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2009 Oxford Business & Economics Conference Program
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THE BUSINESS CYCLE, STRUCTURAL AND TECHNOLOGICAL: THE IMPACT
ON LABOUR SKILLS IN AUSTRALIA
Ross Kelly and Philip Lewis, Centre for Labour Market Research
University of Canberra, Canberra ACT 2601, Australia
Contact 61 2 6201 2705, [email protected]
June 24-26, 2009
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2009 Oxford Business & Economics Conference Program
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THE BUSINESS CYCLE, STRUCTURAL AND TECHNOLOGICAL: THE IMPACT
ON LABOUR SKILLS IN AUSTRALIA
ABSTRACT
This paper presents an analysis of skill change for each of several skill dimensions for
Australia for the period 1991-2006. This period is of particular interest since it covers three
phases of the business cycle – recession, full employment and excess demand. The
pronounced shedding of low skill workers and increased demand for skilled workers observed
in many countries over the last two decades has been attributed to a number of different
causes. In this paper the attributes of different occupations are used to obtain measures of
distinct skill dimensions plus education using a method developed by the authors in previous
work. The results indicate that there were very significant changes in skills mix during the
three phases of the business cycle. The results have important implications for policy
particularly in relation to employment, unemployment and training.
INTRODUCTION
The structure of the Australian economy has changed dramatically over the last two decades,
Lewis et al (2006), with a sustained shift away from agriculture and manufacturing. The
growth industries have been in services, consistent with long term trends in advanced and
many developing economies. These trends have had a profound effect on the skills that are in
demand in the economy, which in turn has also altered the structure of skills and average skill
levels within industries. The implications of these changes are of crucial importance in
training policy.
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Over the last two decades there has been a pronounced shedding of low skill workers and
increased demand for skilled workers observed in many countries, Gautié (2002). This has
been attributed to a number of different causes, with the most commonly cited reason being
skill-biased technological change. The shifting skill structure in the economy has also been
driven by shifting patterns of demand, with the post-recession period between 1991 to 1996
being a critical period for the shedding of low skill jobs. In this paper the attributes of
different occupations are used to obtain measures of three distinct skill dimensions - motor
skills, interactive skills and cognitive skills. A fourth and more widely used measure of skill,
education, is also used. Motor skills are essentially the ability to do physical tasks. Cognitive
skills relate to the possession of and ability to create knowledge. Interactive skills refer to the
ability to relate between managers and employees, employees and employees, and employees
and customers. The paper presents an analysis of skill change for each of these skill
dimensions for Australia for the period 1991-2006. The analysis examines the pattern of
industry skill demand by analysing skill changes separately for full-time and part-time
workers and for the sub-periods 1991-1996, 1996-2001 and 2001-2006.
EMPLOYMENT GROWTH IN THE AUSTRALIAN ECONOMY
Like many advanced economies, Australia experienced a deep and protracted recession in the
early 1990s. The period of recovery left a large number of the low skilled workforce stranded
in long-term unemployment and marginalised employment (Norris and Wooden, 1996).
Since the 1991/92 recession, there has been a remarkable period of growth impacting on total
employment in Australia, Lewis et al (2006). However, this has followed very different
trajectories across each of the 17 industry sectors and occupations in the Australian economy.
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The observed growth and contractions of each sector have also had different drivers. For
example, the utilities sector (Electricity, Gas & Water) experienced substantial re-structuring
and labour shedding throughout the 1990s as a result of the partial de-regulation and
privatisation of the sector. Manufacturing has experienced steady decline over the entire
period, for the most part due to the increasing productivity and sophistication of Chinese
manufacturing allowing for a wider range of consumer goods to be cost effectively sourced
from that country. Growth in each of the sub-sectors of manufacturing in terms of
employment since 1991 has been mixed. However, consistent with the rise of Chinese
manufacturing, the standout is a 60 per cent decline in the number of people employed in
Textile, Clothing, Footwear and Leather (TCFL) manufacturing. TCFL has fallen from 10.8
per cent of total employment in manufacturing to 3.9 per cent since August 1991.
Other industries aligned more directly aligned with the fortunes of the mining sector, such as
construction, have risen sharply since 2001 as the demand for raw materials to feed the rapid
industrialisation in China and India worked its way through the resource rich states of
Australia (mainly Queensland and Western Australia). The impact of the mining boom on the
economies of the resource states was so pronounced that they have been effectively running at
over full employment (male unemployment fell below 2 per cent in 2008 in Western
Australia) and had to recruit heavily from overseas to fill skill shortages. The overall growth
in employment has been very different between industries over the period in question (see
Table 1) and the changes have occurred at different stages for a range of industries (see
Figure 1), suggesting different drivers are influencing the structural changes taking place.
The changing structure of industry will, in principle, change the pattern of demand for
occupations and, as a consequence, the skill types and skill levels in demand. This needs to be
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taken in to consideration by both industry and training authorities in their forward planning.
As shown in Table 2 the mix of occupations changed quite dramatically. Total growth for the
15 years to 2006 was just over 1.8m persons, with the higher skilled occupational groupings
growing significantly faster than the traditional blue collar unskilled and trade qualified
occupations. Advanced Clerical and Service Workers were the only occupational grouping
where total employment fell. Given the scale and duration of the economic expansion in
Australia this is quite remarkable.
The timing of employment growth by occupation was also varied, with some occupations in
the lower skilled occupations only increasing in the last few years, mainly in response to the
massive investment boom in the resources sector that occurred from around 2002 onwards.
Managers and Administrators really only began to increase in any volume after 2004, as was
the case for Tradespersons, and Intermediate Production and Transport Workers.
Professionals, Associate Professionals and Intermediate Sales and Clerical Workers grew at a
steady rate throughout the 1996 to 2006 period.
