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ON THE DETERMINANTS AND DYNAMICS OF THE INDUSTRY LIFE

CYCLE

Pier Paolo Saviotti¤, Andres Pyka*, Jackie Krafft§

¤INRA-UMR GAEL, Université Pierre Mendès-France, PO Box 47, 38040 Grenoble, Cedex 9 , France, and I2C CNRS GREDEG, Sophia Antipolis, France.

ppsavio@grenoble.inra.fr, saviotti@gredeg.cnrs.fr

*University of Bremen, Economics Department, Chair in Economic Theory,

Hochschulring 4, D-28359 Bremen. pyka@uni-bremen.de

§ I2C, CNRS GREDEG, Sophia Antipolis, France. jackie.krafft@gredeg.cnrs.fr

First draft. Please do not quote without the authors’ permission,

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ON THE DETERMINANTS AND DYNAMICS OF THE INDUSTRY LIFE CYCLE

Pier Paolo Saviotti, Andreas Pyka, Jackie Krafft.

1) INTRODUCTION The discovery of the industry life cycle (ILC) has been one of the most important developments in industrial dynamics of the last twenty years. A large number of sectors have been found to follow a similar development path, going through the same series of stages which can be described as a life cycle. This biological metaphor means that those industrial sectors following an ILC go from birth to youth to maturity in some sense as a biological organism. This analogy cannot be carried any further and there is no need foe the stages of a biological organism to be the same as the stages of an ILC. What is important is that many industrial sectors follow the same stages in the same order. Given that an ILC is followed by many but not by all the sectors, a very important question arises concerning the determinants of and the conditions under which an ILC can occur. The literature has provided a number of answers to this question. In what follows of this paper we are going to review these answers and to reconsider the nature and existence of the ILC in the context of a model of economic development by the creation of new sectors. As it will turn out, our explanation of the nature and existence of the ILC does not coincide with those previously presented in the literature, although it is not exclusive of them. In the rest of this paper we review the literature on the ILC, discuss the ILC phenomenon by means of our model of economic development by the creation of new sectors and propose our interpretation of the nature and dynamics of the ILC.

2) ON THE CONCEPT OF ILC.

The concept of industry life cycle (ILC) has had a number of precursors which did not use the same name but implied that some technologies could be expected to follow a cyclical behaviour going through a series of stages. Relevant examples of these concepts would be dominant designs (Abernathy, Utterback, 1975), technological regimes (Nelson, Winter, 1977), technological paradigms (Dosi, 1982), technological guideposts (Sahal, 1985), product life cycle (Vernon, 1966, Gort, Klepper, 1982). During the 1990s some scholars started using the expression industry life cycle. Such a concept could be different from the previous ones, and in particular from the product cycle, if an industrial sector were not to coincide with the set of firms producing a common even if differentiated product. As it will turn out, all the sectors for which a life cycle was observed are implicitly defined by their product. Thus, we will see that the ILC is not so different from a product life cycle, as the name ILC would have led us to suppose. However, the scholars who contributed to the study of the ILC did not only change name but improved considerably our understanding of the problem. The ILC is usually governed by the existence of six regularities or principles of evolution: production increases in the initial stages and declines in the final stages; entry is dominant in the early phases of the life cycle and is progressively dominated by exit. A massive process of exit (a shakeout) occurs in the final stages of the life cycle; market shares are highly volatile in the beginning, and tend to stabilize over time; product innovation tends to be replaced by process innovation; first movers generally have a leadership position which guarantees their long-term viability; product variety disappears over time, as a dominant design emerges.

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Amongst these the most important regularity found in all cases of ILC concerns the time path of the number of firms. When the sector is first created usually by a radical innovation, constituted by a completely new object, the number of firms grows from zero to a maximum and then starts declining towards a much smaller value. The point at which the maximum number of firms is attained and after which it starts declining is called the shake-out. Of course, the maximum number of firms or the lower value attained in the maturity phase vary considerably amongst industrial sectors, but the overall pattern remains the same. The other regularities are present in some but not necessarily in all the cases of ILC observed. Jovanovic and Tse (2006) also stress that while most industries face a shakeout where the number of firms declines, the industry output generally continues to rise, suggesting a reallocation of capacity between incumbents and entrants. In industries where technological progress is rapid, they also show that the shakeout of firms tends to occur sooner, and coincides with the replacement of obsolescent capital. In spite of these inter-sectoral variations the concept of ILC is quite a powerful organizing framework for industrial dynamics. The most important problem arising from the observations and stylized facts about the ILC is to explain why and how such a phenomenon occurs. A number of answers have been given in the literature. SHAKEOUT AND DOMINANT DESIGN Utterback and Suarez (1993) develop an analysis of shakeout which is derived from the traditional Schumpeterian hypothesis on the R&D advantage of large firms. Large firms are generally engaged in important R&D programs which generate new products. When a large firm selects one of these new products and decides to launch it on market, this large firm must face a high level of uncertainty affecting both the conditions of demand and supply. On the demand side, uncertainty comes from the fact that the firm does not know the details of customers’ preferences, preferences related to the various possible characteristics of the product. On the supply side, the conditions of production are also highly uncertain and may evolve over time. Different producers can thus experiment with various product innovations having distinct characteristics, and implement different processes of production. These alternative producers engage in a process of competition. Over time, however, uncertainty decreases and selection operates. On the demand side, uncertainty decreases once customers of the new product have tested the alternative characteristics, and acquired experience on what they expect from the new product, which characteristics are more adapted to their personal taste and usage. Eventually customers select a series of product characteristics and demand becomes more predictable. On the supply side, rival producers learn over time and accumulate experience on what customers prefer. In time they also select a series of production techniques which are adapted to low cost production. Since uncertainty decreases, the shakeout appears as an endogenous phenomenon. Product innovation diminishes because most of the actors (producers and customers) are naturally oriented towards the production and consumption of a standardized good. The progressive emergence of a dominant design involves higher barriers to entry which correspond to investments by incumbents in process innovation. Entry is thus limited, and less efficient incumbent firms exit the industry. SHAKEOUT AND TECHNOLOGICAL SHOCK According to Jovanovic and MacDonald (1994) the new industry is created by an innovation. During the subsequent evolution of the industry some incumbent firms create a refinement innovation, which gives them a competitive advantage with respect to their competitors. In their model the shake-out is determined by the exit of the firms which cannot develop or learn the refinement innovation. Firms which had not entered the industry are incapable of

