organised for each of our measures, we
provide an example using GWA information contained in O*NET5
The GWA taxonomic structure of the O*NET is hierarchically organised and is very detailed.
Each descriptor defines a GWA, such as getting information to do the job, identifying objects,
actions and events, judging quality of things, analysing data or information and thinking
creatively. For a given occupation, each of these areas of GWAs is ranked on a scale of 0 to 100,
for both its importance to the occupation and for the level in that area required in that occupation.
With a rank of zero in the importance scale, the descriptor of GWA shows that the work activity
is of no consequence to that occupation, while at a ranking of 100, the descriptor is considered to
be extremely important in performing that particular job. Thus the ‘importance’ indicator for a
5 In the O*NET, information on skill and knowledge scores is organised in the same way as for GWAs.
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particular GWA refers to how important it is to the performance of the job in question.
Similarly, the ‘level’ indicator for a particular area of GWA refers to the degree or quality which
that work activity requires in the occupation under investigation.
Thus, in the example provided in Table 1, for the occupation ‘generalist medical practitioner’ the
importance of the GWA descriptor ‘monitor processes, materials, surroundings’ is ranked 100,
and the level of ‘monitor processes, materials, surroundings’ required is high, at 89. For
‘librarian’ the same GWA is not as important (score 42 in importance) and the quality required is
even less demanding (score 29 in level). Their most important GWA relates to ‘communicating
with persons outside the organisation’ (importance score 92, whilst its level has a score of 62).
3.6.5 Link between O*NET and Australian Occupations
To analyse changes in the GWA knowledge, and skill intensity of Australian occupations,
Australian employment data were linked to the O*NET occupational data. This was done
because, unfortunately, concordances between ASCO and the O*NET occupational codes do not
exist. But even though in the US concordances are available between the O*NET and
occupational employment data series, the number of occupations covered in the O*NET is
greater than in the employment series. For example, by comparison with about 1,120 O*NET
codes, the most detailed occupational employment data in the US are the Department of Labor’s
Occupational Structure data which cover 820 occupations. Thus, the ‘most appropriate’ O*NET
code was assigned to a particular occupation in the employment data. More specifically, each
Australian occupation was assigned an O*NET occupational code on an individual basis. In
assigning the ‘most appropriate’ O*NET occupation code to an ASCO 2nd
edition occupation
code, it was assumed that there was a similarity in the description of US and Australian
occupations, as the great bulk tend to have similar or close definitions and descriptions.6
6 This assumption has also been made by Pappas (2001) using the DOT and by Sheehan and Esposto (2001) using
the O*NET.
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Table 4. Generalised Work Activity, Importance and Level
Generalised work activities / Occupation Generalist Medical
Practitioner
Librarian Bricklayer
O*NET 32102A O*NET 31502A O*NET 87302
ASCO 2311 ASCO 2292 ASCO 4414
Importance Level Importance Level Importance Level
Monitor processes, materials, surroundings 100 89 42 29 67 26
Identifying objects, actions and events 95 83 58 43 63 29
Analysing data and information 95 89 63 43 54 26
Making decisions and solving problems 95 83 54 38 38 21
Getting information to do the job 95 91 79 60 71 31
Assisting and caring for others 95 91 67 38 8 12
Implementing ideas, programs, etc. 85 74 67 38 67 29
Documenting/recording information 85 57 79 48 4 10
Communicating with persons outside
organisation.
80 71 92 62 8 14
Source: US Department of Labor (1998a) and author’s calculations.
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The assignment of an O*NET occupation to an ASCO 2nd
edition occupation was done by
matching the occupation title from the O*NET to the ASCO 2nd
edition, using the list of
definitions and titles of occupations as described in both the ASCO (ABS cat. no. 1220) and the
O*NET Dictionary of Occupational Titles (1998 edition). The assignment of occupations
involved searching for an occupation title in the ASCO 2nd
edition and then finding its
corresponding title in the O*NET database. This was done for a total of 340 ASCO 2nd
edition
occupations.
3.6.6 Applications of O*NET in Labour Market Research
As noted, the O*NET is an occupational database which offers an important resource that can be
applied in the study of labour market change. Given its recent and continuing development a
number of studies have used the O*NET to study and analyse the labour market, to identify
occupational skill requirements and to provide forecast of short-term demand for skills.
One of the first studies to use the O*NET database was conducted by the Minnesota Department
of Economic Security (MDES). The report titled “Minnesota’s Most Marketable Skills”, (1999)
identified the occupational skill requirements considered to be most marketable. Marketable
skills were defined as those occupational requirements that are meant to be associated with high
wages and/or employment growth. The report found that out of 57 occupational skill
requirements that measure the knowledge, ability and skill dimensions of an occupation, 18 were
found to be extremely ‘marketable’. The finding of this study were used by the State of
Minnesota to apply to areas of policy related both to the labour market and to align the skill
requirement of occupations to education curricula.
In 2002, the MDES conducted a forecast of the demand for skills in the short term, using 46
skills, 52 abilities, 33 knowledge and 38 generalised work activity measures found in the O*NET
database. The report summarised research into the feasibility and appropriate methodology for
developing short-term, skill-based forecasts in order to direct public resources more effectively.
The report concluded that the O*NET was an effective data system that could be applied for the
analysis of forecasts of short-term demand for skills.