SKILL BIASED TECHNICAL CHANGE
Among the explanations for the change over time in the skill composition of the Australian
and other advanced economies is the changing pattern of trade between countries. The
argument put forward is that increasing trade with developing countries has led to shifts away
from labour intensive manufacturing industries in industrialised countries. This has the effect
of lowering the relative demand for unskilled labour. A study by Pappas (1998) using shiftshare analysis showed that the contribution of trade to changes in mean industry skill scores
for Australia between 1976 and 1991 were very small, due in part to the fact that the share of
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employment in the traded sector is relatively small. Other studies, such as Wolff (1995),
reached the same conclusion for the US.
The role of new technologies and how they have impacted on skill requirements have been
studied in a number of different contexts both in Australia and elsewhere. A broad consensus
has emerged that there is a high degree of complementarity between skill and technology,
Berman et al (1997), Bound & Johnson (1992), Goldin & Katz (1998), Kelly & Lewis (2003),
Maglen & Shah (1999), Pappas (1998), Wolff (1995). The principal argument is that recent
technological change in the economy, particularly the intensification of information and
communication technologies (ICT), has complemented skilled and highly skilled labour in
production.
ICTs can change the composition of skills in the economy in two ways. First, the direct
substitution of easily automated labour intensive type jobs by computer-based technologies
will alter the composition of skills. It can also eventuate from the organisational
complementarity that exists between computer based technologies and managerial and
professional jobs, Autor et al (1997), Caroli (1999). A study by Autor et al (2000) found this
to be the case for a major US bank where image processing technology was installed in the
mid 1990s, with data entry jobs being directly affected. These jobs were typically filled by
low skilled high school graduates. Jobs involving more discretion and interdependence
between workers were also streamlined. Nonetheless, subsequently there was more emphasis
placed on the employment of college graduates relative to less educated labour. A Canadian
study shows there has been a process of upskilling in workplaces where computer-based
technologies have been introduced and that newly created jobs resulting from the introduction
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of these technologies tended to be high-skilled jobs. Most of the jobs made redundant were in
the low-skilled categories Gera & Masse 1996).
Mukoyama (2003) argues that the speed of technology diffusion is affected by the skill
distribution in the economy. Skilled machine-users adopt a new technology first, while
unskilled users wait until machines become more reliable and accessible. This is a reasonable
description of the trajectory experienced with the PC and related applications, to a point
where it is now common for people to be establishing computer networks in the home or
small businesses with little or no experience. This would have been unthinkable as little as a
decade ago.
ICTs enable organisational forms to vary to traditional, existing forms; they favour ‘lateral
communication and coordination’. Related to this is increased autonomy - this changed mode
of supervision also requires different skills. People skills, or interactive skills, are critical to
this process. They are an integral part of the new form of ICT enabled production and outputs
and they are also critical to the process of change itself.
To summarise, skill biased technological change suggests that the demand for labour will vary
by skill type as ICTs extend their reach in the economy (capital widening) and successive
generations of ICTs improve their capability (capital deepening). Repetitive and easily
routinised tasks are more likely to be substituted than ‘complex and idiosyncratic’ ones. Work
that is cognitively demanding and requiring judgment or creativity, on the other hand, will be
more difficult to automate and computerise, Autor et al (2000, 2003), Bresnahan et al (1999)
and hence less likely to be substituted.
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Evidence of technological deepening
There has been a rapid expansion of the coverage of computers and Internet access in the
Australian workplace over the last decade. Evidence presented in Kelly and Lewis (2007)
shows that there was a significant uptake of computers and internet by businesses of all sizes
in the decade to 2001. By 2001 77.2 per cent of businesses employing 1-4 people were using
computers. Access to the Internet by business also increased over the same period, with micro
businesses (1-4 employees) growing at 32 per cent per year over the three year period to
2001, small businesses (5-19 employees) were growing at 23 per cent per year over the same
period. Kelly and Lewis (2007) suggest that hardware investment in ICTs had approached
saturation for the larger firms, but that the application of ICT infrastructure continued to
develop, as evidenced by the rapid increase in Internet usage. Internet usage increased for a
range of purposes n the 1999-2001 period, one application of particular interest being ‘making
or receiving payments’. In 2006/7 40 per cent of businesses were placing orders via the
internet, 24 per cent received orders via the internet (ABS 2008a). This increased from 8.3 per
cent in 1999.
Further evidence of the increasing influence of ICTs can be found in the changing net stock of
IT capital in the economy. In
3 shows the net stock of selected electrical, electronic and IT equipment for Australia
over the past decade. All items showed substantial increases, but it is computers, peripherals
and computer software that have shown the greatest growth. The growth in computer software
since 1998 has been very rapid. The interesting aspect of
Figure
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3 is that the expansion is more about applications (computer software) and not just hard
infrastructure, suggesting business models and supporting applications are successfully
exploiting what is now pervasive technology. It is also important to recognise that the items of
interest in
Figure
Figure
3 are net of depreciation. These items are written off within four years, which makes
the observed net increases all the more impressive. As a consequence of this, the increases in
the net IT stock suggest much of it is relatively young and represents not only capital
widening, but also significant capital deepening. How this stock has been applied in the
workplace and what the relationships are with labour and skill demand is of great interest.
MEASUREMENT OF SKILL AND SKILL CHANGE
The issue of skill measurement will confront any analysis of skills at an aggregate level.
Measures typically favoured by economists in studies of human capital rely on years or level
of education as a measure of skill attainment. The obvious shortfalls are that these measures
do not necessarily capture the actual skill requirements of jobs – the rapid growth in
educational attainment may have as much to do with credentialism as skill attainment.
