It has been assumed in the preceding chapters that increasingly advanced knowledge and skills are being required in many processes of production. In the context here it is appropriate to treat knowledge as consisting of skills or capacities. How is the level of advancement of knowledge or skill to be measured? What factors lead to a change in this measure?
Not all skills may be useful. The medieval alchemist had a set of intricate procedures believed to convert inferior matter into gold. Trainee alchemists had to learn this art, perhaps over several years. Likewise, some strange religious rituals may seem worthless to the outsider. We may thus distinguish between those skills that serve human needs, and those that do not (Doyal and Gough, 1991). However, the techniques of measurement proposed in this chapter do not depend upon such a criterion. Instead of need, it is possible to consider those skills that are called upon by market demand, or by any other rule that may be chosen.
This chapter is devoted to issues of measurement and analysis. We are concerned primarily with the measurement of skills and capabilities. A first approximation is: the measure of a skill is the amount of time it takes to achieve that level of skill. Knowledge and skills take time to acquire and that duration is their magnitude.
Consider an example: a brain surgeon. Overall, the acquisition of such a skill requires something in the order of (say) thirty years. All necessary education from birth is included in this calculation, including primary socialisation in the household, elementary literary skills, basic schooling and specialist education at university and elsewhere. In contrast, a manual labourer may be able to acquire the necessary skills for labouring work in about half the time. In general, the shift from action-centred to intellective abilities involves a substantial increase in the time taken to acquire the required skills.
Note that this is not the same as the Marxian method of computing and assessing skills. Marx saw skill as being enhanced by the labour of the instructor. For Marx, labour time was a 'substance' that flowed around the economy. He saw learning as the flow of labour from teacher to student, into a stock of congealed talent. Factors such as the student-teacher ratio are relevant in this approach, because the total labour outflow from the teachers has to be divided among the students. In the Marxian approach there is a conservation principle; as long as it is not wasted, labour time is conserved in the transfer process.
Here the approach is quite different. There is no notion of labour embodied, or of labour as a substance being transferred to and embodied in the commodity output. Furthermore, unlike Marx, no direct account is taken of the labour time of the teachers or trainers passing on the skills, nor the materials used in the education process. There is no flow, nor conservation, of a labour substance. Instead the picture is of the building up of skills and capabilities through time. Skill is here measured as a stock, not as a flow. Although a temporal measure is being proposed here, it is not the same as the Marxian concept of labour time.
The first difficulty that we may address concerns the existence of different physical and mental talents and aptitudes. Not all people may have the ability to become brain surgeons within thirty years. Some may never be capable of such work, just as some may not be able to develop an adequate ability to become a manual labourer. In the Marxian approach this difficulty is dealt with by considering the potential of the average person, ignoring any variation about the mean. The essentialism of this approach is rejected here, in part because of its neglect of the ineradicable variety of human ability and skill.1
Instead, the proposed solution to this difficulty is as follows. First, we address the division of labour within the economy at a given level of development. The division of labour will depend on the composition of final output, including the pattern of consumption. A proportion of the working population will be allocated to each skill. We assume that training procedures have been optimal in allocating human abilities to jobs, in the sense that any change in the allocation of people to training schemes, or subsequently to jobs, would eventually reduce the net output the economy.2
Accordingly, the metric of skill is amended in the following manner. The measure of a skill is the minimum amount of time that it takes the proportion of the population allocated to that skill to acquire that skill, given the currently optimal allocation of labour. This is measured on a per capita basis for each skill. For each person involved, the minimum amount of time required to acquire that skill will be calculated. The measure of a particular skill will be the mean value of these minima.
The above formulation can be amended to deal with cases of team production, where the output is the product of a group, rather than a whole economy. The skills may be complementary and inseparable, and it may not be possible to identify the contribution of each individual. In this case the measure of the skill of the team is the average amount of time it takes for each member of the currently most efficient possible team to acquire the skill that is used within the team.
The approach developed here can also be applied to organisations and firms, as well as to whole economies. By tracking human skills and capabilities down to a quantifiable metric it could serve as an important analytical instrument for what is now known as the 'capabilities', 'resources' or 'competence-based' theory of the firm (Foss, 1993; Foss and Knudsen, 1996; Hodgson, 1998b, 1998c; Penrose, 1959; Senge, 1990; Teece and Pisano, 1994). Like much of this literature, there is a stress on capacities and potentialities and not simply immediate, manifest performance. However, we must leave the development of this application, as well as various technical problems involved in the comparison of a sector of the economy with the economy a whole, to a future study.