Esposto (2005) applied the O*NET database to study labour market change in Australia.
Drawing on earlier work conducted by Sheehan and Esposto (2001), Esposto (2005) investigated
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labour market change using the O*NET by addressing the following issues. Firstly, the study
focused on how skill should best be measured in addressing the issue of skill-bias in the demand
for labour; secondly, he showed that the Australian labour market had experienced a long-term
process of skill-bias in the demand for labour; and thirdly having confirmed skill-bias, the study
showed using O*NET measures of skill and knowledge evidence that this increasing relative
demand for higher skill labour is an important explanatory factor in the rise in earnings
inequality in Australia.
Another application of the O*NET measures was used in a study designed to identify
occupations that are comparable in earnings to primary and post-primary teachers in the US.
Using the generalised work activities, basic and cross-functional skills and educational level
scales embodied in the O*NET, Milanowski (2003) performed a variety of cluster analysis
methods, and was able to identify comparable work activity and skills for US occupations.
Rotundo and Sackett (2004) using the O*NET database conducted a job-level evaluation of
whether specific skills or abilities could be identified that were most strongly linked to wage or
whether broad skill/ability factors accounted for a majority of wage variance. The authors found
that a majority of the wage variance explainable by skills/abilities could be attributed to a general
cognition factor.
4. The Monash Forecasting System
The demand for labour depends on many factors. It depends on the state of macroeconomic
health of the domestic economy and of the economies of trading partners. It depends on the
amount of capital investment and on its allocation between industries. It depends on the rate of
technical change and on changes in government policy. Moreover, all these factors are
interconnected. Developments in one industry affect the demand for labour in other industries.
The Monash Forecasting System (MFS) incorporates all these factors in a set of formal
economy-wide forecasts for labour demand.
The elements of the MFS are set out in Figure 2. As a formally specified system, its role is to
supply a framework for incorporating relevant data into the forecasting framework. Published
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data accessed by the system include the national accounts, input/output tables, State accounts,
population censuses, foreign trade statistics, capital stock statistics, and income and expenditure
surveys. Additional unpublished material is prepared by the Australian Bureau of Statistics
especially for the system. Moreover the MFS requires all its data to be consistent. If any
inconsistencies do exist in the primary sources, they must be reconciled before the data can be
included. This consistency requirement makes the system especially powerful as a framework
for organising data.
As well as data about the past, formal or model-based forecasts usually rest upon informed
opinion about future changes in variables that are exogenous to (i.e., determined outside) the
system. The MFS is quite adaptable in this regard. It incorporates the views of numerous expert
bodies and can accommodate more detailed exogenous forecasts as they become available. The
sources of exogenous forecasts identified in Figure 2 are:
the private forecasting agency, Access Economics (which contributes information about the
future state of the macro economy),
The Australian Bureau of Agricultural and Resource Economics (export prices and volumes
for primary products),
The Tourism Forecasting Council (prospects for tourism),
The Productivity Commission (changes in protection implied by government industry
policy), and
The Centre of Policy Studies (changes in technology and consumer tastes).
The system can also produce alternative forecasts corresponding to competing views about the
future. Just as for historical data, all opinions formally incorporated in a particular forecast must
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Macro
Forecasts
(ACCESS)
Industry
Policy (PC)
Exports
(ABARE &
TFC)
MONASH
simulations
2004-05
to
2012-13
Structure of
Technical & Taste
Changes (CoPS)
Output & Employment Forecasts for
158 Industries
Occupational Share
Effects (CoPS)Labour Market Extension I
Employment Forecasts for
340 Occupations
Qualification Share
Effects (CoPS)Labour Market Extension II
Employment Forecasts for
8 Qualification Levels, 72
Qualification Felds
Figure 2. The Monash Forecasting System
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be consistent with each other. A forecaster using the MFS must either seek a consensus between
the expert bodies involved in forecasting the exogenous variables or impose his/her own
judgement to resolve any outstanding differences before the forecast can proceed. In other
words, the MFS provides a framework for coordinating both historical data and expert opinion
about the future that bear on the future demand for labour.
An MFS forecast of the demand for labour proceeds in five stages. It begins with a
macroeconomic scenario derived from the Business Outlook published quarterly by Access
Economics7. Table 5 compares the current scenario with recent history for a selection of macro
variables. During the eight years of the forecast period (2004-05 to 2012-13), Access expects
annual GDP growth to be 0.37 percentage points lower than the 3.61 per cent achieved on
average during the preceeding eight years. Furthermore, the contribution of exports to GDP
growth is expected to increase at the expense of private consumption and investment. The
balance of trade deficit is forecast to decline.
The second stage in the process is to convert the forecasts for GDP and its components into
forecasts of output and employment8 by industry. The structural forecasts supplied by the expert
bodies indicated in Figure 2 are incorporated at this stage. In particular, the array of exogenous
information is treated as a set of constraints which governs a simulation using the MONASH
applied general equilibrium model in forecast mode. Results of the simulation for 17 industries
are shown in Table 6. Among the industries with good output growth prospects, Mining,
Wholesale Trade and Education are strong participants in the forecast rapid growth in exports.
Wholesale Trade also benefits because it provides margins on the exports of other industries.
