A new GLO Discussion Paper presents a theoretical conceptualization of the data economy: Knowledge extraction from large, inter-connected data sets displays natural monopoly characteristics that generate and disclose the amount of knowledge that maximizes their profit. Provided that monopoly theory holds, this level of knowledge is below the socially desirable amount.
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GLO Discussion Paper No. 515, 2020
The Semicircular Flow of the Data Economy and the Data Sharing Laffer curve – Download PDF
by de Pedraza, Pablo & Vollbracht, Ian
GLO Fellow Pablo de Pedraza
Featured image: Photo by Mika Baumeister on Unsplash
Author Abstract: This paper presents a theoretical conceptualization of the data economy that motivates more access to data for scientific research. It defines the semicircular flow of the data economy as analogous to the traditional circular flow of the economy. Knowledge extraction from large, inter-connected data sets displays natural monopoly characteristics, which favors the emergence of oligopolistic data holders that generate and disclose the amount of knowledge that maximizes their profit. If monopoly theory holds, this level of knowledge is below the socially desirable amount because data holders have incentives to maintain their market power. The analogy is further developed to include data leakages, data sharing policies, merit and demerit knowledge, and knowledge injections. It draws a data sharing Laffer curve that defines optimal data sharing as the point where the production of merit knowledge is maximized. The theoretical framework seems to describe many features of the data-intensive economy of today, in which large-scale data holders specialize in extraction of knowledge from the data they hold. Conclusions support the use of policies to enhance data sharing and, or, enhanced user-centric data property rights to facilitate data flows in a manner that would increase merit knowledge generation up to the socially desirable amount.
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