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developing or imitating the refinement innovation. These authors propose a vision of the shakeout very different from Utterback and Suarez’ explanation, based on dominant design. For these authors the shakeout is generated by an external technological shock, exogenous to the industry. The first technological shock sets in with the development of the new product being launched on the market. Entry is stimulated by the emergence of new profit opportunities related to this new technology/new product. Subsequently there is a progressive reduction in profit margins and the industrial structure stabilizes with a limited number of firms in the industry. At this stage, which corresponds to the maturity of the industry, a new technological trajectory emerges and again stimulates the process of entry, simultaneously involving an adjustment of incumbent firms. The process of adjustment is driven by a stochastic process and only a few firms survive this external shock. The shakeout thus eliminates firms which failed to adapt themselves to the new technology. SHAKEOUT, TIMING OF ENTRY, AND COHORTS OF ENTRANTS In Klepper (1996) the industry is created by a major innovation. Firms in the industry are capable of carrying out R&D, of developing product and process innovations, of monitoring and imitating their competitors etc. In Klepper’s models what determines the cyclical behaviour is the presence of increasing returns. As a consequence Klepper relates the shakeout to the timing of entry. The reference is, here again, the Schumpeterian hypothesis on the relation between firms’ size and R&D capacity. But the novelty is that this hypothesis is discussed on the basis of a finer distinction between different types of firms, which can be incumbent, new entrants or latecomers. Process innovation decreases the average costs of large firms, which are the major actors of this type of innovation. However, some key elements may erode the advantage of larger firms. For instance, large firms have to cover specific costs, such as expansion costs, which limit their growth. The activity of R&D can also exhibit decreasing returns to scale over time. Due to these differences early entrants can develop process innovations, sometimes much better than incumbents or latecomers. Early entrants can thus enjoy a leadership position in process innovation since incumbents have to deal with other problems related to their large size and latecomers have to concentrate on product innovation which allows them to grow to a minimum size in order to survive. The timing of entry is thus a major determinant in the formation of a competitive advantage of early entrants relative to incumbents and to latecomers. This mechanism provides an alternative explanation of the shakeout. Each of these explanations is based on different variables. Furthermore, some of the explanatory concepts, such as the dominant design, are not easily measurable. As a consequence for the moment no proper comparative testing of these different explanations has been carried out. Klepper and Simons (2005) tested the different hypotheses but their results for the different industries investigated (automobile, tires, penicillin, television) are highly contrasted, and no general conclusion can be drawn. Furthermore, these different hypotheses are not necessarily mutually exclusive. It is not impossible that different variables are capable of determining cyclical behaviour in a given system. If this were the case the effects of ‘cyclical’ variables could be combined magnifying the cyclical behaviour due to each variable. We will come back to this discussion later on in the paper after having introduced our model of economic development by the creation of new sectors.

3) ILC in EVTEFI In this model the economic system is constituted by an endogenously variable number of sectors. A sector consists of the collection of firms producing the differentiated product

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described by a distribution of models in service characteristics space (Saviotti, Pyka, 2004a, 2004b). Each sector is created by a major innovation. The innovation itself gives rise to what we call an adjustment gap, corresponding to the size of the potential market established by the innovation. The term adjustment gap is due to the fact that at the beginning of the ILC this market is only potential, because neither the required production capacity nor the demand exists. As both of these are created the adjustment gap is gradually closed, leading to the saturation of the market. A sector is here defined as the collection of firms producing the same even if (highly) differentiated product. The sector is established by the first entrepreneur who creates a firm to exploit the innovation in order to achieve a temporary monopoly. If the innovation is successful imitators enter thus raising the intensity of competition. The number of firms within each sector is determined by the balance between entry and exit. As imitating firms keep entering the intensity of competition rises until any further entry is discouraged and exit starts taking place. In this process the once new and innovating sector becomes a part of the circular flow (Schumpeter, 1912, 1934), or of the routines of the economic system. The maturation of pre-existing sectors induces the creation of new ones, as entrepreneurs observe the declining ability of the maturing sector to create profits and shift their investment to new and promising niches, some of which will in turn give rise to new sectors. In this model economic development occurs mainly by the creation of new sectors. As it will be seen later, the number of firms in each sector rises rapidly at first, reaches a maximum and then falls, sometimes to a very low number. Employment follows a similar time path, rising in the emergence phase, reaching a maximum and then falling. This may not entail an absolute fall of employment in a sector, but it entails a fall in employment per unit of output. This time path is partly the result of the assumption that employment per unit of output falls with increasing output size, an assumption which has a strong empirical backing. Due to, the emergence of new sectors, the high rates of growth of output and employment of emerging sectors in their early stages can compensate for the declining ability of older sectors to create both of them. This compensation allows the economic system to avoid the bottleneck that would be generated by the imbalance between continuously growing productivity and saturating demand 5Pasinetti, 1981, 1993). In this process the diversity (or variety) of the economic system grows if the number of new sectors created is greater than the number of those which become obsolete. Structural change is then at the heart of this model of economic development. Competition plays a very central role in this model for two reasons: first, the balance between temporary monopoly and increasing intensity of competition has a great impact on the dynamics of the sector; second, competition is both intra- and inter-sector. In fact, in this model as in any real life economic system, two types of competition, called Schumpeterian and classical competition, coexist while being very different. Schumpeterian competition consists of doing something different, and somehow better, than what everyone else is doing, for example by creating a radical innovation. The objective of this type of competition is to achieve a temporary monopoly, that is, the opposite of competition. If this were the only type of competition, the creation of a sector would lead to a limited amount of economic development. However, entry by imitating firms introduces the second type of competition. Classical competition consists of doing the same thing as everyone else is doing, but more efficiently. The addition of classical competition to Schumpeterian competition allows the economic system to exploit more rapidly the full economic potential of the new sector. Inter-sector competition exists when different sectors supply some common services, as for example in the case of railways companies, airlines and bus companies. The intensity of