Attewell (1990). An alternative favoured by sociologists focuses on the skill attributes
required of jobs, as defined in the US Department of Labour’s Dictionary of Titles (DOT).
Despite the limitations of using the DOT, Attewell (1990), it provides a convenient basis for
the analysis of skills independent of productivity measures and knowledge of individuals or
workplaces and so is used for the following analyses. A brief overview of how skill scores are
assigned to an occupation and industry follows. The full details can be found in Kelly and
Lewis (2003).
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Mean skill scores (i.e. average skill per hour worked in the whole economy) for four skill
dimensions for 1991, 1996, 2001 and 2006 were calculated. Census data for each year were
used to compile the skill indexes for each of the skill dimensions being considered. Measures
of skill were constructed using data and information from Australian occupational task
descriptions contained in the Australian Standard Classification of Occupations (ASCO), 2nd
edition. These were then combined with occupation by industry employment matrices
showing total hours worked for part-time and full-time workers and scales of skill complexity
for four skill dimensions developed by the United States Department of Labor (USDOL).
The Dictionary of Occupational Titles (DOT), 4th edition (1991), used in the US provides a
schema for rating skills at the finest level of occupational detail, as shown in Table 5. In DOT
jobs are classified as requiring workers to function to some degree in relation to data, people,
and things. The scale for each skill dimension shown in Table 4 is in descending order.
Those tasks that involve more complex responsibility and judgment are assigned lower
numbers for each category and the less complicated have higher numbers. For example, for
the data skill dimension (see Table 4) ‘compiling’ would be considered a more complex task
than ‘copying’. The same applies for the other dimensions. Each dimension is considered
separately. The scale relates to an ordering of the complexity of tasks normally undertaken in
an occupation, it does not signal anything about the intensity of use of those skills. At an
industry level, this is determined by the hours of employment, or utilisation, of the skills
embodied in an occupation. The occupation, in turn, tells us something about the tasks
undertaken and how they relate to the scale of complexity shown in Table 5.
Previous studies, Autor et al (2003), Kelly & Lewis (2003), Pappas (1998), Wolff (1995),
have used approaches similar to this to determine the skill scores of an industry. For
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consistency, the nomenclature used in those studies to relate to the various skill dimensions is
employed here. Thus, the schema shown above is applied as follows.
Four types of skill are analysed: motor skills, education, interactive skills and cognitive skills.
(A fifth measure of skill – strength – based on a separate system of categorizing the physical
demands of work in the USDOL DOT is employed in the analysis by Wolff (1995); however,
a similar measure cannot be derived from the task descriptors used in the ASCO.) The ‘data’
category in Table 4 provides a measure of cognitive skills, the ‘people’ category aligns with
interactive skills and the ‘things’ category provides a good indicator of motor skills. The
education category used for this study comes from the education requirement listed for each
occupation in the ASCO (2nd edition). The levels of education, based on the Australian
Qualifications Framework (AQF), were grouped into six levels, with masters and doctoral
degree the highest and AQF I & II the lowest, the measure being made complete by the
addition of a ‘no qualification required’ level. (AQF I & II are the most basic of qualifications
requiring a narrow range of elementary competencies, such as demonstrating “…basic
practical skills such as the use of relevant hand tools…” AQF (2002); they may be acquired
through accredited training courses and/or recognition of prior learning). All other measures
were inverted, that is, the least complex tasks were given the lowest score. The scale was
converted to a common scale of 0 to 10. Finally, the scores were assigned to a given
occupation for each skill dimension at the finest level of information on occupations, the
ASCO (2nd edition) six-digit level. The most complex task undertaken in an occupation for
each skill dimension, as identified from the ASCO, provided the basis for applying the scores.
Thus, the mean skill score for a given dimension in industry k is as follows:
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Sk 
r ,u
 smOmn
m , n 1
ISBN : 978-0-9742114-1-1
r ,u
O
m, n 1
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mn
(1)
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with the mean skill for a given skill dimension for the economy defined as:
S 
q , r ,u
q , r ,u
k , m , n 1
k , m , n 1
 sm Okmn
O
(2)
kmn
k = (1, …q)
m = (1, …r)
n = (1,.....u)
where:
S is the mean skill score;
s is the skill score of an occupation and is constant across time;
O is the number of hours worked.
Subscripts denote:
k industry;
m occupation;
n part-time or full-time employment status.
Given that the skill score for a given skill dimension and occupation is held constant for each
time period, it is changes to the occupational composition of employment that determines
changes in the economy-wide mean skill level. This can be represented as:
S  s m

 Om

k , m , n 1 
q , r ,u
q , r ,u
O
k , m , n 1
kmn



(3)
From (2) it is apparent that changes in mean skills in the economy can arise from changes in
the share of an occupation in an industry and changes in industry shares of total hours
employed in the economy.
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To simplify exposition we denote the occupational share of industry k as:
bkm  Om
r
O
m 1
(4)
m
and an industry’s share of total hours employed in the economy as:
r
hk   Om
m 1
q
O
k 1
k
(5)
Thus, the change in mean skill for the economy as a whole for skill dimension j is:
S jt  S jt 1   bkm hk t  bkm hk t 1 
(6)
An exact decomposition is provided by:
S   bkm h k   b km hk
(7)
with change expressions identified by the delta symbol and the bar over expressions
indicating the inter-temporal mean.
The first term on the left of equation (7) provides the within-industry effect, taken to be the
change in skills mix due to technological change. The second expression is the betweenindustry effect, the change in skills mix due to structural change. Both of these are further
decomposed to show the contribution of the part-time and full-time workforce to changes in
mean skill.
The way changing industry shares of total employment affect economy-wide mean skill
scores can be explained as follows. If an industry with a relatively high proportion of skilled
workers increases its share of overall employment, then there will be an increase in the
economy-wide average skill level. This is the inter-industry effect and can be split into the
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contributions from part-time and full-time employment by applying the respective weights for
part and full-time hours employed.