For the remainder of this chapter we shall consider the skill level in the economy as a whole. We also add a further complication at this stage, concerning the dynamics of development through time rather than static, cross-sectional variation. The measure of each skill is likely to change, as the economy develops. A number of factors may be considered here. The first is innate ability. We shall then move on to discuss the educative and cultural environment, the changing efficiency of the processes of education and training, and the effects of technical change.
Consider innate ability. In the modern, Darwinian view of human evolution, the innate potential of human beings has changed little in thousands of years. Contrary to the Lamarckian view, characteristics acquired during the development of each human individual are not passed on in the genetic makeup of the next generation. Accordingly, the human genetic stock can change only very slowly, by the processes of natural selection. As a result, changes in innate ability are too small to be significant in the much shorter time scale to be considered here.
In contrast, human culture and human institutions change relatively rapidly. Much can happen in a few hundred years that cannot be explained in terms of the much slower changes of human genetic material. The changes in culture and institutions create the potential for human learning and human development. With a more sophisticated technological and scientific culture, even greater human learning is possible, not because of the changes in genetic make-up or innate ability, but because of the possibility of a more advanced education. What changes with the advance of science, technology and human institutions is not genetically endowed human nature but the greater social capacity for educative nurture.3
The major effect of these cultural and institutional advances is to increase the educative and skill potential of the population. An increase in the level of skill and relevant knowledge of a human population will be represented as an upward movement in the learning frontier. By definition, at that frontier the system as a whole has reached the maximum level of learning and skill, given its level of development and state of scientific and technological knowledge. No further overall productivity improvement is possible via some reallocation of labour.
The extent of the learning frontier is measured by the average amount of time it takes the population to reach the levels of skill that are deployed at this optimal position. The upward movement of the learning frontier is the main quantitative representation of the general advancement of human knowledge and development in the system.
As the learning frontier moves upwards, individuals enjoy enhanced possibilities of educative development. These enhanced possibilities do not result from significant changes in innate capacity but from changes in the social culture, socio-economic institutions and the level and availability of knowledge. To use the appropriate biological terms, phylogenetic evolution (involving changes in the genetic material) is much too slow to have any significant influence on socio-economic evolution. Nevertheless, the rapid changes in the social environment are a moving ceiling for the ontogenetic development of each human individual. (Ontogeny is the development and growth of a single organism, without genetic changes.) The actual (that is, 'phenotyopic') development of any particular organism depends, additionally, on the stimulation and nutrition it receives from its environment. And a movement in the learning frontier provides the possibility of this stimulation.
A secondary effect of the aforementioned cultural and institutional advances is to increase the efficiency of education in each specific skill. These advances may not simply improve the general potential, but also the time taken to reach any point up to that potential. In particular, a stimulating and educative cultural environment will increase the rate at which many people - of given innate capacities - can acquire a skill.
The effect here is similar to that of Alfred Marshall's (1949) notion of 'external economies'.4 Like the concept of 'external economies' in the classic text of Allyn Young (1928) and the subsequent work of Nicholas Kaldor (1972, 1978, 1985), they cannot be encompassed by the standard textbook concept of increasing returns. Increasing returns are manifestations of a given and static production function. Instead, 'external economies' are cases of interdependence, the results of which are not manifest instantaneously, but only through adequate time. In part to avoid this confusion, and to further the conceptual separation from the increasing returns phenomenon, they shall be termed here as learning externalities. We are referring to the reduction of the time taken in the learning of a skill due to a more stimulating or appropriated cultural, intellectual, scientific or technological environment. This environment is itself the product of the general improvement in the level of knowledge and skill throughout the economy. Typically such improvements have externalities, or spillover effects.
In addition, people may stumble upon quicker and more efficient ways of doing things themselves. The sum total of all reductions in the time taken to acquire a skill, with given tools, machines and equipment, will be referred to as learning efficiencies. Learning efficiencies are made up, in part, of learning externalities.
In quantitative terms, such improvements in the efficiency of skill acquisition have the effect of deflating the measure of the skill. Unless due correction is made, over time the skill level - measured by the minimum amount of time it takes to acquire the skill - will appear to be less than it is, measured consistently by former standards. Improvements over time in the efficiency of skill acquisition mean that skills are being measured with units of changing size.