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competition perceived by each firm within a sector is thus the combination of intra- and inter-sector competition. This conceptual framework allows us to develop a measure of intensity of competition which can in principle be empirically useful. This measure takes into account the influence of the nature of output on competition. If firm’s output were homogeneous, firms could differ only for their relative efficiency, leading to the differential possibility to charge a lower price or to have a higher profit rate. If firms differ also for the nature of their output, which can occur when the nature of the output is heterogeneous, a greater diversity of competitive strategies can be adopted. It becomes possible to create an output which is different, and not exactly or not at all comparable, with that of any other firm. This form of competition is essentially different from competing to produce more efficiently the same type of output. Elsewhere (Saviotti, Krafft, 2004; Saviotti, Pyka, 2006) we called the former Schumpeterian competition and the latter classical. In Schumpeterian competition entrepreneurs compete by attempting to be first in developing a new product, different from anything which exists, in order to achieve a temporary monopoly, while in classical competition firms attempt to replicate what their competitors are doing, but being more efficient. Thus, Schumpeterian and classical competition are really two opposites, in the sense that the former attempts to avoid the latter. In reality, the existence of these two types of competition is the result of two forces contributing to economic development: efficiency and creativity. The former leads to classical competition and the latter to Schumpeterian competition. In our model relative efficiency can be measured by unit costs and creativity by the rise it determines in diversity/variety. These two forces are jointly involved in economic development and they have a complementary relationship, as described in the following two hypotheses: Hypothesis 1: The growth in variety is a necessary requirement for long-term economic development. Hypothesis 2: Variety growth, leading to new sectors, and productivity growth in pre-existing sectors, are complementary and not independent aspects of economic development. These two hypotheses can be justified by the imbalance between productivity growth and demand growth (Pasinetti, 1981,1993). If productivity keeps increasing all the time while the demand for new goods and services reaches a saturation point, an imbalance arises. If the economy were constituted by a constant set of activities, in presence of growing productivity it would become possible to produce all demanded goods and services with a decreasing proportion of the resources used as inputs, including labour. This imbalance would then constitute a bottleneck for economic development. The addition of new goods and services to the economic system, that is, a change in composition leading to a growth in variety, can be a form of compensation for the potential displacement of labour and of other resources. Variety growth is then required for the long term continuation of economic development. On the other hand, new goods and services can only be generated by means of search activities. The resources required for these activities can only come from the increases in productivity in pre-existing sectors in a way similar to what happened during the process of industrialisation. Then productivity growth in agriculture created the resources required for industrialisation (Kuznets, 1965). Similarly productivity growth in pre-existing sectors creates the resources required for search activities and thus for the generation of new products and services. In a Schumpeterian fashion, the growing productivity of the routines constituting the circular flow creates the resources required for innovation, without which economic development would come to a halt. In this paper the number of sectors existing in our artificial economic system

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at a given time will act as a proxy for variety. Thus, conditions leading to a faster rate of creation of new sectors will enhance the rate of variety growth. It is to be noted that the previous hypothesis N°2 can be considered as the complementarity of efficiency and creativity. Whereas in older models economic growth was implicitly based only on increasing efficiency, our model attempts to show that creativity, as represented by the ability of the economic system to create new goods and services, is an equally important determinant of economic development. Thus, the process of economic growth is not due only to a quantitative change in which the increasing efficiency of given processes provides an increasing quantity of goods and services at constant resources and composition. On the contrary, in our view growth and development are transformation processes which generate both qualitative and quantitative change, these two aspects being combined in such a way that any instance of qualitative change provides the scope for further quantitative improvements. Creativity is the result of search activities, which are a general analogue of research and development (Nelson, Winter, 1982). They can be defined as all the activities which explore the external environment looking for alternatives to the presently used routines. They can encompass R&D but also other activities such as design, construction of scenarios etc, which are not normally included in R&D. In fact, it is possible to divide all economic activities into routines and search activities. In our model search activities can be fundamental or sectoral. Fundamental search activities are carried out to study the external environment, in both its physical and social dimensions, without pursuing necessarily an industrial application but being mainly curiosity driven. In principle they can affect all industrial sectors, although not in the same way. Fundamental search activities can have two types of effects on industrial sectors: first, they can lead to radical innovation, thus speeding up the creation of new sectors; second, they affect differentially sectoral search, having an important effect on science based sectors and a less important impact on more traditional ones. Sectoral search activities affect mainly one sector and tend to be more applied. They are driven by demand, but influenced by fundamental search activities. In this model employment is due to the creation of new firms and to their labour intensity during the life cycle. Labour intensity is obtained by the very simple assumption that labour per unit of output falls with increasing output size. The model computes both sectoral and aggregate employment (Fig 4). A very interesting result of this model is that even when sectoral employment falls, for example during the mature phases of the life cycle, aggregate employment can keep growing if there is an adequate coordination of the decline of older sectors and of the emergence of new ones. This allows to overcome the imbalance identified by Pasinetti (1981, 1993). The emergence of new sectors compensates for the growing inability of older sectors to create employment. 3.1) THE STRUCTURE OF THE MODEL. In the model each sector is described by an equation specifying the dynamics of entry and exit of firms into and out of the sector.

ti

ti

ti

ti

ti MAICAGFAkN −−⋅⋅=∆ 1 (1)

Where k1 is a constant depending on the conditions determining entry, such as barriers to entry, bureaucratic obstacles to the creation of new firms etc, ∆Ni

t is the change in the net number of firms in sector i in period ∆t, FAi

t is financial availability, AGit is the adjustment

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gap for sector i, ICit is the intensity of competition in sector i, and MAi

t is the number of mergers, acquisitions and failures in sector i. From now the subscript t will be neglected for all simultaneous terms, since all terms except the constants are functions of time. The terms with a positive sign describe entry and those with a negative sign exit. Each of the terms of Eq 1 has an explicit form. For example:

iiMaxi DDAG −= , (2)

TotIIi

totiici NRN

NNkIC

+⋅

= (3)

i

itii AG

RANkMA ⋅⋅= −1

9 (4)

( )[ ]{ }01918

00

exp1 SESEkkpYY

DDti

ti

tit

i −⋅−+⋅∆⋅

⋅= (5)

Where DMax,i is the maximum value of demand in sector i, and Di is the instant value of demand; kIC is a constant depending on the general conditions determining competition, such as rules limiting anti-competitive behaviour, and RII is the ratio of inter to intra-sector competition; RAi is returns to adoption; Yi are the services supplied by the product of sector i, �Yi is product differentiation, p0 is initial price, k18 and k19 are two parameters affecting the rate of growth of demand, SEi and SE0 are search activities at times t and zero. As in a logistic equation, the two parameters k18 and k19 have the function of delaying the start of the development (k18) and determining the rate of growth (k19) of demand. Search activities can be sectoral or fundamental. The total mount of search activities is the sum of the two:

�+=i

ti

tttot SESEFSE (6)

)]exp(1[ 540 t

ilt

i DacckkSESE ⋅−−⋅= (7) Where SEF, or fundamental search activities, is the result of investment, together with physical capital and education.

tInvestmentTotalrdshareSEF __ ⋅= (8)

Where share_rd is the share of the total investment (Total_investmentst ) invested in fundamental search activities. The remaining funds are invested either in human or physical capital.