Intra-industry changes to occupation composition work the same way. When an occupation
that is relatively highly skilled increases its share of employment within a given industry, that
industry experiences an increase in its mean skill level. This can be further decomposed into
the contributions from part-time and full-time employment. This enables an examination of
whether the large shift towards part-time employment over the last decade has resulted in deskilling. If the occupational composition of part-time employment is different to that of fulltime employment, then a change in emphasis within an industry toward one or the other will
influence the economy-wide mean skill score. The sum of such changes across the economy
shows the within-industry effect on the change in the economy-wide average skill level.
RESULTS
The analysis of the 1991 to 2001 period has been reported in Kelly and Lewis (2006). The
results from that work show an approximately equal skill change for the 1991-1996 and 19962001 periods. Cognitive skills were the main exception with 58 per cent of the increasing
skill level over the decade to 2001 occurring in the 1996-2001 period. The observed skill
change was driven by between-industry (structural) changes in the 1991-1996 period and
within-industry (technological) changes in the 1996-2001 period. Around 80 per cent of the
skill change between 1996 and 2001 was due to within-industry changes, which is attributed
to technological change.
Kelly (2007) shows that investment in information technology was a significant driver of skill
substitution for cognitive skills for the same period. When the changes in mean skills over
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the 1991 to 2001 period are examined in terms of the influence of changing shares of parttime and full-time workers, the reduction in the share of full-time employment for both time
periods resulted in the economy-wide mean skill level falling, even though the average skill
level of both part-time and full-time workers was actually increasing slightly. The effect is a
result of the lower average skill of part-time workers compared with full-time workers and the
changing weight in employment. The impact of the reduction in full-time employment was
greatest for the post-recession period of 1991 to 1996.
The latter part of the 1990s saw the build up for the Sydney Olympics and a sustained period
of growth for the Australian economy. The major shocks were experienced through the late1997 financial crisis among many of the ASEAN economies. Among these are a number of
substantial trading partners of Australia, especially Malaysia, Indonesia, Thailand, Singapore,
Taiwan and South Korea. Post-2001 was period where the influence of Chinese and Indian
industrial growth ignited an investment boom in the resource sectors in Australia, with the
flow-on effect strongest in the construction and transport industries.
As shown in Table 6 the majority of the change in skill levels over the period was due to
range of technological influences that enabled an alternative skill mix of labour in production.
In the case of education levels, the shift in industry weights actually had the overall effect of
lowering the average level of education attainment required.
As shown in
Table
7 virtually all the change from within-industry effects for motor and cognitive skills
occurred between 1996 and 2001. For educational attainment and interactive skills, around
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two thirds of the within-industry contribution occurred between 1996 and 2001. The 19962001 period is also when the majority of change took place in the mean skill levels across the
economy for motor skills and cognitive skills. Around 60 per cent of the increase in
interactive skills occurred in the 1996-2001 period, the level of educational attainment
required of jobs was split relatively evenly between the two periods with 52.6 per cent in the
earlier period.
In the period 2001-2006 there was a reversal of a long-term trend away from full-time
employment in the Australian economy as the effects of continuous economic expansion was
capped off with the investment spurt fuelling the resource sector boom. The outcome was
reflected in the mean skill level of employment. The mean skill level of the full-time
workforce continued to increase over the 1996-2001 period (see Table 8) as industry
maintained its bias toward a higher skilled mix of occupations among the full-time workforce.
However, as for the 1991-1996 period (see Kelly & Lewis (2006)) the full-time share in the
workforce fell. The increasing use of the lower skilled part-time workforce reduced the mean
skill in the economy. This was reversed in the 2001-2006 period and is consistent with the
reporting of widespread skill shortages across most sectors of the economy.
The 2001-2006 period shows a much smaller increase in the mean skill level of the part-time
workforce than for each of the intercensal periods between 1996 and 2001. This is due to the
economy approaching full employment and the gradual exhaustion of the available skill pool.
Indeed the period, with the benefit of hindsight, can now best be described as one of excess
demand, with the marginal employment of low skilled workers reflected in the very small
increase in mean skill levels of this segment of the workforce.
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CONCLUSION
There have been very important changes in skill mix in the Australian economy. While the
post recession period of 1991-1996 was characterised by skill changes resulting from
structural change, in the recovery and boom periods technological change was the dominant
influence on skills change. Of the change in skills due to structural change in the recovery and
boom period, this was most apparent in the boom period 2001-2006, consistent with terms of
trade movements and the resultant mining boom. Technological change continues to be a key
influence on the pattern of skill demand but recent Australian experience was also clearly
influenced by patterns of trade and industry structure.
The rapid increase in Information and Communication Technologies (ICTs) investment
observable in the 1990s continued in the first half of the 2000s with the increasing share of
ICT in the capital stock. The increasing importance of ICT in the capital stock is clearly
having an impact on the type of skills demanded in the economy. This is most likely
occurring due to direct demand for ICT related skills and indirectly through the enabling
characteristics of ICTs. Importantly, it is not only the increasing emphasis of computers in the
workplace and industry, but the rapid increase in the uptake of software applications by
industry. The growth rate of growth of the share of computer hardware in the capital stock
actually escalated during the economic boom, as did the uptake of software applications. It
appears that ICTs have allowed a substantial re-ordering of occupations within industries.
That is, they are enabling a reorganisation of the workplace, one that places greater emphasis
on skills, particularly interactive and cognitive skills. The extent to which these skills are able
to be diffused through formal training and education needs to be explored.
