Long-term growth driven by a sequence of general purpose technologies

Abstract

We present a Schumpterian model of endogenous growth with General Purpose Technologies (GPTs) that captures two important historical stylized facts: First, from the beginning of mankind until today GPTs are arriving at an increasing frequency and, second, all GPTs heavily depended on previous technologies. In our model, the arrival of GPTs is endogenous and arises stochastically depending on the currently available applied knowledge stock. This way of endogenizing the arrival of new GPTs allows for a model which is more in tune with the historical reality than the existing GPT models.

Big science and innovation: gestation lag from procurement to patents for CERN suppliers

“…They showed that the time lag between the deployment of research resources and the beginning of the stream of private revenues from their commercial applications heavily influences the private rate of return to research. (Basu et al, 2006;Comin & Gertler, 2006;Kung & Schmid, 2015) and forces affecting long-run economic growth (Bresnahan & Trajtenberg, 1995;Schaefer et al, 2014).…”

Energy, Knowledge, and Demo-Economic Development in the Long Run: A Unified Growth Model

“…As will be shown in Section 3, we build on these two recent articles and explicitly introduce a stock of useful knowledge when developing our model. More precisely, we rely on the innovative theoretical approach of Schaefer et al (2014) that tracks down the history of technical improvements that accumulate in a stock of useful knowledge. The latter stock then shapes the pattern of arrival of General Purpose Technology (GPT), which in turn impact the rate of change of all kinds of technical changes -distinguished between learning-by-doing and profit-motivated R&D as in Strulik et al (2013) andO'Rourke et al (2013).…”

“…Following Strulik et al (2013), we have considered two kinds of technical changes: non-profit motivated learning-by-doing occurring in the final good sector, and profit-motivated R&D affecting both final and energy-producing sectors. As suggested by Schaefer et al (2014), all these technical advancements should be interrelated by the evolution of a General Purpose Technology (GPT) referred to as G. Lipsey et al (2005, p. 98) define a GPT as a single generic technology, recognizable as such over its whole lifetime, that initially has much scope for improvement and eventually comes to be widely used, to have many uses, and to have many spillover effects. 23…”

“…Following Schaefer et al (2014), we assume that successive vintages of GPTs, G v,t , are developed endogenously as a result of non-profit motivated activities. In the following setting, we suppose that the level of the current GPT vintage positively affects technical change, i.e.…”

“…Together, those features allow compliance with two stylized facts regarding the historical arrival of GPTs: (i) the initially slow evolution of the efficiency of new GPTs, and (ii) the decreasing time interval between successive GPTs. We differ from Schaefer et al (2014) in two ways. First, following the endogenous growth literature centered on human capital (Jones, 1995;Strulik et al , 2013), we consider researchers (i.e., human capital allocated to R&D) rather than machines (i.e., lab-equipment purchased though financial expenditures) to be the key driver of innovation processes.…”