The description of the most typical results of EVETFI follows. EVETFI can predict among others the evolution of the number of firms, of output, of demand, of the intensity of competition etc. Here we will focus on the aspects which are more relevant for the discussion of the ILC. Of course, the first aspect to be considered is the evolution of the number of firms, an example of which is shown in Fig. 1.

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-

25

50

75

100

1 251 501 751

Fig. 1. Evolution of the number of firms in the different sectors of the economic system.

-

0.4

0.8

1.2

1.6

2.0

1 251 501 751

Fig 2 . The adjustment gap AGi

-

1

2

3

4

5

6

1 251 501 751

Fig 3. Intensity of competition ICi

10

0

25

50

75

100

1 251 501 751

Fig. 4. Aggregate employment

As it can be seen from Fig. 1, the number of firms in each sector first rises, then reaches a maximum, and subsequently starts declining. The actual shape of the Ni(t) curve can vary depending on the values of a number of parameters. However, under most circumstances the general shape of the curve (rise, maximum and fall), remains the same as in Fig 1. In other words, the time path of the number of firms in each sector describes a life cycle extremely similar to the one which has been observed in the literature. The adjustment gap, AGi, rises during the initial period of the sector’s life and subsequently it falls to a lower and constant value (Fig. 2). The rise in AGi and the fact that the adjustment gap does not fully close are explained by the improvement in both product and process technology which improves the performance of the product and reduces the unit cost of the services supplied. Improved and more accessible products expand the population of potential buyers while the delay of qualitative saturation determined by the improvement in the services supplied prevents AGi from falling to zero. The intensity of competition ICi follows a path similar to that of the number of firms, rising until it reaches a maximum and then falling (Fig 3). The final value of ICi is determined by the balance between intra-and inter-sector competition. The curve for aggregate employment is the envelope of the sectoral employment curves. Employment within each sector follows closely the pattern of firm creation, rising or falling with the number of firms. The aggregate employment curve (Fig. 4) depends on the shapes of sectoral employment curves and on their relative position, which is determined by the delay between the creation of two subsequent sectors. To the extent that the creation of new sectors is one of the main driving forces of economic development, such delay can be interpreted as a form of inter-temporal coordination. The overall macroeconomic employment profile can be characterized by its level and its rate of growth. These two properties can be measured by the intercept and by the slope of the linearized employment trend (LET), a straight line that best fits the employment trend (Fig. 4). It is then possible to study the influence of several variables on the rate of growth of employment by plotting the LET for different values of the variables and parameters studied.

3.2) ILC AND THE EVTEFI MODEL

It is to be noted that our main motivation to create this model did not have any explicit relationship to the existence of an ILC. The EVTEFI model was created to test hypotheses 1 and 2 above. Thus, its main objective was related to the analysis of structural change in economic development. No explicit intention or assumption which would necessarily lead to the existence of a life cycle was contained in the model. The ILC is here obtained as a consequence of the behaviour of intensity of competition and of demand. The first entrepreneur funds a firm to exploit an innovation in order to achieve a temporary monopoly.

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If the firm establishing the new sector is successful imitative entry follows (a Schumpeterian bandwagon) leading to a rise in the intensity of competition. Given that the temporary monopoly, i.e. the absence of competition, was the inducement to enter, the evolution of the sector will gradually reduce such inducement. As the sector evolves by imitative entry, the rising intensity of competition makes it increasingly similar to already established sectors, which constitute the routines of the economic system, or, in Schumpeterian terms, its circular flow. A cyclical pattern can then be originated by the very same process that gives rise to the new sector. As imitative entry occurs the inducement to any further entry falls gradually until exit predominates over entry. At this point we expect the number of firms to start falling, or the shake out to occur. In Schumpeterian terms the shake out coincides with the transition from the innovative state to the routine/circular flow state of the sector.

The evolution of the adjustment gap, or of market size, provides a further explanation of the existence of a life cycle. The entry of firms into a sector is determined by the size of the adjustment gap AGi. It is to be observed that the adjustment gap is the expected, or potential, size of the market established by the new sector. Since the market is empty at the time of the creation of the new sector, in the long run we expect AGi to fall to low values, and even possibly to zero, corresponding to the achievement of market saturation. A falling AGi leads to a falling rate of entry of firms into the sector, further contributing to the net exit of firms from the sector. The trend towards market saturation thus reinforces the effect of the growing intensity of competition in transforming the sector from an innovative to a mature one. Given its importance in the dynamics of our model the concept of market saturation deserves a more detailed discussion. A market can be considered saturated when all prospective buyers of the goods or services supplied by market do not wish to buy any more of these goods or services except for replacing the ones which wear out and are no longer usable. Or, at least, this would be the definition of market saturation in a world without product innovation. In a market characterized by continuous product innovation purchasing behaviour is not solely determined by the physical obsolescence of products. As new versions, or models, of each product are continuously introduced new purchases can occur even before the product becomes physically obsolete. Such new purchases can be driven by the higher level of services supplied by the most recent versions of the product (Saviotti, Metcalfe, 1984; Saviotti, 1996) relative to the previous ones. This has the important consequence that saturation has to be defined in both quantitative and qualitative terms. A sector is quantitatively saturated when only replacement purchases of the constant type of output it produces, even if possibly with increasing efficiency, can occur. A sector is qualitatively saturated when the product technology of its output stabilizes after having improved very considerably during the previous evolution of the sector. Quantitative saturation can occur for homogeneous products affected at best by innovations in process technology but not in product technology. The possibility of quantitative saturation depends on the existence of a low or decreasing price elasticity of demand for products with qualitatively constant features, which supply a constant level of services. Quantitative saturation is impossible to avoid in a world endowed with growing efficiency but constant output features. Qualitative change in product technology, in the form of new or improved service characteristics (Saviotti, Metcalfe, 1984; Saviotti, 1996), avoids quantitative saturation and can delay indefinitely qualitative saturation. In other words, if product innovation keeps improving products’ service characteristics and if income constraints permit it, purchases of existing products can keep occurring at a rate and with a value higher, and possibly a lot higher, than if the product had been homogeneous and with unchanging service characteristics. Putting it differently, in value terms the onset of saturation can be delayed considerably (or indefinitely?) by product innovation leading to enhanced service characteristics. The previous considerations imply that the adjustment gap is not necessarily going to close or that, if it were to do so, it could do it in a very long time. Thus,

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market saturation could take place in volume but not necessarily in value, and if both were to occur they would not occur simultaneously.