Finance and insurance and Property and business services, on the other hand, are favoured by
changes in technology which result in their outputs being used more intensively by other
industries. The poor growth prospects for the Construction industry reflect a slump in
residential construction forecast for 2009-10 by Access Economics. Personal Services suffers
because it is very labour intensive and has little scope for improvements in labour productivity.
Hence, as real wage rates are forecast to increase at about 0.4 per cent per annum, the output of
the industry tends to become relatively expensive and households switch their expenditure in
7 The forecasts presented in the paper are based on the September 2005 edition. 8 In what follows, we implicitly assume that employment is demand determined and refer to employment when,
strictly speaking, we mean the demand for labour.
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Table 5. Average Annual Growth Rates, Selected Macro Variables
Australia, Per Cent Per Annum
Historical Forecast
Variable Data
1996-97 2004-05
to to
2004-05 2012-13
1 Private consumption 4.14 2.86
2 Private residential construction 5.78 1.30
3 Private non-residential construction 6.20 2.11
4 Total private investment 6.87 2.59
5 Government investment 4.91 2.97
6 Total investment 6.68 2.62
7 Government consumption 3.19 2.45
8 Exports 3.06 7.61
9 Imports 8.05 4.82
10 Gross domestic product 3.61 3.24
favour of other industries. Government expenditure, on the other hand, is expected to shift in
favour of Public Administration and Defence during the forecast period. Hence the prospects
for Health and community services and Cultural and recreational services are commensurately
reduced.
The output and employment forecasts are related by production functions which determine the
increase in output associated with given increases in inputs (capital and labour) and a given rate
of primary factor saving technical change. The influence of capital growth and technical change
can produce quite different output and employment forecasts for some industries. The change in
capital inputs depends critically on whether an industry was under- or over-capitalised in the
base period of the forecast (i.e., on whether the rate of return in the industry was above or below
the average across industries). An industry with a relatively high rate of return attracts
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Table 6. Forecast Changes in Output and Employment, ANZSIC Divisions, Australia, 2004-05 to 2012-13
Output (Constant Prices) Employment (Persons)
Growth Rate Growth Share Growth Rate Growth Share
(per cent per annum) (per cent) (per cent per annum) (per cent)
A00 Agriculture forestry and fishing 3.13 3.35 0.30 0.86
B00 Mining 5.10 8.44 2.72 2.49
C00 Manufacturing 2.60 9.93 0.78 6.77
D00 Utilities 2.88 2.37 -1.05 -0.60
E00 Construction 1.56 3.38 -0.15 -0.99
F00 Wholesale trade 3.92 7.37 1.70 6.20
G00 Retail trade 2.74 5.50 1.58 19.46
H00 Accommodation cafes restaurants 2.73 2.01 0.83 3.37
I00 Transport and storage 3.52 6.92 1.33 4.96
J00 Communications 3.29 3.42 -1.59 -2.12
K00 Finance and insurance 4.09 12.28 0.72 2.07
L00 Property and business services 4.03 16.12 2.10 19.92
M00 Public administration and defence 2.87 4.13 1.57 5.81
N00 Education 3.64 5.82 2.88 16.86
O00 Health and community services 2.50 5.55 1.15 9.35
P00 Cultural and recreational services 2.40 1.51 0.55 1.15
Q00 Personal and other services 2.33 1.87 1.39 4.43
999 All industries 3.24 100.00 1.25 100.00
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investment and enjoys a relatively high rate of capital growth. For a given rate of output growth
and technical change, this implies a relatively low rate of employment growth. Similarly an
industry with a relatively rapid rate of technical change will tend to have a relatively low rate of
growth in employment. Thus Communications, which has better than average prospects for
output growth, has poor employment prospects because it is expected to experience particularly
rapid growth in labour productivity.
At the third stage, the national forecasts for output and employment are converted into regional
forecasts using the MONASH regional equation system (MRES), a derivative of the ORANI
regional equation system (Dixon et al., 1982, Chapter 6). The regionalisation process takes
account of:
differences in industrial structures,
region-specific industry effects, such as mine closures,
population movements,
expected expenditures by regional governments, and
local multipliers.
Regional forecasts are produced at two levels of aggregation, namely, eight State and Territories
and 56 Statistical Divisions.
At the fourth stage, the employment forecasts are converted from an industry basis to an
occupational basis. National results for nine occupations are reported in Table 7. As the table
indicates , employment growth (measured in persons) for a particular occupation can be
decomposed into:
• a component due to the growth in aggregate employment (measured in hours),
• a component (the industry share effect) due to changes in the distribution between industries,
• a component (the occupational share effect) due to changes in the distribution of
employment between occupations within industries, and
• a component due to changes in the number of hours per worker.
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The forecast for aggregate employment in hours is 1.14 per cent per annum. The industry share
effects are computed from the growth rates in employment by industry using an industry-by-
occupation matrix derived from the Population Census and the Labour Force Survey. The
occupational share effects are treated as a type of technical change and are forecast by
extrapolating historical trends in the occupational mix in each industry. The method is described
in detail in Meagher (1997). Changes in the number of hours per worker in an occupation are
also derived by extrapolating past trends.
At the final stage, the forecasts for employment by occupation in persons are used to determine
thew employment outlook for workers identified by age, sex, qualifications and hours worked
per week. The method is analogous to that used to determine the occupational forecasts from
the industry forecasts.
