Nonrivalry and the Economics of Data
By Charles I. Jones, Christopher Tonetti. 2019
733 citations recorded by the source corpus.
Data is nonrival: a person’s location history, medical records, and driving data can be used by many firms simultaneously. Nonrivalry leads to increasing returns. As a result, there may be social gains to data being used broadly across firms, even in the presence of privacy considerations. Fearing creative destruction, firms may choose to hoard their data, leading to the inefficient use of nonrival data. Giving data property rights to consumers can generate allocations that are close to optimal. Consumers balance their concerns for privacy against the economic gains that come from selling data broadly. (JEL C80, D11, D21, D83, E22, K11, O34)
Subfields: Management Science and Operations Research, Marketing, Strategy and Management
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In Management Science and Operations Research
Citations in the corpus
- SplitNN-driven Vertical Partitioning (2020, background): “…our proposed split learning scheme, the need to pool all of the data together is obviated, keeping the data sources private, which enables unprecedented collaboration between the sharing parties and the non-rivalry of data (Charles & Christopher, 2018) would potentially lead to increasing returns.”
- Time and the Value of Data (2020, neutral): “…by several authors [e.g., Aghion et al. (2019),Agrawal et al. (2019),Bald-win (2019),Brynjolfsson et al. (2018),Cockburn et al. (2019),Cowgill and Tucker (2020),Jones and Tonetti (2020),Korinek and Stiglitz (2019),Milgrom and Tadelis (2019),Reimers and Shiller (2018),Tirole (2020),Varian (2019)].”