The distinction between quantitative and qualitative saturation is closely related to the two basic forces operating in economic development, efficiency and creativity. Quantitative saturation would exist in a world in which firms’ and industrial sectors output are constant and all that can vary in the course of time is the growth in production efficiency. The possibility of qualitative saturation comes into existence when product innovation can start changing the performance of products in the form of the services that they can supply to their users. Changing output variety, following from product innovation and being the expression of creativity, delays the onset of qualitative saturation. In the previous part of this paper we have already shown that the EVTEFI model gives rise to an industry life cycle which is mainly driven by the intensity of competition and by the dynamics of demand. As we pointed out in the initial section of the paper, the ILC models already published attribute the existence of an ILC to other variables ranging from the emergence of an improvement innovation, of a dominant design, or to increasing returns to R&D. We do not specifically investigate these other variables in our paper but we wish to suggest that several variables can lead to a cyclical behaviour. The relevant question then is then ‘what properties of the variables can give rise to a cyclical behaviour?’. A possible general answer to this question can be given as follows. In order to behave cyclically a variable must have a time path in which it first grows and then falls. Such behaviour is systematically obtained when the time derivative of the variable (Y) depends on the product of the variable itself and of the difference between the maximum and the instant value of the same variable (Eq 9). In this case we can expect the time path of the variable to behave as if there were no limits to its development for very low values of the variable but to start feeling the effect of the limit when the maximum value is approached. At that point the (Ymax -Y) part of the equation will approach zero and will slow down the rate of growth of the variable.

)( max YYYdtdY −= β (9)

We can observe that in the evolution of the capitalist economic system the growing complexity and heterogeneity of products started occurring at a mass level only after what could be considered basic needs were satisfied for the majority of the population. This trend began in the early part of the XXth century in the US and later reached all industrialised countries. The trend towards higher quality, more differentiated products could be interpreted as the natural tendency of consumers becoming more affluent to choose better if more expensive products in preference to cheaper but lower quality ones. Yet, if producers had followed only the efficiency route and had continued to offer progressively cheaper but unchanged lower quality standardized products, the declining propensity of consumers to allocate more of their growing income to the same type of standardized products could have led to the imbalance identified by Pasinetti (1981, 1993) and to a bottleneck in economic development. To the extent that the existence of an industry life cycle is caused mainly by the growing intensity of competition and by the partial or total closing of the adjustment gap, we can expect all the variables which interact strongly with either ICi or AGi to have a potential effect on the existence, shape and duration of the industry life cycle. In the following section of this

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paper we describe a series of experiments which we carried out with our model to study the influence of several variables on the ILC. 3.4) EXPERIMENTS WITH THE EVTEFI MODEL. In this section an experiment consists of a series of model runs with variable values of a particular model parameter. In turn such parameters affect model variables. Each experiment will then be described by the variable and by the parameters which are changed. In each case the results of the experiment will consist of the graphic representation of the behaviour of related variables. Thus, in each experiment there will be one affecting and one or more affected variables. For example, in the first two experiments we vary two constants, called k4 and k5 respectively, both of which affect the time path of sectoral search activities SEi (Eq 6). We know that SEi affects directly several other variables, such as the level of services Yi supplied by a given product model, the extent of product differentiation �Yi, price pi, sectoral output Qi, etc, and indirectly other variables such as demand, adjustment gap, intensity of competition etc. From Eq 6 we can see that k5 determines the rate at which SEi rises from its initial value to a limiting value that it reaches in the long run while k4 determines the limiting value itself. This is based on the assumption that as a technology matures sectoral search activities are going to grow at a decreasing rate. In other words, in the long run sectoral search activities can be expected to run against decreasing returns. In each experiment there will be both microeconomic and macroeconomic affected variables. Amongst the macroeconomic affected variables the rate of growth of employment will play a particularly important role because it will be used as an indicator of the performance of the economic system. EXPERIMENT 1. VARYING THE PARAMETERS, K4 AND K5 , DETERMINING THE RATE OF GROWTH OF SEARCH ACTIVITIES WITH DEMAND. We begin our series with two experiments (N° 1 and N° 2) in which the two parameters, k4 and k5, affecting the time path of search activities are varied. Since sectoral search activities are demand driven, these two parameters determine the rate at which they grow for a given pattern of accumulation of demand. The SEi curve increases from the beginning at a decreasing rate (Eq 6). k4 and k5 determine both the rate of growth and the maximum level attained by SEi. Once more we observe that in EVTEFI search activities influence many aspects of our artificial economic system. Hence we expect that by varying k4 and k5 we are going to affect several aspects of our artificial economic system. Given the form of Eq 6 we expect k4 to affect preferentially the maximum level attained by SEi and k5 the rate at which it grows. Since in this paper our main concern is the ILC we will focus predominantly on the curves showing the change in the number of firms in each sector in the course of time. To display the results with greater clarity we will simply show the variation in the number of firms Ni(t) for one sector, in this case the second. Of course, the results are independent of the sector we choose. In the case of the first experiment we show the effects of changing parameter values on several variables. For the other experiments, given that the same procedure will be followed, we will need to supply less complete results. VARYING K4.

