Biotechnology and life science-based industries more generally, are now viewed as quintessential elements of the contemporary knowledge economy, and as important drivers of economic growth and dynamism in developed and developing countries alike (Feldman, 2000b; Rosiello, 2008; Thorsteinsdo´ ttir et al., 2004). In recent times, these industries have become the focus of economic development strategies in many national and subnational jurisdictions, with the emphasis on biotechnology in particular (see Christensen, 2003; Feldman, 2003; Feldman & Frances, 2003).
However, this somewhat narrow focus tends to overlook a number of related activities within the broader sphere of life sciences that are themselves very significant sources of innovation and employment (Cooke, 2007, 2008). This approach suggests that it is more useful to consider the life sciences more broadly, to include a wider range of activities related to but not limited to—biotechnology and that draw upon a variety of different technologies and knowledge bases.
At least as far back as Marshall, economists have suggested that organizations store and accumulate knowledge that affects their technology of production. The returns to this production knowledge are generally associated with the concept of learning by-doing. There is an extensive empirical literature that explores the relationship between production experience and plant productivity. Recent studies such as Bahk & Gort (1993), Irwin & Klenow (1994), Jarmin (1994), Benkard (2000), Thompson (2001), Thornton & Thompson (2001), and Cooper & Johri (2002) find that agents and organizations appear to become more productive as they gain experience at producing a particular product or service. The results from these studies suggest that a doubling of cumulative output generally leads to an approximately 20% reduction in unit costs of production. In contrast to these studies, which cover a diverse range of narrowly defined industries, this paper studies the importance of learning-by-doing for generating productivity dynamics within the manufacturing industry as a whole.
Several authors have recognized that learning-by-doing introduces an inter-temporal component to output decisions made by production units. Higher current production rates, which involve an increase in current costs with no immediate effect upon productivity, lead to a reduction in future costs of production through learning-by-doing. Consistent with this literature, this paper treats the stock of experience as a state variable under the control of the production unit, and explicitly models the decision of how much output to produce as a joint decision with how much production experience to accumulate.
The US economy has become substantially more IT intensive in recent years. Figure 1a plots IT’s percentage of total investment in tangible wealth each year from 1987 to 2006, together with the equivalent percentages for equipment and plant (the three values for each year sum to 100). IT’s share of the total nearly doubled during this period, to over 21% of total tangible wealth. Figure 1b, which plots IT stock per full-time employee (FTE) in the US over the same period, shows that annual increases in this measure were particularly large in the latter half of the 1990s when several novel technologies appeared. By 2006, at more than $2,600, it was at a record high, more than three times higher than it had been in 1987. What’s more, because IT is delivering more power per dollar, these increases in spending dramatically understate the amount of real computing power delivered and the consequent potential for changing business strategy, structure and performance. In total the real, quality-adjusted quantity of computing power used by American companies increased by over 33-fold within the past twenty years (Figure 1c).
In this paper, drawing from a series of case studies, we show that contemporary IT, in particular large-scale commercial enterprise applications, has become a means of not only embedding business innovations, but also replicating them with high fidelity across an arbitrarily large intra-firm “footprint.” Today, managers can scale up their process innovations rapidly via technology without the degree of inertia historically associated with larger firms. In other words, they can achieve scale without mass.
