CRONOGRAMA VALORADO DEL PMA DE LA ESTACION DE SERVICIOS HENRY

This section of the chapter starts from the generic features of systems outlined in 2.1 and applies them to skilling. The model developed here, though still very abstract and conceptual rather than predictive, should provide a more tangible illustration of how the approach can contribute to the understanding of practical as well as academic problems. The concept is developed in the first instance at the national level, as this is where the contribution of institutional factors to the overall dynamic of the system can best be demonstrated.

2.2.1. Precedents and antecedents

The concept of a national skilling system is novel in the sense that nobody appears previously to have developed an explicit systems model with its function and boundary defined by skill. Its content and configuration derive from a growing body of theoretical literature since the 1970s on national systems of innovation, production or both and their institutional determinants. Amable (2000: 661-665) lists seven main schools of

institutionalist thought that have developed such system models. In addition, an influential tradition of skills-related literature in the UK and Australia has addressed aspects of system failure, in some cases using that explicit term, though without specifically describing a formal system. The model developed here derives principally from five themes in the skills and innovation literatures:

• the skill ecosystem model originally devised by Finegold (1999) and further developed by Buchanan and his collaborators (e.g. Hall and Lansbury 2006). This in turn derives from Finegold and Soskice’s earlier (1988) model of a low-skill, and subsequently a high-skill equilibrium, and is related to the concept of a skill

trajectory used by Wilson and Hogarth (2003: ix);

• the concept of a national innovation system which originated in the 1980s with authors such as Freeman (1982) and Lundvall (1985) and is now commonplace in the innovation literature to characterise the combination of institutional factors which determine the potential of a nation (or on a lesser scale, a region or an industry sector) to undertake different kinds of innovation successfully;

• the matched-firm international case studies carried out by Prais and colleagues for the UK National Institute for Economic and Social Research in the 1980s and 90s, which examined the relationship between national approaches to skill formation and workplace dynamics, and their impact on the sources of each nation’s

competitiveness (Daly, Hitchens and Wagner 1985; Jarvis and Prais 1989; Prais, Jarvis and Wagner 1989; Steedman and Wagner 1989; Mason, van Ark and Wagner 1994);

• the work in evolutionary economics done initially by Lazonick and O’Sullivan (1994) and subsequently continued by authors including Lazonick (2005) and Lam (2005) on the relationship between the institutions of skill formation, organisational forms and national competitive advantage, especially in different styles of

innovation;

• the “Varieties of capitalism” literature (Hall and Soskice 2001) which builds the elements of the last mentioned tradition into more structured comprehensive models of different ways of running a market economy.

Innovation system models, which generally include learning and skill formation among their key processes, form the most direct model for the concept as set out here. This reflects the degree to which they have been articulated in systems terms, and the presence of many common elements. There is considerable overlap between the two types of system, though neither subsumes the other (see 2.2.2.1 below). However, much of the conceptual legacy, and the majority of its evidentiary underpinning, derives from the more conventional literature on skills and work organisation in the evolutionary and

institutionalist traditions.

The skilling system is essentially a larger-scale version of skill ecosystems, defined by Buchanan (2006: 14) as “clusters of high, intermediate and low-level competences in a particular region or industry, which are shaped by interlocking networks of firms, markets and institutions”. One difference, as is clear from this quotation, is that ecosystems are seen as specific to subsets of the economy: Finegold, who invented the concept, gave as one of his main reasons for preferring it over the high/low-skill equilibrium model the evidence that multiple skill ecosystems, with different characteristics and different optimum skill profiles, could coexist more or less independently within the one economy (1999: 63). Consequently, they are seen as relatively fluid and transient, though different authors approach their fluidity in different terms. Finegold is at pains to emphasise the role of chance in the emergence of high-skill ecosystems (1999: 66), but also argues that their sustainability is precarious, and indeed that they often contain the seeds of their own ultimate destruction (1999: 74). Buchanan, taking a different perspective, argues that dysfunctional (low-skill) ecosystems are the product not only of system failure but of coordination failure (2006: 12), and hence that they can be manipulated or even new ones created through collective action directed primarily at the latter.

By contrast, the national skilling system (NSS) is seen as more durable and pervasive, the foundation on which diverse ecosystems can rise or decay at different moments or in different parts of the economy, and a source of constraints as well as incentives shaping the directions in which individual ecosystems can develop. While the outputs of an NSS and

the activities of its constituents are constantly changing, the system as a whole can be expected to behave in relatively constant and predictable ways, changing mostly in response to changes in the underlying institutions, or through the intermediary of such changes in response to exogenous shocks (cf. Hall and Thelen, 2006: 14). In this it has more in common with a national innovation system, but also with the original concept of an equilibrium, with its connotations of path-dependence at the level of the economy as a whole. It is thus more enduring and ubiquitous than the individual ecosystems that develop within it, but less so than the underlying institutions.

2.2.2. Definition of an NSS

The “skilling system” concept developed in this thesis is an eclectic one which builds on aspects of the literature mentioned in 2.2.1 above but does not rest on the authority of any one of the precedent approaches. Consequently it is put forward, in part, without

supporting references because no such prior construct exists. It is presented as the first cut at an evolving concept which is coherent and lends itself to empirical testing but will almost certainly be improved and refined over time with further empirical testing. Once again, though, it should be pointed out that models such as this are meant as heuristics rather than as accurate or authoritative representations of an objectively existing reality.

As foreshadowed earlier, the core of the model is a dynamic interaction between the three key mechanisms of supply, demand and utilisation or deployment. (The latter term will be preferred for the sake of consistency, and coincides with the usage of other authors who have contributed to the development of the concept, notably Keep and Buchanan.) These three elements are further defined in 2.2.4 below.

Other, non-system models also incorporate the same mechanisms. However, a linear model would treat them as stages in a unidirectional cycle: demand leads to supply, leads to

deployment, leads to the creation of new demand, etc. The system model differs in that it sees the three as interacting constantly with one another in a non-sequential, non-

hierarchical way (Fig. 2.1). An alternative way of representing this would be a triple helix, where the three strands interact continuously at all points along their length, and would unravel and dissolve into their component elements once they ceased to do so.

Figure 2.1

Cyclical and system models of interaction between supply, demand and deployment

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