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The primary object of interest for the present research lies in the degree to which jobs, viewed at various levels of generality, involve the deployment of more or less skill over time. This is the fundamental definition of a skill trajectory as used in this thesis. The generic terms used to denote this object of interest are the skill content or skilfulness of a job. These terms embrace the three dimensions identified in Chapter 3: substantive complexity, skill-intensity and task discretion. A job which rates highly on any of these dimensions, or any combination of them, is said to be a skilful job. This term is preferred to “a skilled job” because of the latter’s conventional association in Australian usage with a specific level in the qualifications hierarchy, i.e. trades-level qualifications gained through a traditional apprenticeship.

The literature on skill trajectories, already discussed in Chapter 5, acknowledges that different kinds of process underlie upward or downward movements in the skill content of jobs across an economy or industry. One set of processes is generic, and affects jobs across the economy, though not necessarily all to the same extent. Such processes can work through expansion, as where skill requirements rise because more workers even in lower- skilled jobs are expected to be able to use ICT, or through attrition, where the lower-skilled

The second is compositional, involving the addition to an economy of new industries and occupations which supplement or replace existing ones, or the decline of industries or occupations which once represented a high proportion of employment. Like generic change, it has an expansionary component, e.g. where innovation and evolution broaden the range of viable activities in an economy, and a component of attrition where lower-skilled industries decline as a proportion of economic activity because the country can no longer compete on labour costs in an international market.

The model followed in this thesis is based on that of Rumberger (1981) and differs from other versions of the job content/compositional model by assuming three distinct processes:

1. increases in the skill content of existing jobs: the jobs currently occupied by individuals change to require more skill, involve more learning and/or allow more task discretion;

2. more skilful jobs at the industry or occupation level: existing jobs in an industry, occupation or industry/occupation cell are replaced or supplemented by new ones involving more skill, learning or task discretion;

3. a shift in the balance of economic activity towards more skill-intensive sectors: jobs are lost in industries or occupations involving low levels of skill, learning or task discretion and replaced by new ones in higher-skilled industries and occupations. The reason for splitting compositional change into two components is that each requires a different type of evidence, and the HILDA data lend themselves to this kind of separate analysis. In addition, a three-part model can more effectively capture a gradation, from changes affecting the nature of work in general at one end, through to changes which stem solely from a shifting balance of economic activity and may not imply any underlying change in the nature or organisation of work, even within the industries or occupations which are increasing their representation. It is important to be able to make this kind of discrimination, since in an economy which exhibits growing (or indeed, shrinking) overall requirements for skill, there is a good likelihood that all three processes will be occurring simultaneously to varying extents, and any aggregate growth in skill-intensity will represent the combined impact of all three. Several considerations could cloud the issue of what is happening to skill unless the methodology makes it possible to distinguish the incidence and impacts of each over any period of interest.

First and most obviously, the impact of one process may offset that of another. A

compositional shift of investment towards an industry with high average skill-intensity but few exceptionally high-skilled jobs could entail the destruction of some highly skilled jobs in declining industries. Even if the overall level of skill deployed across the economy rises as a result, the implications for workforce flexibility and well-being in such a case will be different from those of a comparable shift in the skill trajectory distributed more evenly across industries. Even at the level of the national economy, the scarce skills which are lost in the displaced industries might represent a loss of national competitive potential in the future, unless the people who hold them are productively redeployed into the growth industries.

Conversely, a change in one process can trigger a complementary movement in the others that might not have occurred otherwise. For example, when new jobs utilising more skill are generated in an industry as the result of some kind of product or process innovation

(process 2), this may entail a complementary rise in the skill demands on older, previously low-skilled jobs in the same industry or its supply chain (process 1) before the full

productivity benefits can be achieved. Alternatively, the growth of new industry sectors (process 3) may provide an incentive for existing sectors to increase their skill-intensity (processes 2 and/or 1) as the price of remaining competitive and continuing to attract investment.

In addition, the three processes may take effect over different time-scales, with feedback cycles of widely varying length and different degrees of path-dependence. Virtuous or vicious circles may be triggered in one process (e.g. cascades of investment or worker preference towards new industries perceived as sexy, or the development of costly types of long-cycle training that involve a large element of sunk cost for both the provider and the trainee) which block adjustment in another. Hence, the trend and sustainability of any overall change in the amount of skill exercised may depend on which process dominates at the time in question. This consideration is especially important in periods when the labour market is temporarily dominated by a one-off or cyclical spike in the demand for skilled labour in one sector, since meeting that demand can mean diverting both labour and

training effort from other industries and occupations which have a longer learning cycle and might be more economically sustainable in the long term.

Finally, there will be different implications for the content of the skills exercised depending on which of the three processes is in operation at a given point in the system. Both process 2 and process 3 are logically associated with the development either of new kinds of skill or of new technical competencies within existing ones, whereas process 1 is more likely to imply a deepening of the existing technical skill content of jobs. However, even where the technical content remains essentially the same, process 1 may still require the workforce to acquire new soft skills: for example, greater control by individuals over their own work demands greater skill in such things as planning, time management and prioritising competing demands, and devolution of decision-making to the work group level depends for its success on the presence of teamworking skills that might not have been expected under a more directive style of work organisation. And even where strictly technical skill is concerned, and remains essentially the same skill, a deepening of the individual’s capability beyond a certain point will involve a shift from knowing-what to knowing-how, and

subsequently from knowing-how to knowing-why. Given that each of these kinds of skill has its own optimal learning method and/or training requirements, it is important to know which process is driving the change in which areas before determining how either the educational and VET infrastructure or organisational learning practices should best respond to changes in the aggregate skill requirement.

Thus the use of a dataset like HILDA, which contains practically no variables describing the actual competency content of jobs, makes it all the more important to preserve

analytical separation between the three processes. Apart from a set of questions in the early waves about types of teaching and nursing qualification, the only two job content-related variables in a set of over 3,000 refer to highly generic soft skills, namely learning

(NUSKILLS) and using initiative (Waves 5 and 6 only). While both of these might seem potentially valuable for an analysis of trends in the demand for soft skills, the very high level of positive responses and the virtual absence of significant movement in either between waves make them all but useless for tracking change over this limited period.

These qualifications do not invalidate either the construct of a skills trajectory developed in Chapter 2 or the conclusion in Chapter 3 that a metric combining skill-intensity with task discretion is the most appropriate one so far available in Australia for tracking the state of the national skilling system. The overall growth or decline in the amount of skill exercised in this broader sense remains the best construct with which to answer the question “How is the system travelling?” or “How is it performing?”, and the output it describes is the most directly relevant to the well-being and productivity of the workforce and the overall performance of the economy at a given point in time. At the same time, measurements of this aggregate can have only limited explanatory or predictive power. There is no

contradiction here, but rather a distinction between the purposes of tracking, on the one hand, and understanding, predicting or anticipating on the other. The one answers the question “Where is the system at now, and how has it changed?”, the other answers the questions “Why is the system behaving this way, and how is it likely to behave in future?” It is important to keep the two sets of questions analytically separate, since the present research is directed primarily though not exclusively at the former question.