The recent turmoil in financial markets and the widely predicted recession would be expected
to see increasing emphasis on high skilled employment with opportunities for firms to exploit
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labour saving technologies and renew focus on ICT. Traditional ‘blue-collar’ skills would be
expected to stagnate or continue to decline, and this will test the ability of the labour market
to adjust and absorb the existing supply of these skills. The inability of many individuals to
adjust to the current and expected skill demands of industry will continue to see a large
component of unemployment in Australia being structural in nature as well as demanddeficient. When capital becomes technologically obsolete, the social consequences will be
relatively benign. When the skills of workers become obsolete, the social consequences are
much more serious, with unemployment, financial hardship and marginalisation the likely
outcome.
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Australian Bureau of Statistics ABS (2002c), Business Operations and Industry Performance,
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Catalogue No. 5368.0, Australian Bureau of Statistics, Canberra.
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Australian Bureau of Statistics ABS (2003d), International Merchandise Exports, Australia
Catalogue No. 5432.0.65.001, Australian Bureau of Statistics.
Australian Bureau of Statistics ABS (2008), Labour Force, Australia Catalogue No.
6291.0.55.001, Australian Bureau of Statistics, Canberra.
Australian Qualifications Framework (AQF 2002) Advisory Board, Australian Qualifications
Framework Implementation Handbook Third Edition 2002, Australian Qualifications
Framework (AQF) Advisory Board, Melbourne.
Autor, D., Levy, F. and Murnane, R. (2000),‘Upstairs-Downstairs: Complementarity and
Computer-Labour Substitution on Two Floors of a Large Bank’, NBER Working
Paper No. 7890, National Bureau of Economic Research, Cambridge.
Autor, D., Levy, F. and Murnane, R. (2003), ‘The Skill Content of Recent Technological
Change: An Empirical Exploration’, Quarterly Journal of Economics, 118 (4),
Novembe.
Autor D., Katz L. and Krueger A. (1993), ‘Computing Inequality: Have Computers Changed
the Labor Market?’ NBER Working Paper No. 5956, National Bureau of Economic
Research, Cambridge.
Berman, E., Bound, J. and Machin, S.(1997), ‘Implications of Skill-Biased Technological
Change: International Evidence’, NBER Working Paper No. 6166, National Bureau of
Economic Research, Cambridge.
Borland, J. (1999), ‘Earnings Inequality in Australia: Changes, Causes and Consequences’,
Economic Record, 75, pp177-202.
June 24-26, 2009
St. Hugh’s College, Oxford University, Oxford, UK
20
2009 Oxford Business & Economics Conference Program
ISBN : 978-0-9742114-1-1
Borland, J. and Wilkins, R.(1996), ‘Earnings Inequality in Australia’, Economic Record, 72,
pp7-23.
Bound, J. and Johnson, G. (1992), ‘Changes in the Structure of Wages in the 1980s: An
Evaluation of Alternative Explanations’, American Economic Review, 82, pp371-392.
Bresnahan, T., Brynjolfsson, E. and Hitt, L. (1999), ‘Information Technology, Workplace
Organisation, and the Demand for Skilled Labour: Firm-Level Evidence’, NBER
Working Paper No. 7136, National Bureau of Economic Research, Cambridge.
Caroli, E. (1999), ‘New Technologies, Organisational Change and the Skill Bias: a go into the
Black Triangle’, in Employment and Economic Integration, P. Petit and L. Soete
(eds), Edward Elgar, London.
Gautié, J. (2002) ‘The Destabilisation of Internal Labour Markets’, Centre d’Etudes de
L’emploi, Université de Reims et LSS-ENS.
Gera, S. and Masse, P. (1996), Employment Performance In The Knowledge-Based
Economy, Industry Canada Working Paper No. 14, Human Resources Development
Canada W-97-9E/F, Canada.
Goldin, C. and Katz, L. (1998), ‘The Origins of Technology-Skill Complementarity’,
Quarterly Journal of Economic, 113, pp693-732.
Kelly, R. (2007), ‘Changing Skill Intensity in Australia Industry’, The Australian Economic
Review, Vol 40, No. 1, pp. 1-18
Kelly, R. and Lewis, P.E.T. (2003), ‘The New Economy and Demand for Skills’, Australian
Journal of Labour Economics, Vol. 6, No. 1, pp 135 – 152.
June 24-26, 2009
St. Hugh’s College, Oxford University, Oxford, UK
21
2009 Oxford Business & Economics Conference Program
ISBN : 978-0-9742114-1-1
Kelly, R. and Lewis, P.E.T. (2006), ‘Measurement of Skill and Skill Change’, Contributions
to Probability and Statistics: Applications and Challenges, Brown P, Liu, S and
Sharma, D. (Eds), World Scientific, Singapore
Lewis, P. (ed), Garnett, A., Hawtrey, K. & Treadgold, M. (2006), Issues, Indicators and Ideas:
a Guide to the Australian Economy, 4th ed., Pearson Education, Sydney.
Maglen, L. and Shah, C. (1999), ‘Emerging Occupational Patterns in Australia in the Era of
Globalisation and Rapid Technological Change: Implications for Education and
Training’, Working Paper No.21, Monash University-ACER and the Centre for the
Economics of Education and Training (CEET), Melbourne.
Mukoyama, T. (2004), Diffusion and Innovation of New Technologies under Skill
Heterogeneity, Journal of Economic Growth, Vol. 9, No. 4, pp. 451-79
Norris, W.K. & Wooden, M. (1996) (eds), The Changing Australian Labour Market: A
Survey of the Issues, Australian Government Printing Service, Canberra.
Pappas, N. (1998), ‘Changes in the Demand for Skilled Labour in Australia’, in Working for
the Future: Technology and Employment in the Global Knowledge Economy, P.
Sheehan and G. Teggart (eds), Victoria University Press, Melbourne.
