Technical Progress As Knowledge Accumulation

Technical knowledge of this kind has several important characteristics that differentiate it from the other elements of wealth, such as stocks of goods and money or securities. In the first place, 'know-how', as reflected by quantitative measures, seems to increase almost automatically over time. This phenomenon has been called 'learning-by-doing' (Arrow 1962) or 'learning by using' (Rosenberg 1982a). It has been observed and quantified in a wide range of industrial activities, from cigar-rolling to aircraft and ship manufacturing. In some cases, learning is combined with increased scale of output, and in such instances the term 'experience' is preferred to 'learning' (Argote and Epple 1990; Andress 1954; Baloff 1966; Wene 2000; Yelle 1979; Cunningham 1980).15 But in some attempts to endogenize technical change, it has been attributed to 'experience' (for example, Rowthorn 1975; Verdoorn 1951, 1956; McCombie and de Ridder 1984; Rayment 1981; Ayres and Martinas 1992).16

Experience, as well as learning, clearly does have economic value to firms and individuals, though the value is rarely quantifiable except as it applies to easily measurable skills such as typing or brick-laying. More commonly, the economic value of experience (for employees) is attributed to time-inservice or seniority.

From the perspective of this book, knowledge is productive and therefore worth investing in, either for purposes of increasing skills and 'know-how' or - as R&D - in order to promote discovery and invention. Knowledge tends to increase the market value of so-called 'brain workers' but only in an average sense. Knowledge embodied in procedures, protocols, software and designs is productive and therefore adds to the potential profitability, competitiveness and market value of firms. However the knowledge base of any given firm is of little value to others, except possibly a very close competitor in the same business. But knowledge is not an element of economic wealth per se, except to the extent that it can be protected, like patents or copyrights, and exchanged.

The idea that knowledge, in the broad sense, is the driver of human evolutionary progress is quite an old one. We cannot undertake a review of this intellectual history. The biological background is simple enough: knowledge is derived initially from exploration. Humans, like all the higher animals - as well as firms (which are structured groups of humans) - deliberately explore their environments to locate potential sources of food, shelter and danger, that is, to maximize their chances to survive and grow. Animals rely only on memory or observation and imitation of others of their species or social group. Knowledge accumulation among animals, as populations or species, is extremely slow and inefficient by human standards.17

However, while curiosity plays a role and undoubtedly accounted for some of the earliest human discoveries and inventions such as the deliberate use of fire for cooking and for hardening bits of wet clay to make pottery, curiosity alone cannot account for the deliberate and systematic search for new combinations and configurations, to overcome a barrier and solve a specific problem. The same incentives to explore are applicable, although the environment is different and mostly non-physical. Humans and human organizations have steadily improved on this quasi-random process of exploration, especially (in the beginning) by learning to communicate and record information, so that later explorers need not rediscover everything anew. In prehistoric hunter-gatherer times, knowledge was passed from generation to generation by word-of-mouth, using simple sounds and gestures. These gradually became words and sentences. Since then, knowledge has been increasingly codified in language, both verbal and subsequently as pictographs, hieroglyphics, cuneiform and finally alphabets and icons. It has been stored and accumulated in written and physical form, in inscriptions, books, pictures, formulae, blueprints, libraries and computer programs.

Most economic macro-models still assume, for convenience, that knowledge growth is effectively autonomous and self-reproducing - hence exogenous - because knowledge permits the creation of more effective tools for research and discovery. The justification for this assumption is that 'knowledge begets more knowledge'. Telescopes have multiplied our knowledge of astronomy. Microscopes have vastly increased our ability to observe and understand microscopic phenomena. Computers enable us to calculate faster and retrieve archival data faster and test theories more quickly. And so on. From this perspective it is reasonable to assume, as some have done, that knowledge grows exponentially, and without limit (Adams 1918; Sorokin 1957 [1937]; Price 1963; Ayres 1944).

Scholars focusing on knowledge accumulation, as such, have suggested output measures such as the number of publications, journals, patents, or PhDs (for example, Lotka 1939). Other scholars have focused on generic functional capabilities, such as energy conversion, information-carrier capacity, information-processing speed, strength of materials, thermodynamic efficiency or power/weight ratio of engines.18 Some of these measures appear to grow exponentially, over a long period of time, because the upper limits are far away or even unknown. However, in most cases the period of exponential growth eventually comes to an end.

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