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Table 7. Contributions to Employment Growth, Australia, ASCO Major Groups, 2004-05 to 2012-13, Per Cent Per Annum
Growth Industry Occupational Total Hours Total
Effect Share Share Growth Effect Growth
(hours) Effect Effect (Hours) (persons)
1 Managers & Administrators 1.14 -0.43 1.46 2.17 0.17 2.34
2 Professionals 1.14 0.57 0.30 2.01 0.14 2.15
3 Associate Professionals 1.14 -0.01 0.61 1.74 0.29 2.03
4 Tradespersons & Related Workers 1.14 -0.53 -0.86 -0.25 0.09 -0.15
5 Advanced Clerical & Service Workers 1.14 0.08 -1.71 -0.49 0.36 -0.12
6 Intermediate Clerical, Sales & Service Workers 1.14 0.04 0.12 1.30 0.09 1.40
7 Intermediate Production & Transport Workers 1.14 -0.01 -0.45 0.68 0.14 0.82
8 Elementary Clerical, Sales & Service Workers 1.14 0.15 -0.85 0.44 0.33 0.77
9 Labourers & Related Workers 1.14 -0.12 -0.92 0.10 0.08 0.19
10 All Occupations 1.14 0.00 0.00 1.14 0.11 1.25
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5. Forecasting Employability Skills
As adapted by Esposto, the O*NET assigns an “importance” indicator between 0 and 100
(inclusive) to each of 46 skill groups for each of 340 occupations (i.e., the ASCO unit groups).
To extend the MFS forecasts of employment by occupation (measured in hours) to apply to the
O*NET skill groups, we assume that the number of hours worked in an occupation is distributed
between the skill groups in proportion to the importance indicator for that occupation. More
formally, let Aik be the value of the importance indicator for skill type i in occupation k. Then
į1 Ȉi Aik = Ek ,
where Ek is total employment in occupation k and į1 is a scale factor. That is, employment of
skill type i in occupation k is given by į1 Aik when measured in hours.
We further assume that the number of efficiency units delivered per hour by labour of skill type i
in occupation k is proportional to the corresponding O*NET skill “level” indicator. Let Bik be the
value of the level indicator for skill type i in occupation k. Then
į2 Ȉi Bik (į1 Aik ) = Wk ,
where Wk is total employment (measured in efficiency units) for occupation k and į2 is a scale
factor. That is, employment of skill type i in occupation k is given by į1 į2 Aik Bik when
measured in efficiency units. Wk might reasonably be set equal to the wage bill in occupation k
but the size of an efficiency unit is essentially arbitrary.
Similar assumptions are adopted for the 32 O*NET knowledges and the 42 O*NET work
activities.
Clearly, the methodology for forecasting the demand for employability skills (as represented by
the O*NET skills, knowledges and work activities) is somewhat less robust than that for the
other variables of the MFS. In particular, the conversion of occupational forecasts into forecasts
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for employability skills is not based on specific information (such as that collected in the Labour
Force Survey) about the relationship between the two, but on opinion (embodied in the O*NET
importance and levels indicators) about what that relationship might be. However, the method
is plausible, the opinion involved is expert opinion , and there is no obvious alternative method
by which it can be taken into account. Certainly, it is not possible to convincingly accommodate
matrices of the size required (i.e., 340 by 46 in the case of the O*NET skills) by means of
intuition.
Table 8 presents results for the 46 O*NET skill groups. The forecasts of employment growth in
hours range from 0.57 per cent per annum for 601 Visioning to 1.59 per cent for 703
Management of material resources. This range (1.02 percentage points) is substantially less than
that for the nine major ASCO occupations shown in Table 7 (2.42 percentage points). At the
ASCO unit group level (340 occupations), the range is much larger still, extending from –11.11
per cent per annum for 4943 Footwear tradespersons to +6.66 per cent for 2429 ESL Teachers.
This is consistent with the generic character of the O*NET categories which implies a growth
rate which is an average of the growth rates for the relevant occupations. Nevertheless, the
growth rate forecast for Management of material resources is nearly three times that for
Visioning and substantial variation remains.