Technical changes that replace specific skills will also have a negative effect. For example, the development of the mechanical or electronic calculator may render unnecessary the skills of mental arithmetic. The invention of the automatic gearbox reduced the number of skills that were necessary to drive a car. Harry Braverman (1974, p. 225) cites a study which showed that, due to the introduction of more numerous and accurate measuring and monitoring instruments, the additional time taken to train a coal-tar distiller was reduced from six months to three weeks. In general, as a result of technical advances, less training and education of human operatives will be required to reach the same level of productive capability.
At this stage it is useful to make a distinction between a skill and a capability. A capability is a specific task, whether aided or unaided by tools, machines or other technological devices. Skills are the direct human contributions to capabilities. Skills empower the work that is directly performed by the human agents involved in that task.
If an accountant no longer requires the ability of mental arithmetic, and can use a calculator instead, then the measure of the skill of an accountant may be reduced but the capability to perform the task of accounting remains undiminished. The chores of mental arithmetic can be replaced by electronic calculators, thus diminishing the skill of accounting, but not diminishing accounting capabilities. Similarly, much of the growth in the use of machines over the last two hundred years has been to substitute for human muscle and effort. There would thus be losses in physical strength, dexterity and stamina. In both cases some skills have declined will capabilities have been constant or enhanced.
Both skill-replacing technical changes and learning efficiencies lead to a diminution of the time required to acquire the skill. However, the capability level or capability frontier has not declined. The system still has the same level of capability. Furthermore, learning efficiencies arguably represent no reduction in the true skill level, but merely a reduction in the time taken to acquire the skill. Some method must be found to compensate for this measure, to bring it into line with the true skill level.
Consider the following example. We start from a representative sample or 'basket' of established capabilities and skills at a given point in time. At t0 the average time taken to acquire the skills involved in these capabilities would be measured. Let us assume that the result is 20 years (of time taken to acquire the skills). At this point we do not attempt a numerically separate measure of capabilities, despite the fact that were it not for technical aids the time taken would be greater, because we are primarily interested in changes through time rather than absolute amounts. Hence this difference is disregarded in the first instance. We could imagine an 'early and rude state of society' where production was accomplished with bare hands, unaided by tools or machines. However, in practice it would neither be practical nor meaningful to obtain the data going back to those prehistoric times. It would be more sensible to start from a base year, in the modern period, and trace the contributions of technical changes and acquired skills from that date on. This base year is t0.
One decade later the same calculation is performed on the same representative sample of capabilities. Let us assume that a decrease of time taken of two years takes place and that the resulting measure at t10 of the skills involved in the same sample of capabilities is thus 18 years. This reduction is due to both increased efficiencies in learning the skills and better technical aids, replacing skills. In this illustrative example, it will be assumed that skill-replacing technical changes cause half of this decline and learning efficiencies account for the other half. Overall, the crude skill measure has deflated by 10 per cent. Nevertheless, in reality, there has been no change in the capabilities being delivered. Accordingly, a 10 per cent upwards adjustment is required to the value at t10 to take account of the fact that the two values at t0 and t10 are in fact measuring the same capabilities.
The crude measure of skill, which was 20 years at t0 and 18 years at t10 we shall call the unadjusted skill level. With the same capabilities, the capability level is constant. The capability frontier would thus be horizontal, at 20 years. The learning frontier is a third measure, which must reflect the real de-skilling involved. In this example, the learning frontier has moved from 20 years at t0 to 19 years at t10, representing a 5 per cent decline due to skill-replacing technical changes alone. In other words, at t10 a 5 per cent upwards compensation to the unadjusted skill level is required to take account of learning efficiencies.
We also have to take into account the fact that representative basket of capabilities may change through time. Although these factors create difficulties for the proposed measures of the skill and capability levels, they do not render the methods of measurement invalid. Problems concerning the inflation or deflation of a unit of measure are commonplace in socio-economic systems and a whole set of techniques have been developed to deal with them. The formulation of the widely used index of retail prices is a prominent example.
We now amend this example to take account of the fact that new skills and capabilities have become established in the economy in that decade, because of qualitative economic developments. Hence at t10 the representative sample of capabilities has to be altered. Assume that the new representative basket of capabilities has an average time of acquisition at t of 21 years. The representative level of skill of 21 at t10 is the new unadjusted skill level. Taking this into account, the new unadjusted skill level has moved from 20 years at t0 to 21 years at t10.
