The Boundary of Open Data: Implications for the Financial Market and Real Efficiency

We analyze the optimal boundary for open data in an economy where financial and real-sector participants access both open and private data. The distinctive features of open access and nonrivalrous usage of open data enable its dual roles as a public information source and innovation input but raise privacy concerns. Our model reveals a novel tradeoff: Although enhanced private data precision and data skills substitute for open data’s information source role, its ability to amplify innovation benefits (via improved investment efficiency) establishes a crucial complementary relationship. This induces a crowding-in effect on the optimal open data boundary under low uncertainty but a crowding-out effect under high uncertainty. The innovation role of open data further generates nonmonotonic effects, yielding complex nonlinear impacts on market and real efficiency. These findings highlight critical policy tradeoffs in balancing innovation, market efficiency, and privacy in the digital age.

This paper was accepted by Bo Becker, finance.

Funding: Z. Wang acknowledges financing from the National Natural Science Foundation of China [Grants 72442025 and 72272028] and the Graduate Education Reform of Dongbei University of Finance and Economics [Grant yjzd202309]. Z. Qiu acknowledges financing from the Major Program of the National Natural Science Foundation of China [Grant 72192804] and the National Key Research and Development Program of China [Grant 2023YFC3304701].

Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2023.03679.