The average annual growth rate of 1.59 per cent for Management of material resources translates
into a total growth rate of 13.45 per cent over the eight years of the forecast period. Table 9
shows the contributions made by each of the nine ASCO major groups to total employment
growth. From the first row of the table, the contribution of Managers and Administrators is
given by 0.197 x (18.74 + 1.60) or 4.01 percentage points. A substantial part of the hours spent
performing the skill Management of material resources is contributed by workers belonging to
the occupation Managers and administrators (19.7 per cent in the base period). Over the
forecast period, the employment of Managers and administrators is forecast to increase more
rapidly than average employment (18.74 per cent as compared to 9.52 per cent). Specifically,
the growth of the occupation contributes (0.197 x 18.74) or 3.69 percentage points to the growth
of the skill group (the shift effect). Moreover, Management of material resources is forecast to
increase its share of employment within the occupation Managers and administrators at the
expense of other skill groups, contributing an additional (0.197 x 1.60) or 0.32 percentage points
(the share effect). Because the O*NET “importance” indicators are assumed to remain constant
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Table 8. Average Annual Growth Rates, O*NET Skill Groups
Australia, Hours, Per Cent Per Annum
Historical Forecast
Skill Group Data
1996-97 2004-05
to to
2004-05 2012-13
100 Content Skills 1.56 1.11
101 Reading and comprehension 1.50 1.08
102 Active listening 1.61 1.11
103 Writing 1.58 1.16
104 Speaking 1.68 1.20
105 Mathematics 1.54 1.07
106 Science 1.38 0.98
200 Process Skills 1.65 1.19
201 Critical thinking 1.71 1.24
202 Active learning 1.70 1.23
203 Learning strategies 1.63 1.21
204 Monitoring 1.57 1.11
300 Social Skills 1.68 1.20
301 Social perceptiveness 1.68 1.20
302 Coordination 1.69 1.14
303 Persuasion 1.76 1.29
304 Negotiation 1.78 1.27
305 Instructing 1.62 1.23
306 Service orientation 1.61 1.12
400 Complex Problem Solving Skills 1.61 1.15
401 Problem identification 1.59 1.14
402 Information gathering 1.58 1.14
403 Information organisation 1.46 1.06
404 Synthesis/Reorganisation 1.63 1.17
405 Idea generation 1.70 1.21
406 Idea evaluation 1.66 1.18
407 Implementation planning 1.67 1.20
408 Solution appraisal 1.62 1.17
…continued
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Table 8 (continued). Average Annual Growth Rates, O*NET Skill Groups
Australia, Hours, Per Cent Per Annum
Historical Forecast
Skill Group Data
1996-97 2004-05
to to
2004-05 2012-13
500 Technical Skills 1.37 0.91
501 Operations analysis 1.62 1.14
502 Technology design 1.46 0.97
503 Equipment selection 1.38 0.88
504 Installation 1.37 0.78
505 Programming 1.57 1.12
506 Testing 1.33 0.93
507 Operation monitoring 1.34 0.96
508 Operation and control 1.28 0.87
509 Product inspection 1.25 0.88
510 Equipment maintenance 1.38 0.97
511 Troubleshooting 1.11 0.62
512 Repairing 1.27 0.81
600 Systems Skills 1.87 1.29
601 Visioning 1.10 0.57
602 Systems perception 1.95 1.38
603 Identifying downstream consequences 2.04 1.46
604 Identification of key causes 2.04 1.45
605 Judgement and decision making 1.93 1.34
606 Systems evaluation 1.93 1.30
700 Resource Management Skills 2.11 1.43
701 Time management 2.20 1.53
702 Management of financial resources 1.99 1.35
703 Management of material resources 2.34 1.59
704 Management of personnel resources 1.99 1.30
9999 All Skill Groups 1.63 1.14
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over time, the share effects for the 340 ASCO unit groups are zero. The share effects identified
here reflect a change in the mix unit groups within each major group.
When the occupational contributions are compared for the skill groups Management of material
resources and Visioning, it is clear that the former owes its relatively good employment
prospects to its concentration in the managerial and professional occupations, and the latter owes
its relatively poor prospects to its concentration in the trades and labouring occupations.
Tables 10 and 11 present the forecasts for the O*NET knowledge groups and work activities.
They can be understood in the same way as Table 8. The final table, Table 12, compares the
forecasts in hours and efficiency units.
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Table 9. Contributions to Employment Growth, Australia, Selected O*NET Skill Groups, 2004-05 to 2012-13
Shift Effect (Per Cent) Share Effect (Per Cent) Contribution
Skill Group / Occupation Employment
hare
(Percentage
nts) S Poi
2004-05 Average Total Average Total
Annual Annual
703 Management of Material Resources
1 Managers & Administrators 0.197 2.17 18.74 0.20 1.60 4.01
2 Professionals 0.214 2.01 17.23 0.26 2.10 4.14
3 Associate Professionals 0.232 1.75 14.88 0.28 2.25 3.97
4 Tradespersons & Related Workers 0.068 -0.24 -1.90 -0.25 -1.99 -0.26
5 Advanced Clerical & Service Workers 0.048 -0.48 -3.80 0.67 5.52 0.08
6 Intermediate Clerical, Sales & Service Workers 0.115 1.31 10.94 -0.31 -2.43 0.98
7 Intermediate Production & Transport Workers 0.075 0.68 5.55 -0.04 -0.32 0.40
8 Elementary Clerical, Sales & Service Workers 0.034 0.44 3.55 -0.23 -1.80 0.06
9 Labourers & Related Workers 0.017 0.10 0.83 0.56 4.54 0.09
10 All Occupations 1.000 13.45
602 Visioning
1 Managers & Administrators 0.059 2.17 18.74 -0.36 -2.84 0.94
2 Professionals 0.087 2.01 17.23 -0.36 -2.84 1.24
3 Associate Professionals 0.077 1.75 14.88 -0.50 -3.90 0.85
4 Tradespersons & Related Workers 0.281 -0.24 -1.90 -0.10 -0.77 -0.75