According to the original measure of skill at time t0, this unadjusted figure of 21 (pertaining to t10) must be inflated by 5 per cent to make it commensurate with the former measure (pertaining to t0), and to recognise the effect of learning efficiencies, giving a result of about 22.5 Hence, by this method of computation, in the 10 years the learning frontier has risen from 20 to about 22. This rise is powered by the emergence of new skills and learning efficiencies, but not by any skill-replacing technological changes.
A further compensation is required to update the capability frontier. The figure of 21 (pertaining to t10) must be inflated by 10 per cent to make it commensurate with the former measure (pertaining to t0), and to recognise the effect of both learning efficiencies and skill-replacing technological changes, giving a result of 23.5. Hence, by this method of computation, in the 10 years the capability frontier has risen from 20 to 23.5. This rise is powered by both new skills and skill-replacing technological changes.
An analogous method of adjustment is used in the computation of the price index in modern economies. In a similar manner, a regularly updated basket of goods is chosen as the standard. Likewise, a scalar index is derived and used to obtain comparable values over time.
To repeat: the learning frontier is a comparative and intertemporal measure of the skills of the population. The capability frontier is a measure of average productive capacity or sophistication of the economy as a whole, taking into consideration both human beings and the means of production. The unadjusted skill level is a measure of the time per capita devoted to education and training. Hence the unadjusted skill level is equivalent to the mean training time for the population. Assume that it is possible to measure these three regularly, involving periodic updates of the representative basket of capabilities. Three time series of data will result, capable of diagrammatic representation. Two possible scenarios are presented in Figures 10.1 and 10.2. Figure 10.1 shows a picture consistent with the advance of complexity and human knowledge in the socio-economic system.
It is now possible to represent the omega scenario - as discussed in the preceding two chapters - in the above framework. According to the omega scenario, technical changes involving machines, computers and robots lead to a replacement of mental and manual human labour in some areas. There is an overall stagnation in the level of learning in the economy, even if the economy as a whole is growing in output, measured conventionally (Rifkin, 1995). The periodic update of the basket of representative capabilities shows that some highly-skilled jobs are disappearing, as they are taken over by machines. The average time taken to acquire the representative basket of capabilities is decreasing.
This is represented in Figure 10.2. This figure also shows a picture consistent with the advance of complexity in the socio-economic system. Accordingly, there may also be advances in human knowledge. However, for much of the population there is substantial human deskilling.
In Figure 10.2 both the unadjusted skill level and the learning frontier decline, even when the capability frontier moves upwards. Overall, the economy is growing in technological sophistication and output, but, on the average, enhanced human skills are not being deployed. Furthermore, human learning is diminishing in its extent, requiring less and less time to train the average worker. By this measure, assumption 2 in Chapter 8 is no longer valid, even if assumption 1 remains.
As noted above, many of the technological advances over the last two hundred years have substituted primarily for human muscle and effort. Production has been revolutionised as a result. However, in the terms explored here, machines substituted for muscles that did not take much time to strengthen. If a machine simply substitutes for muscular effort, then the reduction in time for skill acquisition is simply the amount of time that no longer has to be devoted to reaching the level of physical fitness to work efficiently. The loss of training time due to that replacement would not have been that great. These losses could have readily been compensated by the acquisition of new skills that would require training, in equal or greater measure.
However, in the last two hundred years the time taken to obtain representative and widely used skills has increased significantly. Many of the innovations of the late twentieth century, such as the computer, may lead or have led to a greater proportionate reduction in the time required to acquire the skills associated with specific capabilities. This is because such innovations substitute for a wide range of relatively sophisticated mental abilities which originally would take a longer time to acquire. It is very likely that intellective skills have a greater metric. Accordingly, many of the technical innovations of the late twentieth century may have lead to a proportionately greater destruction of specialised skills, measured by the time required to achieve that skill.
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For example, the development of computer-aided design and manufacture (CAD-CAM) has made obsolete a whole range of skills from technical drawing to manual lathe operation, that each took many years to acquire. They are often replaced by computer programming and operating skills, perhaps of similar measure. But whatever the gain or loss after replacement, consider the scale of destruction of specialist skills involved in the transition. The dimension of the loss of skill involved in CAD-CAM is much greater than that associated with, for example, the earlier and highly consequential transition from the horse-drawn plough to the tractor. As the measure of skills increases, there is more retraining to be done as the skills are replaced. The march of technical progress thus imposes increasing costs of transition, and requires increasing education and (re)training to compensate for the destruction of old skills. The costs of change increase in proportion.