United States Department of Labor (USDOL 2000), Dictionary of Occupational Titles (4th
Ed.), Appendix B.
Wolff, E.N. (1995), ‘Technology and the Demand for Skills’, Working Paper 153, New York
University.
June 24-26, 2009
St. Hugh’s College, Oxford University, Oxford, UK
22
2009 Oxford Business & Economics Conference Program
ISBN : 978-0-9742114-1-1
Table 1: Employment growth by industry, Australia, 1991 - 2006
Industry
1. Agriculture, Forestry and Fishing
2. Mining
3. Manufacturing
4. Electricity, Gas and Water Supply
5. Construction
6. Wholesale Trade
7. Retail Trade
8. Accommodation, Cafes and Restaurants
9. Transport
10. Communication Services
11. Finance and Insurance
12. Property and Business Services
13. Government Admin and Defence
14. Education
15. Health and Community Services
16. Cultural and Recreational Services
17. Personal and Other Services
18. Total Industries
Annual
growth rate
%
-0.9
2.3
-0.2
-1.3
3.8
-0.2
2.2
2.4
1.3
1.6
0.6
4.8
2.4
1.7
2.9
3.3
2.3
1.9
Total
change
%
-12.8
41.0
-2.2
-18.2
75.2
-2.4
37.9
42.0
21.2
27.1
9.1
101.0
42.8
29.5
52.6
63.3
39.9
32.9
Total Change
(000s)
-52.1
38.7
-24.2
-18.7
382.9
-11.7
409.6
141.8
81.3
38.1
31.7
625.3
152.6
160.0
367.2
101.9
111.1
2,517.7
Source: ABS (2009), Catalogue No. 6291.0.55.003, Labour Force, Australia, Detailed,
Quarterly Table 06. Employed persons by Industry Subdivision and Sex (Time Series
Workbook)
June 24-26, 2009
St. Hugh’s College, Oxford University, Oxford, UK
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2009 Oxford Business & Economics Conference Program
ISBN : 978-0-9742114-1-1
Table 2: Employment growth by occupation, Australia, 1991-2006
Occupation
Managers and Administrators
Professionals
Associate Professionals
Tradespersons and Related
Workers
Advanced Clerical and
Service
Intermediate Clerical, Sales
and Service
Intermediate Production and
Transport
Elementary Clerical, Sales
and Service
Labourers and Related
Workers
Total Occupations
Aug-06
000s
%
change
843.0
1,965.0
1,280.6
1,293.7
% of
employ
ment
8.3
19.3
12.6
12.7
393.8
3.8
-2.3%
-0.2%
-9.4
1,682.1
16.5
20.6%
1.9%
287.2
861.7
8.5
8.6%
0.8%
68.3
963.3
9.5
11.7%
1.1%
101.0
884.8
8.7
6.0%
0.6%
50.3
10,168.0
100.0
22.0%
2.0%
1,835.2
35.1%
41.4%
47.7%
13.4%
annual Change
growth
000s
rate
3.1%
218.9
3.5%
575.4
4.0%
413.3
1.3%
152.5
Source: ABS (2009), Catalogue No. 6291.0.55.003, Labour Force, Australia, Detailed,
Quarterly Table 07. Employed persons by Occupation and Sex (Time Series Workbook)
Table 3: Business use of selected technologies in Australia, selected years, per cent
Computers
Internet access
Web presence
1998
63
29
6
2001
84
69
22
2006
89
81
30
Source: ABS (2002a & 2008) Cat. No. 8129.0, Business Use of Information Technology
June 24-26, 2009
St. Hugh’s College, Oxford University, Oxford, UK
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2009 Oxford Business & Economics Conference Program
ISBN : 978-0-9742114-1-1
Table 4: Scale of Complexity for Skill Categories
Data
0 Synthesizing
1 Coordinating
2 Analyzing
3 Compiling
4 Computing
5 Copying
6 Comparing
People
0 Mentoring
1 Negotiating
2 Instructing
3 Supervising
4 Diverting
5 Persuading
6 Speaking-Signaling
7 Serving
8 Taking Instructions-Helping
Things
0 Setting Up
1 Precision Working
2 Operating Controlling
3 Driving-Operating
4 Manipulating
5 Tending
6 Feeding-Off bearing
7 Handling
Source: USDOL (2000)
Table 5: Economy-wide mean skill levels, 1991-2006
motor
interactive
cognitive
education
total
part-time
full-time
2.44
1.85
2.66
3.60
3.44
3.67
4.75
4.29
4.92
3.36
2.68
3.61
total
part-time
full-time
2.35
1.88
2.56
3.76
3.46
3.89
4.83
4.26
5.08
3.41
2.62
3.76
total
part-time
full-time
2.23
1.84
2.43
3.88
3.58
4.02
4.87
4.31
5.14
3.45
2.66
3.84
total
part-time
full-time
2.21
1.74
2.43
3.96
3.69
4.09
4.88
4.32
5.14
3.48
2.64
3.87
1991
1996
2001
2006
June 24-26, 2009
St. Hugh’s College, Oxford University, Oxford, UK
25
2009 Oxford Business & Economics Conference Program
ISBN : 978-0-9742114-1-1