14

- 10 20

30 40

50 60

70 80 90

1 251 501 751 1001

0.01 0.005 0.0075 0.025 0.0125

Fig 5. Effect of varying k5 on the number of firms in the 2nd sector

-

0.2 0.4

0.6

0.8 1.0

1.2

1.4 1.6 1.8

2.0

1 251 501 751k5 = 0.005 k5 = 0.0075 k5 = 0.01

k5 = 0.0125 k5 = 0.025

Fig 6. Effect of varying k5 on the adjustment gap of the 2nd industrial sector

-

0.2

0.4

0.6

0.8

1.0

1.2

1 251 501 751

k5 = 0.005 k5 = 0.0075 k5 = 0.01

k5 = 0.0125 k5 = 0.025

Fig. 7. Effect of varying the parameter k5 on the services Yit supplied by the output of the 2nd

industrial sector

15

0

20

40

60

80

100

120

1 251 501 751 1001

0.01 0.005 0.0075 0.025 0.0125

Fig 8. Aggregate Employment for different values of the parameter k5

- 10 20 30 40 50 60 70 80 90

1 251 501 751

k5 = 0.005 k5 = 0.0075 k5 = 0.01

k5 = 0.0125 k5 = 0.025

Fig.9. Effect of varying the parameter k5 on employment in the 2nd sector

30

35

40

45

50

55

60

1 251 501 751 1001

Linear (0.005) Linear (0.0075) Linear (0.025)

Linear (0.01) Linear (0.0125)

Fig 10. Linearized employment growth

16

- 5

10 15 20 25 30 35 40

1 251 501 751 1001

0.01 0.005 0.0075 0.025 0.0125

Fig. 11. Effect of varying k5 on income growth

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

-0.004 -0.002 0 0.002 0.004 0.006 0.008 0.01

labour growth rates

income growth rates

k5

Fig 12. Effect of varying k5 on income and employment growth.

Several aspects of the artificial economic system are affected by changing k5 :

• The time of creation of new sectors, or equivalently, the rate of creation of new sectors. By raising k5 new sectors are created more rapidly or at earlier times. This is shown for the number of firms (Fig 5), for the adjustment gap AGi (Fig. 6), for the services Yi supplied by the output of the sector (Fig. 7), for aggregate (Fig 8) and sectoral (Fig 9) employment. A possible exception occurs for the highest values of k5, where an inversion of the above trend is observed in some cases.

• In all these figures the maximum value attained by the variable or the area under the curve fall with increasing values of k5.

• The slope of the linearised employment trend (LET) rises moving from negative to positive values as k5 is increased while simultaneously its intercept falls (Fig. 11).

• Income grows faster than labour when k5 is increased even in ranges of k5 in which rates of employment growth are negative.

On the whole this experiment shows that the general effect of rising values of k5, which are expected to raise the rate of growth of sectoral search activities, consists of a faster rate of creation of new sectors but also of a smaller ‘scope’ of each sector. The latter aspect is due to the lower maximum value attained by the variables (ex. the maximum sectoral employment in Fig 9) or by the reduced area under the curves (number of firms in Fig 5 and adjustment gap in Fig 6). These two outcomes of the rising rate of growth of search activities have contrasting effects. The higher rate of creation of new sectors can be expected to contribute positively to the development of the economic system while the falling scope of each sector can be expected to have the opposite effect. This trade off seems to be manifested in the LET shown

17

in Fig 10, we can see that by increasing k5 the slope of LET curves rises while their intercept falls. This result could be interpreted by saying that rising rates of growth of sectoral search activities reduce short term employment levels in order to raise long term employment growth rates. VARYING K4.

- 10

20

30

40 50

60

70

80 90

1 251 501 751

k4 = 5 k4 = 7.5 k4 = 10 k4 = 12.5 k4 = 14

Fig. 13. Effect of changing k4 on the number of firms in the 2nd sector.

-

50

100

150

200

250

1 251 501 751

k4 = 5 k4 = 7.5 k4 = 10 k4 = 12.5 k4 = 14

Fig 14. Effect of changing k4 on aggregate employment.

-5

-

5

10

15

20

25

30

1 251 501 751

Linear (k4 = 5) Linear (k4 = 14) Linear (k4 = 12.5)

Linear (k4 = 10) Linear (k4 = 7.5)

Fig 15. Effect of changing k4 on the linearized employment trend

18

0

0.005

0.01

0.015

0.02

0.025

0.03

-0.05 - 0.05 0.10 0.15

k4

laour growth rates

income growth rates

Fig 16. Income vs employment growth by changing k4

The impact of k4 seems to be much more dramatic than that of k5. For different values of k4 the curve for the number of firms (Fig. 13) changes shape to such an extent that in some cases it becomes difficult to talk of a life cycle. This is true in particular for low values of k4, for which it becomes difficult to identify a shake out. Conversely, by reducing the value of k4, that is by reducing the rate of growth of sectoral search activities with the growth of demand, the life of the sector becomes so long as to render the concept of life cycle irrelevant. Even before attempting to interpret these changes in terms of their underlying causes, this experiment provides a graphically striking illustration of the way in which some factors can determine not only the ‘shape’ of the ILC but its very existence. Equally interesting are the effects of k4 on aggregate employment (Fig. 14), on the linearized employment trend (Fig 15) and on the relationship between employment and income growth (Fig 16). In all these cases we observe a pronounced non linearity in the effects of k4 on the variables investigated. Thus, the rate of growth of aggregate employment (Fig 14) and the slope of the linearized employment trend (Fig 15) first increase and then fall as we gradually raise the value of k4. This nonlinearity becomes even more graphically evident in Fig 16, showing that income grows faster than employment as we start raising k4, but starts falling for higher values of k4. EXPERIMENT N° 2. VARYING THE PARAMETERS DETERMINING THE GROWTH OF THE SERVICES YI SUPPLIED BY THE PRODUCT OF SECTOR I. In the previous experiment we studied the influence of search activities on the dynamics of the ILC. This experiment aims at studying the effect on ILC of another variable, the services supplied by the product of the sector. Such services affect demand, one of the two main factors, together with ICi, which determine the cyclical behaviour of industrial sectors. The rate of growth of the services Yi is itself determined by search activities according to, equation 10:

)](exp[11

01514

0

SESEkkyY

ti

ti −−+

+= (10)

Where k14 and k15 are the parameters controlling the starting time and the rate of growth of the services Yi supplied by the product of sector i.

19

VARYING K14.

