Abstract
For the last 30 years, the economy has been undergoing a massive digital transformation. Intangible digital assets, like software solutions, Web services, and more recently deep learning algorithms, artificial intelligence, and digital platforms, have been increasingly adopted thanks to the diffusion and advancements of information and communication technologies. Various observers argue that we could rapidly approach a technological singularity leading to explosive economic growth. The contribution of this paper is on the empirical and the modelling sides. On the empirical side, we present a cross-country empirical analysis assessing the correlation between the growth rate of both tangible and intangible investments and different measures of productivity growth. Results show a significant correlation between intangible investments and both labor and total factor productivity in the period after the 2008 financial crisis. Similarly, both measures of productivity growth are correlated with a combination of both tangible and intangible investments which include information and communication technologies and software and database. These results are used to inform the enrichment of the agent-based macro-model Eurace that we employ to assess the long-term impact on unemployment of digital investments. Computational experiments show the emergence of technological unemployment in the long run with a high pace of intangible digital investments.
Highlights
In his 1930 lecture “Economic possibilities for our grandchildren,” John Maynard Keynes predicted that in 100 years from i.e., around 2030, the production problem would be solved and there would be enough for everyone but machines would cause “technological unemployment.” McKinsey Global Institute in a recent report1 stated that the increasing adoption of automation technologies, including artificial intelligence and robotics, will generate significant benefits for the economy, raising productivity and economic growth, but with a far-reaching impact on the global workforce
Building on the empirical analysis presented in the previous section, which shows a clear effect of investments in ICT&software and databases (Soft&DB) on total factor productivity (TFP), we have developed a new extension of the well-known large-scale macroeconomic model Eurace (see Raberto et al (2012), Teglio et al (2012, 2019), Petrovic et al (2017), Mazzocchetti et al (2018, 2020), Ponta et al (2018), Bertani et al (2020))
“Hard” capital shall be considered as a generalized mean of production which represents the physical part of machine tools, computerized numerical control machines, robots, computing equipment, communications equipment, etc. These means of production need software in order to work; in particular, we assume that each unit of “hard” capital needs to be associated with a digital asset license, each consumption good producer (CGP) selects a specific digital asset to install on its own stock of hard capital among the different digital technologies available on the market
Summary
By Vivarelli (2014), six different economic forces counteracting the process innovation labor-saving effect can be distinguished, namely the compensation mechanism “via additional employment in the capital goods sector,” “via decrease in prices,” “via new investments,” “via decrease in wages,” “via increase in incomes,” and “via new products.”. Arthur has investigated since the 1980s the economic features of software and generally of intangible digital technologies as well as their effects for business and the economy as whole (see Arthur (1989, 1990, 1994, 1996)) He pointed out the existence of two different economic realities: the so-called diminishing and increasing returns world.
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