5 Advanced Clerical & Service Workers 0.010 -0.48 -3.80 -0.88 -6.80 -0.11
6 Intermediate Clerical, Sales & Service Workers 0.081 1.31 10.94 0.86 7.07 1.46
7 Intermediate Production & Transport Workers 0.165 0.68 5.55 -0.28 -2.19 0.56
8 Elementary Clerical, Sales & Service Workers 0.079 0.44 3.55 0.23 1.89 0.43
9 Labourers & Related Workers 0.162 0.10 0.83 -0.07 -0.54 0.05
10 All Occupations 1.000 4.66
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Table 10. Average Annual Growth Rates, O*NET Knowledge Groups
Australia, Hours, Per Cent Per Annum
Historical Forecast
Knowledge Groups Data
1996-97 2004-05
to to
2004-05 2012-13
100 Business and Management 1.78 1.29
101 Administration and management 2.00 1.42
102 Clerical 1.44 1.12
103 Economics and accounting 1.82 1.38
104 Sales and marketing 1.87 1.34
105 Customer and personal service 1.76 1.20
106 Personnel and HR 1.86 1.30
200 Manufacturing and Production Skills 1.28 1.00
201 Production and processing 1.38 1.04
202 Food production 1.14 0.93
300 Engineering and Technology 1.45 0.90
301 Computers and electronics 1.55 1.16
302 Engineering and technology 1.48 0.92
303 Design 1.43 0.88
304 Building and construction 1.55 0.73
305 Mechanical 1.25 0.78
400 Mathematics and Science 1.61 1.12
401 Mathematics 1.67 1.19
402 Physics 1.40 0.91
407 Chemistry 1.42 0.93
404 Biology 1.35 0.98
405 Psychology 1.88 1.31
406 Sociology and anthropology 1.81 1.28
407 Geography 1.65 1.13
500 Health services 1.73 1.18
501 Medicine and dentistry 1.62 1.09
502 Therapy and counselling 1.83 1.27
600 Education and Training 1.90 1.42
…continued
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Table 10. (continued). Average Annual Growth Rates, O*NET Knowledge Groups
Australia, Hours, Per Cent Per Annum
Historical Forecast
Knowledge Group Data
1996-97 2004-05
to to
2004-05 2012-13
700 Arts and Humanities 1.63 1.17
701 English language 1.70 1.23
702 Foreign language 1.63 1.15
703 Fine arts 1.55 1.09
704 History and archaeology 1.59 1.16
705 Philosophy and theology 1.56 1.13
800 Law and Public Safety 1.72 1.17
801 Public safety and security 1.66 1.09
802 Law, government and jurisprudence 1.79 1.24
900 Communications 1.66 1.20
901 Telecommunications 1.57 1.13
902 Communications and media 1.75 1.26
1000 Transportation 1.51 1.08
9999 All Knowledge Groups 1.63 1.14
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Table 11. Average Annual Growth Rates, O*NET Work Activities
Australia, Hours, Per Cent Per Annum
Historical Forecast
Work Activity Data
1996-97 2004-05
to to
2004-05 2012-13
100 Looking for/Receiving Job Related Information 1.63 1.13
101 Getting information needed to do the job 1.61 1.11
102 Monitor processes, material, surroundings 1.66 1.16
200 Identifying/Evaluating Job Related information 1.62 1.08
201 Identifying objects, actions and events 1.63 1.10
202 Inspecting equipment, structures, material 1.51 0.96
203 Estimating needed characteristics 1.71 1.16
300 Information/Data Processing 1.64 1.19
301 Judging quality of things, service, people 1.64 1.15
302 Evaluating information against standards 1.57 1.14
303 Processing information 1.59 1.19
304 Analysing data or information 1.77 1.28
400 Reasoning/Decision Making 1.77 1.27
401 Making decisions and solving problems 1.73 1.23
402 Thinking creatively 1.71 1.28
403 Updating and using job related knowledge 1.72 1.25
404 Developing objectives and strategies 1.95 1.43
405 Scheduling work activities 1.87 1.32
406 Organising, planning and prioritising 1.69 1.19
500 Performing Physical and Manual Work Activities 1.40 0.91
501 Performing general physical activities 1.45 0.82
502 Handling and moving objects 1.32 0.82
503 Controlling machines and processes 1.28 0.83
504 Operating vehicles or equipment 1.60 1.25
600 Performing Complex Technical Activities 1.49 1.03
601 Interacting with computers 1.40 0.97
602 Drafting and specifying technical devices, etc 1.56 0.99
603 Implementing ideas, programs, etc 1.63 1.15
604 Repairing and maintaining mechanical equipment 1.20 0.75
605 Repairing and maintaining electrical equipment 1.36 0.90
606 Documenting/Recording information 1.58 1.18
…continued
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Table 11 (continued). Average Annual Growth Rates, O*NET Work Activities
Australia, Hours, Per Cent Per Annum
Historical Forecast
Work Activity Data
1996-97 2004-05
to to
2004-05 2012-13
700 Communicating with Others 1.75 1.26
701 Interpreting meaning of information to others 1.78 1.31
702 Communicating with other workers 1.79 1.26
703 Communicating with persons outside organisations 1.72 1.26
704 Establishing and maintaining relationships 1.73 1.24
705 Assisting and caring for others 1.62 1.14
706 Selling or influencing others 1.86 1.35
707 Resolving conflicts, negotiating with others 1.91 1.37
707 Performing for/working with public 1.62 1.17
708 Coordinating work and activities for others 1.61 1.11
800 Coordinating/Developing/Managing/Advising 1.62 1.10
801 Developing and building teams 1.51 0.96
802 Teaching others 1.57 1.14
803 Guiding, directing and motivating subordinates 1.73 1.23
804 Coaching and developing others 1.95 1.43
805 Provide consultation and advice to others 1.45 0.82
806 Performing administrative activities 1.60 1.25
900 Administering 1.60 1.19
901 Staffing organisational units 1.63 1.15
902 Monitoring and controlling resources 1.58 1.18
9999 All Work Activities 1.63 1.14
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Table 12. Average Annual Growth Rates, O*NET Categories
Australia, Per Cent Per Annum
O*NET Category Hours Efficiency
Units
Skill Groups
1.31 100 Content Skills 1.11
1.52 200 Process Skills 1.19
1.51 300 Social Skills 1.20