-

20

40

60

80

100

120

1 251 501 751k14 = 0.1 k14 = 0.5 k14 = 1 k14 = 1.5 k14 = 2

Fig 17. Effect of k14 on the number of firms in the 2nd sector

-

0.2

0.4

0.6

0.8

1.0

1.2

1 251 501 751

k14 = 0.1 k14 = 0.5 k14 = 1 k14 = 1.5 k14 = 2

Fig 18. Effect of k14 on Yi in the 2nd sector

- 10 20 30 40 50 60 70 80 90

1 251 501 751k14 = 0.1 k14 = 0.5 k14 = 1 k14 = 1.5 k14 = 2

Fig 19. Effect of k14 on employment in the 2nd industry

35

40

45

50

55

60

65

1 251 501 751Linear (k14 = 0.1) Linear (k14 = 2) Linear (k14 = 1.5)

Linear (k14 = 1) Linear (k14 = 0.5) Linear (k14 = 2.5)

Fig 20. Effect of k14 on linearized employment trend

20

-5

-

5

10

15

20

25

30

35

1 251 501 751Linear (k14 = 2) Linear (k14 = 1.5) Linear (k14 = 1)

Linear (k14 = 0.5) Linear (k14 = 0.1) Fig 21. Effect of k14 on linearized income trend

VARYING K15.

-

20

40

60

80

100

120

140

1 251 501 751k15 = 0.1 k15 = 0.25 k15 = 0.5 k15 = 0.75 k15 = 1

Fig 22. Effect of k15 on the number of firms in the second sector

-

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1 251 501 751k15 = 0.1 k15 = 0.25 k15 = 0.5 k15 = 0.75 k15 = 1

Fig 23. Effect of k15 on the development of Y in the 2nd sector

40

45

50

55

60

1 251 501 751

Linear (k15 = 0.25) Linear (k15 = 0.5) Linear (k15 = 0.1)

Linear (k15 = 0.75) Linear (k15 = 1)

Fig 24. Effect of k15 on the linearized employment trend

21

- 5

10 15 20 25 30 35 40

1 251 501 751

Linear (k15 = 0.25) Linear (k15 = 0.5) Linear (k15 = 0.1)

Linear (k15 = 0.75) Linear (k15 = 1) Linear (k15 = 1.5)

Fig 25. Effect of k15 on income trends The results of this experiment can be summarised as follows: Increasing k14 in the range 0.1- 2.0:

• Delays the creation of new sectors • Reduces the maximum number of firms in each sector • Reduces both the rate of growth and the limiting value of the services Yi supplied by

the product of sector i • Reduces the intercept (level) of the linearized employment trend (LET) for lower

values in the k14 range but (non-linearity) the LET starts growing again for higher values of k14.

• Income growth falls regularly with rising values of k14

Increasing k15 in the range 0.1-1.0:

• Reduces the rate of creation of new sectors in the lower part of the k15 range and increases it in the upper part of the k15 range (non-linear effect).

• Increases the maxim number of firms in each sector all the time • Raises both the rate of growth and the limiting value of the services Yi supplied by the

product of sector i • For lower values in the k15 range it leads to a rise in the intercept of the linearized

employment trend and for higher values of k15 it leads to a fall in the slope and possibly in the intercept of the linearized employment trend (non-linear effect).

• Raises the slope of the income trend all the time On the whole we can see that the effects of k14 and of k15 on the ILC are quite contrasting and almost opposite. This was to be expected given that k14 determines the delay in the start of Yi while k15 determines the rate of growth of Yi. Thus, increasing k14 is likely to slow down the rate of creation of new sectors while rising k15 values are likely to increase the scope (size) of new sectors. Thus, the number of firms, the maximum level of Yi, the slope of the linearized employment trend and the income trend are generally negatively affected by a rising k14 and generally positively affected by a rising k15. However, while these expectations are largely confirmed non linear effects appear. Examples of these non linearities are found in the effects of both k14 and k15 on the linearized employment trend and on the number of firms in each sector. The existence of such non linearities is due to the strongly interacting nature of the EVTEFI model. Most variables interact with other variables in the model. For example, when a change is introduced into the model which speeds up the rate of creation of new sectors, it raises simultaneously the intensity of inter sector competition. This may lead to a faster

22

creation of sectors creating less employment per unit of output. Trade offs of this type are likely to lead to non linear effects. 4) SUMMARY AND CONCLUSIONS. In this paper we used a model of economic development by the creation of new sectors, called EVETFI, to study the conditions of existence and the factors affecting the stability, shape and duration of the Industry Life Cycle (ILC). In the EVTEFI model the economic system consists of an endogenously variable number of industrial sectors. The emergence of new sectors is determined by the conditions of existence of incumbent ones. Each sector, defined as the set of firms producing a common even if highly differentiated output (product or service), established by an entrepreneur founding a firm to exploit an innovation induced by the expectation of a temporary monopoly. This inducement to entry is gradually eroded by the entry of imitating firms which raise the intensity of competition to such high levels as to make any further entry uninteresting. This process leads to a fall in net entry and then to a shake out. The evolution of demand reinforces these cyclical features. The innovation giving rise to the sector establishes a potential but initially empty market whose size is measured by the adjustment gap AGi. Rates of entry, which are driven by the adjustment gap, rise or decline with the value of AGi. In the long run demand for the output of any sector has an upper bound. As a consequence the rates of entry are eventually going to decline even if they can rise in earlier periods of the ILC. As the falling inducement to entry determined by demand reinforces that of the intensity of competition, the ILC moves towards greater industrial concentration by the processes of failures, mergers and acquisitions. Thus, in our model the ILC occurs as a natural consequence of the dynamics of competition and of demand. It is to be noted that the EVTEFI model was not designed to prove the existence of the ILC but rather to test two hypotheses about the relationship between variety and economic development. In a general sense the model confirms these two hypotheses since it shows that, even when employment within a sector tends to fall, aggregate employment can still grow provided that the emergence of new sectors compensates for the falling ability of older sectors to create employment. In the EVTEFI model many variables are strongly interdependent. Thus, even if competition and demand are the main factors driving the ILC, other variables can affect the existence, shape and duration of the ILC. In the paper we carry out experiments to study the effect of other variables on the ILC. The two variables that we chose are search activities, which are very ubiquitous in the EVTEFI model, and the services supplied by the output of the sector Yi. The services Yi determine demand and are themselves determined by search activities. In the experiments we vary the two parameters of the logistic equations representing search activities and the services Yi . In each experiment we study the effect of these variables on the number of firms in each sector Ni, on the adjustment gap AGi, on the rate of growth of employment and of income. The results of these experiments show that increasing the rate of growth of search activities tends in general to accelerate the creation new sectors but also to reduce their size. Furthermore, we know from the structure of the model that raising the rate of creation of new sectors can increase the inter-sector intensity of competition. The actual impact of the parameters affecting search activities on the ILC depends on a series of trade offs, such as for example increasing the number of sectors but reducing their size. Unsurprisingly we find a number of non linear effects. For example, raising the value of k4, a parameter affecting the maximum size of search activities, the rate of employment growth first rises and then falls.

