An Exploration of Technology Diffusion

Abstract

Most cross-country differences in per capita output are due to differences in total factor productivity (TFP), rather than to differences in the levels of factor inputs.1 These cross-country TFP disparities can be divided into two parts: those due to differences in the range of technologies used and those due to nontechnological factors that affect the efficiency with which all technologies and production factors are operated. In this paper, we explore the importance of the range of technologies used to explain cross-country differences in TFP.Existing studies of technology adoption are not well suited to answer this question. On the one hand, macroeconomic models of technology adoption (e.g., Stephen L. Parente and Edward C. Prescott 1994, and Susanto Basu and David N. Weil 1998) use an abstract concept of technology that is hard to match with data. On the other hand, the applied microeconomic technology diffusion literature (Zvi Griliches 1957; Edwin Mansfield 1961; Michael Gort and Steven Klepper 1982, among others) focuses on the estimation of diffusion curves for a relatively small number of technologies and countries. These diffusion curves, however, are purely statistical descriptions which are not embedded in an aggregate model. Hence, it is difficult to use them to explore the aggregate implications of the empirical findings. An Exploration of Technology Diffusion

Innovation in the collective brain

“…Indeed, this is what we see. Analyses of the diffusion of technologies in 166 countries over the last 200 years suggest that adoption rates have been increasing [86]. Analyses of innovation within surgical techniques, as measured by patents and publications over the last 30 years, show an exponential increase in innovations [87].…”

The Local Influence of Pioneer Investigators on Technology Adoption: Evidence from New Cancer Drugs

“…Prior measures of the speed of technology adoption have primarily focused on absolute rates of take-up, such as the length of time since invention for an individual to adopt (e.g., Comin & Hobijn, 2010) or rate of acceptance (e.g., Griliches, 1957). However, these measures can be inappropriate in settings where the “optimal” level of adoption is difficult to ascertain (e.g., due to informational uncertainty) and may even change over time, as competing technologies are introduced and scientific understanding evolves.…”

Technology Diffusion and Productivity Growth in Health Care

“…Crespi, et al (2008) found as much as 50 percent of total factor productivity growth arises simply from the flow of knowledge across firms. Parente and Prescott (1994, 2002) showed that surprisingly small differences in the rates of technological adoption could imply large disparities in country levels of income, while Eaton and Kortum (1999) estimated that countries realized just two-thirds of the potential productivity gains because of the slow diffusion and adoption of ideas across borders (see also Hall, 2004, Comin and Hobijn, 2010, and Comin and Mestieri, 2013). …”