1.47 400 Complex Problem Solving Skills 1.15
0.90 500 Technical Skills 0.91
1.59 600 Systems Skills 1.29
1.71 700 Resource Management Skills 1.43
9999 All Skill Groups 1.14 1.39
Knowledge Groups
100 Business and Management 1.29 1.64
200 Manufacturing and Production Skills 1.00 1.12
300 Engineering and Technology 0.90 0.76
400 Mathematics and Science 1.12 1.34
500 Health services 1.18 1.44
600 Education and Training 1.42 1.93
700 Arts and Humanities 1.17 1.53
800 Law and Public Safety 1.17 1.40
900 Communications 1.20 1.44
1000 Transportation 1.08 1.16
9999 All Knowledge Groups 1.14 1.37
Work Activities
100 Looking for/Receiving Job Related Information 1.13 1.41
200 Identifying/Evaluating Job Related information 1.08 1.21
300 Information/Data Processing 1.19 1.51
400 Reasoning/Decision Making 1.27 1.64
500 Performing Physical and Manual Work Activities 0.91 0.81
600 Performing Complex Technical Activities 1.03 1.18
700 Communicating with Others 1.26 1.56
800 Coordinating/Developing/Managing/Advising 1.10 1.19
900 Administering 1.19 1.20
9999 All Work Activities 1.14 1.34
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6. Concluding Remarks
The Australian economy is currently experiencing a period of tight labour markets.
Because of population ageing, the future growth in labour supply is likely to be relatively
slow. In these circumstances, the allocation of training resources becomes particularly
important. They should be allocated in favour of the skills, be they job-specific or generic,
which are otherwise most likely to be in short supply.
Quantitative labour market forecasting is fraught with uncertainty. However, the question
of allocation is in essence a quantitative question. Eventually, someone has to decide what
courses should be provided and that decision, of its nature, should be informed by a view
about the future. Qualitative analyses, such as the Employability Skills for the Future
report, may inform such a view but eventually the qualitative ideas must be assigned
concrete form. If the quantification of the allocation decision is not based on formal labour
market forecasts, it will of necessity be based on informal or intuitive forecasts and there is
no reason to suppose the former will be more uncertain than the latter. Indeed, formal
methods provide a framework for bringing large amounts of relevant information to bear in
a coherent manner, an achievement that lies outside the range of informal methods. In any
case, the two are not mutually exclusive and a decision maker can consider quantitative
forecasts as simply one of a number of information sources to be taken into account.
When it comes to generic or employability skills, the level of quantitative uncertainty
increases. Generic skills are generally not as tightly defined as job-specific skills, and
hence their economic role cannot be easily elicited by the usual method of surveying
market participants. However, by surveying expert opinion, the U.S. O*NET purports to
specify important quantitative aspects of that role in the form of the “importance” and
“level” indicators it associates with its range of generic skills. As there is no alternative
data source against which the O*NET assignments can be compared, their efficacy must be
judged on the basis of the methodology adopted. The O*NET approach seems to be
appropriate to the task and to have been thoroughly implemented. It can reasonably be
argued, therefore, that the indicators embody important information with which to
supplement the quite extensive qualitative discussion of employability skills in Australia
and elsewhere.
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One purpose of economic research is to develop analytical tools with which to furnish
better information to economic policy makers. The modelling system described in the
paper meets this criterion. The O*NET indicators have been interpreted in a way that is
both in the spirit of their definition and allows existing Australian forecasts of employment
by occupation to be extended to employability skills. The exercise suggests that the
structural details of the future state of the economy do indeed have important implications
for the relative demands for various types of employability skills, and that general
qualitative considerations provide only an incomplete basis for allocating training
resources between those skills. The forecasting methodology has been adapted from a U.S.
database but is amenable to future refinement to incorporate the views of relevant
Australian experts.
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References
Allen Consulting Group, 2004, Employability Skills: Development of a Strategy to Support the Universal
Recognition and Recording of Employability Skills: A Skills Portfolio Approach, Report prepared to
the Australian National Training Authority.
Allen Consulting Group, 1999, Training to Compete: The Training Needs of Industry, Report prepared to the
Australian Industry Group.
Australian Education Council. Finn Review Committee, 1991, Young People’s Participation in Post-
Compulsory Education and Training, Report of the Australian Education Review Committee,
Canberra, AGPS.
Australian Council for Education Research (ACER), 2001, Employment skills for Australian Industry:
Literature Review and Framework Development, Report to BCI and ACCI.
Department of Education and Training (DEST), 2002, Employability Skills for the Future, Commonwealth of
Australia.
Department for Employment and Training, 2005, Skills for Jobs Growth: Queensland’s Proposed Responses
to the Challenges of Skills for Jobs Growth, A Green Paper, Queensland Government.