23

The results of these experiments show that both search activities and the level of services supplied by the sector’s output Yi have a strong effect on the existence, shape and duration of the ILC. In particular they show that if market saturation could be indefinitely delayed by continuous product innovation, the curve for the number of firms in the sector would become so long as to eliminate the shake out and to render the concept of ILC meaningless. This confirms our general hypothesis about cycle inducing variables. In order to be able to impart a cyclical behaviour on an economic system a variable must have an upper bound. In this case the time path of the variable will resemble that of a boundless one for very low values but will start being affected by the upper bound as it grows. A prototype of this dynamics is given by the logistic equation (Eq 7). Thus, we can expect the cyclical behaviour to be more pronounced the faster and the more complete the process of market saturation and the higher the inter-sector intensity of competition. Both of these conditions lead to a very rapid shake out with a drastic fall in the number of firms. Conversely, if both of these conditions do not apply or if they are very weak, the shake out is very limited or non existent and the life cycle disappears. In the experiments described in the paper we study the effect of search activities and of the level of services supplied by the sector’s output Yi on both micro ad macro economic variables. Amongst the former there are the number of firms in a sector and the adjustment gap, amongst the latter the rate of growth of employment and of income. In this sense these experiments are an extension of previous work in which we showed (Saviotti, Pyka, 2005) that micro dynamics has a profound impact on macro dynamics. The existence, shape and duration of the ILC can have a considerable impact on the time path of employment and of income. Depending on the combination of inter-sector coordination and of the ILC the process of economic development can be characterized by a smooth macroeconomic employment growth profile or by a cyclical one with large fluctuations. Important policy implications are likely to be hidden in this message. The results of our model are not complete. We know that many other variables can affect the ILC. It would be impossible to include them all in a paper. In this paper we wanted to establish the principles that (i) the ILC can be created by the joint dynamics of competition and demand, (ii) that other variables related to competition and demand can affect the existence, shape and duration of the ILC, (iii) that the existence, shape and duration of the ILC can affect the macroeconomic dynamics of the system. We consider that we have established these principles, although many further experiments are required for a full exploration of parameter space.

We conclude this paper by making a reference to the previous literature on the ILC. As we previously pointed out, we do not use the same variables as any of the previous papers to explain the existence and the properties of the ILC. We do not consider the results of our paper incompatible with those already present in the literature. As we already pointed out, many variables can satisfy the conditions required to create cyclical development patterns. It is not impossible that all the explanatory variables so far used in the literature reinforce one another. An extended analysis aimed at integrating all these other variables would require modifications of our model, such as the inclusion of specific firms and sector competencies. These extensions are in our plans and we are going to deal with them in future papers. However, for the time being we have demonstrated that in an economic system innovation structural change and ILC can be created by the joint dynamics of two extremely fundamental variables such as competition and demand.

24

REFERENCES Abernathy, W., and Utterback, J., 1975, “A dynamic model of process and product

innovation”, Omega, 3, 639-656. Dosi, G., 1982, “Technological paradigms and technological trajectories: a suggested

interpretation of the determinants and directions of technical change”, Research Policy, 11, 147-162.

Gort, M., Klepper, S., 1982, “Time paths in the diffusion of product innovations”, Economic Journal, 92, 630-653.

Jovanovic, B., and MacDonald, G., 1994, “The life cycle of a competitive industry”, Journal of Political Economy, 102, 322-347.

Jovanovic, B., and Tse, C., 2006, “Creative destruction in industries”, mimeo. Klepper, S., 1996, “Entry, exit, growth and innovation over the product life cycle”, American

Economic Review, 86, 562-583. Klepper, Steven; Simons, Kenneth L., 2005, “Industry Shakeouts and Technological Change”,

International Journal of Industrial Organization, 23(1-2), 23-43. Kuznezts S. (1965), Economic Growth and Structure, New York, Norton. Nelson, R., and Winter, S., 1977, “In search of useful theory of innovation”, Research Policy,

6, 36-76. Pasinetti L.L. (1981). Structural Change and Economic Growth, Cambridge: Cambridge

University Press. Pasinetti L.L. (1993), Structural Economic Dynamics, Cambridge, Cambridge University

Press . Sahal, D., 1985, “Technology guide posts and innovation avenues”, Research Policy, 15, 285-

305. Saviotti P. P. (1996), Technological Evolution, Variety and the Economy, Aldershot, Edward

Elgar. Saviotti P.P, Krafft J. Towards a generalised definition of competition, presented at the 2004

DRUID conference. Saviotti P.P., Pyka A., (2006) Product variety, competition and economic growth, Presented at

the 11th conference of the International Schumpeter Society held in Sophia Antipolis on June 22-24 (2006).

Saviotti P.P., Pyka A., (2005) Micro and macro dynamics: industry life cycles, inter-sector coordination, co-evolution and aggregate growth, presented at the International Workshop Evolutionary Macroeconomics: Building Micro and Meso Foundations from Studies of Systems of Innovation, Self-Organization, Competitive Processes, Knowledge Networks and Complex Adaptive Economic Systems held at the University of Queensland, Australia, 14th-17th July 2005

Saviotti P. P., Pyka A. (2004a), Economic development by the creation of new sectors, Journal of Evolutionary Economics, Vol.14, 1-35.

Saviotti P.P., Pyka A. Economic development, qualitative change and employment creation, Structural Change and Economic Dynamics Vol 15 (2004b) 265-287.

Saviotti, P. P., Metcalfe J. S., (1984) A Theoretical Approach to the Construction of Technological Output Indicators, Research Policy, Vol. 13, 141-151.

Schumpeter, J. (1934, original edition 1912) The Theory of Economic Development, Cambridge, Mass, Harvard University Press.

Utterback, J., and Suarez, F., 1993, “Innovation, competition and industry structure”, Research Policy, 22, 1-21.

Vernon, R., 1966, “International investment and international trade in the product cycle”, Quarterly Journal of Economics, 87, 2.