This paper revisits the traditional ‘circular flow’ of the macroeconomy (Samuelson, 1948) and reworks it to capture the use of big data and artificial intelligence in the economy. The characterisation builds on the multifaceted role of data to conceptualise markets and differentiate them depending on whether data is an output, a means of payment, or an input in knowledge extraction processes. After this, the main differences between the circular flow economy and the data economy are described, identifying the new flows and agents and the circular flow assumptions that do not seem to be as relevant to the workings of the data economy. The result is a ‘semicircular’ flow diagram: unprocessed data flow from individuals, families, and firms to data holders. Only data processed in the form of digital services flows back to families and firms. The new model is used to explore the potential for market failures. Knowledge extraction to generate digital services occurs within a ‘black box’ that displays natural monopoly characteristics. Data holders operate simultaneously in the markets for data generation and knowledge extraction. They generate the amount of knowledge that maximises their profit. This creates data underutilisation and asymmetries between data holders and other agents in the economy such as anti-trust authorities, central banks, scientific communities, consumers, and firms. Public intervention should facilitate additional generation of knowledge by developing additional merit and non-rival uses of data in such a way that knowledge generation maximises the social gain from digitalisation. The semicircular model can incorporate data leakages and knowledge injections activated by data taxation. Data taxes should be paid with data respecting existing legislation, privacy concerns, and preserve the incentives of the data holder to innovate in competitive data generation markets. A centralised data authority, as initially proposed by Martens (2016) and more recently by Scott Morton et al. (2019), would be responsible for knowledge generation and aim to achieve better regulation, standards, and transparency, and maximise common good. Our conclusions are in line with an extensive user-centric approach to data portability (De Hert et al., 2018). This paper contributes to the digital economy discussion by developing a simple theoretical motivation for increased access to data for the public good, which will stimulate further theoretical and empirical exercises and lead to policy actions.
DE PEDRAZA GARCIA Pablo;
VOLLBRACHT Ian;
2019-10-21
Publications Office of the European Union
JRC117362
978-92-76-09231-5 (online),
978-92-76-09232-2 (print),
978-92-76-14146-4,
1831-9424 (online),
1018-5593 (print),
EUR 29825 EN,
OP KJ-NA-29825-EN-N (online),
OP KJ-NA-29825-EN-C (print),
OP KJ-NA-29825-EN-E,
https://publications.jrc.ec.europa.eu/repository/handle/JRC117362,
10.2760/668 (online),
10.2760/40733 (print),
10.2760/022985,