Table 6: Between and within-industry contributions to mean skill levels, per cent
1996 to 2006
total within industry
total between industry
total change
motor
71.0
29.0
100.0
interactive
71.7
28.3
100.0
cognitive
66.9
33.1
100.0
education
101.6
-1.6
100.0
Table 7: Between and within-industry contributions to mean skill levels by census subperiod, per cent
total change
Motor
1996-2001
2001-2006
total
Interactive
1996-2001
2001-2006
total
Cognitive
1996-2001
2001-2006
total
Education
1996-2001
2001-2006
total
total within
industry
total between
industry
88.6
11.4
100.0
97.7
2.3
100.0
66.1
33.9
100.0
59.8
40.2
100.0
68.2
31.8
100.0
38.4
61.6
100.0
73.9
26.1
100.0
100.7
-0.7
100.0
19.7
80.3
100.0
52.6
47.4
100.0
62.7
37.3
100.0
*
*
*
* change too small to calculate % change
June 24-26, 2009
St. Hugh’s College, Oxford University, Oxford, UK
26
2009 Oxford Business & Economics Conference Program
Table
8:
Decomposition
of
ISBN : 978-0-9742114-1-1
economy-wide
change
in
average
skill
levels,
Australia, 1996-2001
motor
total change
interactive cognitive
education
-0.121
0.124
0.041
0.039
contribution of p-t
0.082
0.218
0.239
0.160
contribution of f-t
-0.203
-0.094
-0.197
-0.121
total within industry
-0.095
0.101
0.038
0.047
change in mean skill
for p-t
change in p-t share of
industry employment
change in mean skill
for f-t
change in f-t share of
industry employment
total between
industry
change in industry
share of total
employment
- pt
change
in industry
share of total
employment - ft
-0.012
0.042
0.024
0.023
0.041
0.070
0.089
0.055
-0.076
0.077
0.034
0.048
-0.048
-0.088
-0.109
-0.080
-0.026
0.023
0.004
-0.008
0.053
0.106
0.126
0.082
-0.079
-0.084
-0.122
-0.090
Table 9: Decomposition of economy-wide change in average skill levels, Australia, 19962006
total change
contribution of p-t
contribution of f-t
total within industry
change in mean skill for p-t
change in p-t share of industry employment
change in mean skill for f-t
change in f-t share of industry employment
total between industry
change in industry share of total
employment - pt
change in industry share of total
employment - ft
June 24-26, 2009
St. Hugh’s College, Oxford University, Oxford, UK
motor
-0.137
-0.033
-0.103
-0.097
-0.024
-0.002
-0.076
0.006
-0.040
interactive cognitive education
0.207
0.056
0.074
0.118
0.068
0.034
0.089
-0.012
0.040
0.149
0.037
0.075
0.051
0.017
0.017
0.011
0.006
-0.002
0.105
0.030
0.068
-0.019
-0.016
-0.008
0.059
0.018
-0.001
-0.007
0.055
0.045
0.019
-0.033
0.003
-0.026
-0.020
27
2009 Oxford Business & Economics Conference Program
Table
10:
Decomposition
of
economy-wide
ISBN : 978-0-9742114-1-1
change
in
average
skill
levels,
Australia, 2001-2006
total change
contribution of p-t
contribution of f-t
total within industry
change in mean skill for p-t
change in p-t share of industry
employment
change in mean skill for f-t
change in f-t share of industry employment
total between industry
change in industry share of total
employment - pt
change in industry share of total
employment - ft
June 24-26, 2009
St. Hugh’s College, Oxford University, Oxford, UK
motor
-0.016
-0.115
0.100
-0.002
-0.013
interactive cognitive education
0.083
0.015
0.035
-0.100
-0.171
-0.126
0.183
0.185
0.161
0.047
0.000
0.028
0.010
-0.007
-0.006
-0.043
0.000
0.054
-0.013
-0.059
0.028
0.069
0.036
-0.083
-0.004
0.093
0.015
-0.057
0.019
0.071
0.007
-0.059
-0.051
-0.081
-0.063
0.0458
0.0871
0.0958
0.0702
28
2009 Oxford Business & Economics Conference Program
ISBN : 978-0-9742114-1-1
Figure 1: Employment by ANZSIC 1 digit industry, 1984-2008, Australia, 000s, quarterly observations
Mining
200.0
400.0
150.0
300.0
1,250.0
1,200.0
1,150.0
1,100.0
1,050.0
1,000.0
950.0
900.0
100.0
200.0
50.0
100.0
Nov-84
Nov-86
Nov-88
Nov-90
Nov-92
Nov-94
Nov-96
Nov-98
Nov-00
Nov-02
Nov-04
Nov-06
Nov-08
-
Nov-84
Nov-86
Nov-88
Nov-90
Nov-92
Nov-94
Nov-96
Nov-98
Nov-00
Nov-02
Nov-04
Nov-06
Nov-08
-
Electricity, Gas and Water Supply
Construction
June 24-26, 2009
St. Hugh’s College, Oxford University, Oxford, UK
Wholesale Trade
600.0
500.0
400.0
300.0
200.0
100.0
-
Nov-84