Overall, in the modern economy, the gap between the capability and the learning frontiers may widen more rapidly because of the increasing relative and absolute contribution of technical change. Without other counteracting forces, this greater gap can be associated with a stagnation or decline in the net and unadjusted skill levels. Accordingly, the omega scenario emerges as a much greater threat in the modern than in any former era. Contrary to the thrust of Braverman's (1974) Labor and Monopoly Capital, modern technical change threatens to de-skill mental more than manual labour, at least by the measures of skill adopted in the present work. The antidote to this scenario does not sensibly involve a slowdown in the rate of technical change, nor the stifling of learning efficiencies, but a greater rate of growth of the unadjusted skill level. Quite simply, this means more time spent on education and training, sufficient to outpace the accelerating and combined impact of technical aids and improving learning efficiency.
Alternatively, it could be argued that it is sufficient to halt the omega scenario by an upward move in the learning frontier, rather than the unadjusted skill level. This means that human skills are increasing but the time taken to acquire those skills is possibly being reduced. This would be a dangerous economy in resources, because it would involve a contraction in the time, routines and institutions dedicated to education and training. For its success it would depend on ongoing improvements in learning efficiencies which are often delayed and unreliable. Furthermore, it would involve a severe constraint on the growth of human skill: a worthy end in itself. It would be far better to expand the unadjusted skill level, as well as the learning capability frontiers, and use the gains made by learning efficiencies for improvements in the level and scope of human knowledge. Some further policy implications of this analysis are taken up in the next chapter.
As well as being quite different from the Marxian focus on embodied labour time, the approach here also contrasts with the neoclassical idea of flows of factor inputs - involving symmetrically both labour and capital - being responsible for economic growth. Instead, the analysis centres asymmetrically on levels of human knowledge and skill - often misleadingly described as 'human capital'. Furthermore, the measure of economic development is not in terms of the value of the produced output of goods and services. Instead the primary measure is of human knowledge and capabilities, embodied in individuals and institutions.
This is consistent with Thorstein Veblen's (1915, p. 272) view that this 'immaterial equipment is, far and away, the most important productive agency'. In consonance with this standpoint, material products are no longer the main focus and measure of economic growth. Machines and tools are important, but products of, and subservient to, the advance of human learning. The symmetry between 'capital' and 'labour' in mainstream economics is broken. Humanity, rather than material goods, becomes the centrepiece of economic science.6
Accordingly, the gap between the capability frontier and the learning frontier should not be interpreted simply or primarily as the 'contribution' of machinery and equipment to economic capability or development, just as the learning frontier is seen to represent the contribution of human labour. A reading based on the assumed symmetry of 'factors of production' with each supposedly making a 'contribution' to output is inappropriate. This is not simply because what is involved here is a measure of capabilities rather than output. It also should be noted that some capabilities - such as the ability to fly and travel at high speeds - will not be expressed in the capability frontier because they are not human skills that have been replaced by machines. The significance of the capability frontier is as a relative measure, changing through time. The gap between the capability and learning frontiers is a measure of deskilling of human labour; it partially represents what is no longer necessary and what has been lost in human skills.
In the history of civilisation, the invention of writing diminished the skill of memorising and relating from memory; the invention of printing diminished the skill of oral storytelling; the machine diminished the exercising of our muscles; the typewriter diminished the skill of calligraphy; the radio and television diminished the arts of home self-entertainment; the pocket calculator diminished our aptitude in mental arithmetic; the computer spellcheck program may diminish the knowledge of correct spelling and the computer thesaurus may decrease our instantly recallable vocabulary. Simultaneously, however, these developments created new specialist skills and professions such as the clerk, printer, publisher, mechanical engineer, mechanic, telecommunications engineer, electronic engineer, and computer programmer. They created a new knowledge - not held by all but nevertheless accessible to some - within a more complex set of social relations and division of labour. In a sense, for most of us, this knowledge is held on our social culture but not in our brains.
These developments represented both a loss and a liberation. On the positive side, not only did they create new professions and skills, they also freed up the processes of human work and created possibilities for the development and acquisitions still more skills. The diminution of the burden of manual work by the machine in the nineteenth and the twentieth centuries has liberated an enormous amount of human time for much more creative activity, as well as created the threats of an ill-exercised and obese population. The new information technology will likewise bring new possibilities for and threats to our intellectual and mental progress. The advance of the capability frontier can stimulate further learning, as well as stunt its development. This, perhaps, is a crucial dilemma for the new millennium.
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