The complexity of the intangible digital economy: an agent-based model

Since 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. This research work as a whole is aimed at investigating potential consequences on our economy deriving from digital technological progress. In particular, the contribution of the thesis is both empirical, theoretical and related to model design. On the empirical side, I 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. The analysis results are used to inform the first of the two frameworks of the agent-based macro-model Eurace that I employ to assess the long-term impact of digital investments on economy. In particular, in the first framework, a total factor augmenting approach has been used in order to model the digital technological progress because of the significant and positive correlation between total factor productivity and ICT capital investments, composed by a combination of both tangible and intangible investments which includes ICT technologies, software and database. In the second framework, I propose a different and innovative approach in which digital technological progress influences the elasticity of substitution between capital and labour. In this way, an increase of the elasticity of substitution can be seen as an increase in the tasks that machines can perform replacing human beings. In order to develop this approach, I substitute the Cobb-Douglas production function used in the first framework with a Leontief technology in which input factors are represented by organizational units. In turn, the contribution of each unit is given by a combination of capital and labour. The second framework results to be more realistic because it allows to distinguish between the various activities performed in the companies and the different education levels characterizing the workforce employed. Computational experiments show the emergence of technological unemployment in the long-run with a high pace of intangible digital investments. However, in the elasticity augmenting framework compensation mechanisms work more effectively leading to lower unemployment levels compared to the total factor augmenting one. Both frameworks are able to capture interesting features and empirical evidences characterizing our economic system.