So far, we outlined a conceptualization of GPTs and GPT clusters as spe- cial cases of spillover theories and unbalanced development theories. In what follows we proceed in our construction of an extended theory of GPT, suggesting that the concept of technological multiplier provides a nice de- scription of the process leading to the emergence and establishment of a GPT cluster.
In a critical discussion of Long Waves theory, Rosenberg and Frischtak
(1983) claim that — in order to be able to support the hypothesis of causal- ity running from innovation (mainly a micro phenomenon) to historically relevant fluctuations (a macro phenomenon) — one should assess whether such hypothesis is compatible with a series of conditions regarding causality itself, timing, economy–wide repercussions and recurrence of technological innovation. Fulfilled such conditions, one would be able to clearly qualify technology as primum movens of Long Waves, and to reject the alternative hypothesis according to which innovations are ‘disciplined and structured’ by long term movements. This discussion summarizes the two main lines of research that deal with the issues already identified in Schumpeter’s Busi- ness Cycles (Schumpeter, 1939): the identification of Long Waves (Silver- berg,2003) and clustering of innovation (Silverberg and Verspagen, 2003). The same arguments serve as a yardstick for our extended theory of GPTs. In fact, a GPT cluster and a cluster of radical innovations share a similar characteristic: they occupy a ‘strategic position in the economy in terms of backward and forward links’ (Rosenberg and Frischtak,1983).
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On the analytical side, to take on board this view means that the model- ing of GPT clusters has first of all to be grounded on the micro and meso level of analysis. In such a model an initial localized pulsation (a new up- stream product from existing industries, or the birth of a new upstream industry) enables a wave of forward and backward innovation expenditures as well as positive feedbacks on the locus of the originating ‘push’. Forces playing against positive feedbacks, for example the resistance of existing technologies and the threat of competing technologies, crowding–out effects on innovation incentives generated by other sources of externalities, adverse conditions in terms of macro–prices and expected profits, can contrast the enabling potential of a technology. In this case, a candidate GPT fails to become a GPT, a possibility currently featured only in the model ofvan Zon et al.(2003). If instead the enabling feedback mechanism prevails, dynamic economies of scale kick–in, and a technology suited to be specific becomes general purpose.
If we are ready to borrow concepts from other strands of literature, we can define our generalized approach to GPTs as a fully–fledged technological
multiplier. In the Keynesian view of fiscal multipliers, public expenditures
boost growth via a progressive cascade of increases in consumption and investments (directly affecting, together with expectations, the marginal ef- ficiency of capital). In the same manner a GPT–based inducement effect can generate a wave of additional growth (a crowding–in effect) in downstream industries’ innovative activities. As the effect of the fiscal multiplier can be decreased by the pre–emptive anticipative actions of agents endowed with rational expectations, the potential of the technological multiplier can be de- pressed by the forces contrasting enabling positive feedbacks. As the fiscal multiplier models the potential cascade of economic consequences from an initial expenditure impulse, the technological multiplier captures the per- colation of positive feedbacks through the network of industrial linkages started by an initial technological impulse. The point of origin of the multi- plier impulse can benefit from returning positive feedbacks, therefore further increasing its performance. By improving its attractiveness for additional applications and linkages, it gains pervasiveness and begins the formation of a GPT cluster.
To follow empirically the traces left by a technological multiplier may turn to be a non–trivial exercise, as suggested later in Section 3.6. In any case, the detection of a technological multiplier at work can represent a new cri- terion to identify the process of formation of a GPT cluster, and thus the pervasiveness in the making of technologies and sectors that might dras- tically affect the whole economy. The idea that a multiplier mechanism is at work in the domain of innovative activities can be found in Dietzen- bacher and Los(2002). They studied R&D multipliers using inter-industry flows of embodied R&D and distinguishing between backward and forward multipliers, however without an explicit reference to GPTs. A classic con- tribution by Momigliano and Siniscalco (2013) go as well in the direc- tion to identify the transmission of technological know–how through pro- duction blocks (vertically–integrated sectors) rather than through standard economic branches. Also Eliasson (2011) develops the similar concept of ‘spillover multiplier’ and studies the channels easing the multiplier effect in the context of the Swedish military aircraft industry.
An additional consideration to be made for prospective modeling exercises in the direction suggested by this Chapter regards to the role played by technological complementarities. It is important to stress this point further, since it is one of the missing links in the received microeconomics of GPTs described in Section 3.2. InBresnahan and Trajtenberg (1995)’s definition, GPTs spawn innovational complementarities, a notion that in the model is interpreted in purely economic terms: technical advance is the result of growing returns and profitability, which in turn are properties of the mu- tual feedback structure of the GPT–AS coordination game. The concept of technological complementarity, to be understood as the structure of inter- dependencies and compatibilities between the components of a technology meant as a system of parts, where such components are technological systems themselves, in a recursive way, and are arranged in a modular, hierarchical or near–decomposable architecture (Arthur,2009), is not part of the analy- sis. However, technological complementarity is one of the determinants that affect the dynamics towards prevalence, persistence and pervasiveness of a given technology, because it influences the reach of technological exploration and exploitation and determines the feasibility of certain designs, technologi- cal recombinations and improvements within the fitness landscape (Frenken,
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2006). Moreover, technological complementarity produces micro as well as meso/macroeconomic effects, where learning is an example at the microe- conomic level, while investments in complementary capital and their effects on real output, wages, and productivity are examples at the macroeconomic level.
In fact, the allocation of resources to make old and new technologies ‘tailor– made’ for each other (Aghion and Howitt,1998) and, therefore, the magni- tude of the conversion costs, as discussed byArthur(2009) for the process of technological re–switching, could be one of the factors influencing the sign of the downstream response to upstream spillovers, and one of the explana- tory variables solving the paradox of the lagged effects of new paradigmatic technologies on productivity (Solow,1987). The issue of technological com- plementarity is partially tackled in the growth model ofHelpman and Tra- jtenberg (1994), where the main mechanism leading to the so–called ‘time to sow and time to reap’ fluctuation dynamics, relies on the fact that a new GPT cannot be used until a set of components that are specifically tailored for that technology have been developed by devoting R&D resources to this activity. In that model, GPT–specific — that is, technologically complemen- tary — components are usually produced with a CES production function. This assumption makes components proportionally substitutes, therefore re- laxing a strict interpretation of technological complementarity meant as ar- chitectural compatibility and compositional dependence (Lombardi, 2010). To consider technological and economic complementarities together is there- fore an important criterium to follow in order to model GPT clusters and technological multipliers.
This paragraph suggested a further refinement of GPT conceptualization. We summarize as follows:
An extended theory of GPTs and GPT clusters is in a more general way a theory of technological multipliers, in which an enabling technology affects the sectors to which it is linked, and it is affected by them. The net ef- fect resulting from positive feedbacks, driven by economic and technological complementarity, and negative feedbacks determines if the technology or the sector under consideration succeeds to gain pervasiveness, to establish a GPT
cluster and eventually to influence the direction of an economy’s evolution.