Macroeconomic Theory Of Change And Innovation

From the 'standard' macro-perspective, the core theory of technological change in the aggregate is usually termed 'induced innovation'. This theory has been elaborated qualitatively in several books by Rosenberg (for example, Rosenberg 1969a, 1976, 1982a) and, in more mathematical modeling terms, by Binswanger and Ruttan (Binswanger and Ruttan 1978). Here the fundamental idea is that scarcity induces innovation. For example, economic historians have argued persuasively that the US was short of labor (compared to Europe), but had plenty of good land and fodder for horses in the 19th century. This combination made horse-drawn harvesters and other kinds of agricultural mechanization more profitable to farmers in the US than in Europe. This seems to explain why many innovations in the area of agricultural mechanization, such as the combine harvester (and later the tractor), were innovated - but not necessarily invented - in the land-rich but labor-scarce US.

The theory of induced innovation applies especially to the impact of natural resource scarcity - real or perceived - on economic growth. This topic is so important for this book that we discuss it in a separate section at the end of this chapter.

We note, here, that the induced innovation framework sketched above does not actually explain technological progress or economic growth at the macro-scale. This is because, while the 'bottom up' formulation of microeconomics allows for learning-by-doing and incremental improvement along an established trajectory, it offers no actual mechanism to explain systematic discovery, invention and radical innovation by economic agents. Economists have generally been content to assume that invention occurs spontaneously, perhaps as a consequence of 'monkey curiosity' or something of the kind, and that adoption follows automatically. This issue must be addressed first at the micro-scale before it can be extended to the macro-scale.

At the microeconomic level, one main strand of theory in the literature concerns selection, adoption/diffusion and/or substitution.15 A different strand of theorizing concerns the phenomenon that has been called 'path dependence'. In brief, this can be regarded as an outgrowth of interest in 'chaos' or more particularly the 'butterfly effect' associated with non-linear models. The underlying idea is that infinitesimally different starting conditions can lead to dramatically different outcomes. It follows that many different outcomes are almost equally possible, but that whatever happens depends on what has happened in the immediate past, not on the 'gravitational attraction' of some distant goal. In short, the conventional picture of the economy as a system always traveling along an optimal path, while simultaneously remaining in equilibrium, is false and misleading. As evidence, there are a number of examples of technological choices that have been made in the past, presumably satisfying short-term benefit-cost criteria, but which would not be made the same way today if the original choice set had not been 'locked in' by economies of scale or returns to adoption.

To return to the question of driving forces, the mechanism that drives this knowledge accumulation, including R&D and innovation, is the expectation of increasing financial wealth, via increasing asset values. (Welfare presumably follows wealth, although it is by no means equivalent and the relationship is unclear and certainly non-linear.)

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