Nov-86
Nov-88
Nov-90
Nov-92
Nov-94
Nov-96
Nov-98
Nov-00
Nov-02
Nov-04
Nov-06
Nov-08
1,200.0
1,000.0
800.0
600.0
400.0
200.0
-
Nov-84
Nov-86
Nov-88
Nov-90
Nov-92
Nov-94
Nov-96
Nov-98
Nov-00
Nov-02
Nov-04
Nov-06
Nov-08
160.0
140.0
120.0
100.0
80.0
60.0
40.0
20.0
-
Nov-84
Nov-86
Nov-88
Nov-90
Nov-92
Nov-94
Nov-96
Nov-98
Nov-00
Nov-02
Nov-04
Nov-06
Nov-08
500.0
Manufacturing
Nov-84
Nov-86
Nov-88
Nov-90
Nov-92
Nov-94
Nov-96
Nov-98
Nov-00
Nov-02
Nov-04
Nov-06
Nov-08
Agriculture, Forestry and Fishing
29
ISBN : 978-0-9742114-1-1
Retail Trade
Accommodation, Cafes and
Restaurants
2,000.0
1,500.0
1,000.0
500.0
Nov-84
Nov-86
Nov-88
Nov-90
Nov-92
Nov-94
Nov-96
Nov-98
Nov-00
Nov-02
Nov-04
Nov-06
Nov-08
-
June 24-26, 2009
St. Hugh’s College, Oxford University, Oxford, UK
Nov-84
Nov-86
Nov-88
Nov-90
Nov-92
Nov-94
Nov-96
Nov-98
Nov-00
Nov-02
Nov-04
Nov-06
Nov-08
600.0
500.0
400.0
300.0
200.0
100.0
-
Transport
600.0
500.0
400.0
300.0
200.0
100.0
-
Nov-84
Nov-86
Nov-88
Nov-90
Nov-92
Nov-94
Nov-96
Nov-98
Nov-00
Nov-02
Nov-04
Nov-06
Nov-08
2009 Oxford Business & Economics Conference Program
30
Government Administration and
Defence
600.0
500.0
400.0
300.0
200.0
100.0
-
250.0
500.0
200.0
400.0
150.0
300.0
100.0
200.0
50.0
100.0
-
Education
1,000.0
800.0
600.0
400.0
200.0
-
31
Nov-84
Nov-86
Nov-88
Nov-90
Nov-92
Nov-94
Nov-96
Nov-98
Nov-00
Nov-02
Nov-04
Nov-06
Nov-08
Finance and Insurance
Nov-84
Nov-86
Nov-88
Nov-90
Nov-92
Nov-94
Nov-96
Nov-98
Nov-00
Nov-02
Nov-04
Nov-06
Nov-08
-
Nov-84
Nov-86
Nov-88
Nov-90
Nov-92
Nov-94
Nov-96
Nov-98
Nov-00
Nov-02
Nov-04
Nov-06
Nov-08
Nov-84
Nov-86
Nov-88
Nov-90
Nov-92
Nov-94
Nov-96
Nov-98
Nov-00
Nov-02
Nov-04
Nov-06
Nov-08
Communication Services
Nov-84
Nov-86
Nov-88
Nov-90
Nov-92
Nov-94
Nov-96
Nov-98
Nov-00
Nov-02
Nov-04
Nov-06
Nov-08
Nov-84
Nov-86
Nov-88
Nov-90
Nov-92
Nov-94
Nov-96
Nov-98
Nov-00
Nov-02
Nov-04
Nov-06
Nov-08
2009 Oxford Business & Economics Conference Program
ISBN : 978-0-9742114-1-1
Property and Business Services
1,400.0
1,200.0
1,000.0
800.0
600.0
400.0
200.0
-
Health and Community Services
1,200.0
1,000.0
800.0
600.0
400.0
200.0
-
ISBN : 978-0-9742114-1-1
Cultural and Recreational Services
350.0
300.0
250.0
200.0
150.0
100.0
50.0
-
Personal and Other Services
500.0
12,000.0
10,000.0
8,000.0
6,000.0
4,000.0
2,000.0
-
400.0
300.0
200.0
100.0
Nov-84
Nov-86
Nov-88
Nov-90
Nov-92
Nov-94
Nov-96
Nov-98
Nov-00
Nov-02
Nov-04
Nov-06
Nov-08
-
Nov-84
Nov-86
Nov-88
Nov-90
Nov-92
Nov-94
Nov-96
Nov-98
Nov-00
Nov-02
Nov-04
Nov-06
Nov-08
Total Industry
32
Nov-84
Nov-86
Nov-88
Nov-90
Nov-92
Nov-94
Nov-96
Nov-98
Nov-00
Nov-02
Nov-04
Nov-06
Nov-08
2009 Oxford Business & Economics Conference Program
2009 Oxford Business & Economics Conference Program
ISBN : 978-0-9742114-1-1
Figure 2: Employment by ASCO 2nd ed 1 digit occupation, 1996-2006, 000s, quarterly
observations
Managers and Administrators
Professionals
1,000.0
2,500.0
800.0
2,000.0
600.0
1,500.0
400.0
1,000.0
200.0
500.0
Feb-04
Dec-04
Jun-02
Apr-03
Oct-00
Aug-01
Feb-99
Dec-99
Apr-98
Jun-97
Aug-96
1,600.0
1,400.0
1,200.0
1,000.0
800.0
600.0
400.0
200.0
-
33
Jun-07
Apr-08
Oct-05
Aug-06
Feb-04
Dec-04
Jun-02
Apr-03
Oct-00
Aug-01
Dec-99
Apr-98
Feb-99
Jun-97
Jun-07
Apr-08
Jun-07
Apr-08
Associate Professionals
Aug-96
Oct-05
Aug-06
Oct-05
Aug-06
Feb-04
Dec-04
Jun-02
Apr-03
Oct-00
Aug-01
Feb-99
Dec-99
Jun-97
Apr-98
-
Aug-96
-
2009 Oxford Business & Economics Conference Program
ISBN : 978-0-9742114-1-1
Figure 3: Information technology net capital stock, $m (constant prices)
$60,000
$50,000
$40,000
$30,000
$20,000
$10,000
Jan-2008
Jan-2006
Jan-2004
Jan-2002
Jan-2000
Jan-1998
Jan-1996
Jan-1994
Jan-1992
Jan-1990
Jan-1988
Jan-1986
Jan-1984
Jan-1982
Jan-1980
Jan-1978
Jan-1976
Jan-1974
Jan-1972
Jan-1970
$0
ALL INDUSTRIES ; Computers and peripherals: Current prices ;
ALL INDUSTRIES ; Electrical and electronic equipment: Current prices ;
ALL INDUSTRIES ; Computer software: Current prices ;
Source: ABS Cat. No. 5204.0, Table 69. Information Technology Net Capital Stock, Selected
items by Industry
34