Drucker, P., 1994, ‘Jobs and Employment in a Global Economy’, Atlantic Monthly, 1, 132-136.
Dixon, P.B., B.R.Parmenter, J.Sutton, and D.P.Vincent (1982), ORANI: A Multisectoral Model of the
Australian Economy, North-Holland, Amsterdam.
Esposto, A., 2005, Dimensions of Earnings Inequality in Australia, PhD Thesis, Victoria University of
Technology.
Hubbard, M., McCoy, R., Campbell, J., Nottingham, J., Lewis, P., Rivkin, D., Levine, J., 2000, ‘Revision of
O*NET Data Collection Instruments’ National O*NET Consortium. Available at
http://www.onetcenter.org/dl_files/Data_appnd.pdf, accessed 20 November, 2005.
Meagher, G.A., (1997), “Changes in the Demand for Labour in Australia”, in Changing Labour Markets:
Prospects for Productivity Growth, Industry Commission, Melbourne.
Milanowski, A., 2003, ‘Using Occupational Characteristic Information from O*NET to Identify Occupations
for Compensation Comparisons with K-12 Teachers’, Working Paper no. TC-03-06, Consortium for
Policy Research in Education, University of Wisconsin-Madison.
Minnesota Department of Economic Security, (MDES), 2002, ‘Forecasting Short-Term Demand for Skills’,
http://www.mnworkforcecenter.org/lmi/pub1/mms/,
http://www.deed.state.mn.us/lmi/__shared/assets/forecasting_short_term15579.pdf accessed
10/03/2005.
Minnesota Department of Economic Security, (MDES), 2000, ‘Minnesota’s Most Marketable Skills’,
http://www.mnworkforcecenter.org/lmi/pub1/mms/, accessed 05/11/2005.
Neugart, M., and K.Schomann (eds), 2002, Forecasting Labour Markets in OECD Countries, Edward Elgar,
Cheltenham, UK.
Occupational Information Network, O*NET Consortium, 2006, O*NET Data Collection,
http://www.onetcenter.org/dataCollection.html, accessed 28/02/2006.
Occupational Information Network, O*NET Consortium, 2004, O*NET Data Collection,
http://www.onetcenter.org/dataCollection.html, accessed 05/04/2004.
44
![Page 43: THE FUTURE DEMAND FOR EMPLOYABILITY SKILLS: A NEW](https://reader033.vdocument.in/reader033/viewer/2022052521/628c961f1dc6cf14876779dc/html5/thumbnails/43.jpg)
OECD (Organisation for Economic Cooperation and Development, 1996, Technology, Productivity and Job
Creation, Paris.
Pappas, N., 2001, ‘Earnings Inequality and Skill’, in Borland, J., Gregory, B., and Sheehan, P. (eds), Work
Rich, Work Poor: Inequality and Economic Change in Australia, Centre for Strategic Economic
Studies, Victoria University, Melbourne.
Peterson, N., Borman, W, Jeanneret, P., Mumford, M., Fleishman, E., Levin, K., Campion, M., Mayfield, M.,
Morgeson, F., Peralman, K., Gowing, M., Lancaster, A, Silver, M., and Dye, D, 2001,
Understanding Work Using the Occupational Network (O*NET): Implications for Practice and
Research, Personnel Psychology, vol. 54, pp. 451-492.
Peterson, N., Borman, W., Jeanneret, P., Fleishman, E., Levin, K., Campion, M., Mayfield, M., Morgeson, F.,
Pearlman, K., Gowing, M., Lancaster, A., Silver, M and Dye, D., 2001, ‘Understanding Work Using the
Occupational Information Network (O*NET): Implications for Practice and Research’ Personnel
Psychology, 54, pp. 451-492.
Peterson, N., Mumford, M., Borman, W., Jeanneret, P. and Fleishman, E. (eds) 1999, An Occupational
Information System for the 21st Century: The Development of the O*NET, Utah Department of
Employment Security, Salt Lake City.
Peterson, N., Mumford, M., Borman, W., Jeanneret, P. and Fleishman, E. (eds) 1997, O*NET Final
Technical Report (Vols 1 and 2), Utah Department of Employment Security, Salt Lake City.
Peterson, N., Mumford, M., Borman, W., Jeanneret, P. and Fleishman, E. (eds) 1995, Development of
Prototype Occupational Information Network (O*NET) Content Models (Vols 1 and 2), Utah
Department of Employment Security, Salt Lake City.
Reich, R., 1992, The Work of Nations: Preparing Ourselves for 21st Century Capitalism, New York,: Vintage
Books.
Rotundo, M., and Sackett, P., 2004, ‘Specific Versus General Skills and Abilities: A Job Level Examination
of Relationships with Wage’, Journal of Occupational and Organizational Psychology, 77, 127-148.
Sheehan, P. and Esposto, A., 2001, ‘Technology, Skills and Earnings Inequality: A New Approach to
Understanding the Characteristics of Jobs’, in Borland, J., Gregory, B. and Sheehan, P., (eds) Work Rich,
Work Poor: Inequality and Economic Change in Australia, Victoria University, Melbourne.
US Department of Labor, 1998a, O*NET Viewer, Version 1, CD-ROM, Washington DC.
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