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Cited by 267

The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.

Knowledge Accumulation, Privacy, and Growth in a Data Economy

Open access
Cong, Lin William, Xie, Danxia, Zhang, Longtian·2021·Management Science

Abstract

We build an endogenous growth model with consumer-generated data as a new key factor for knowledge accumulation. Consumers balance between providing data for profit and potential privacy infringement. Intermediate good producers use data to innovate and contribute to the final good production, which fuels economic growth. Data are dynamically nonrival with flexible ownership while their production is endogenous and policy-dependent. Although a decentralized economy can grow at the same rate (but are at different levels) as the social optimum on the Balanced Growth Path, the R&D sector underemploys labor and overuses data—an inefficiency mitigated by subsidizing innovators instead of direct data regulation. As a data economy emerges and matures, consumers’ data provision endogenously declines after a transitional acceleration, allaying long-run privacy concerns but portending initial growth traps that call for interventions. This paper was accepted by Kay Giesecke, finance.

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Metadata

Authors
Cong, L. W., Xie, D., & Zhang, L.
Venue
Management Science
Published
2021-10
Volume
67
Issue
10
Pages
6480-6492
ISSN
  • 0025-1909 (ISSN-L)
  • 1526-5501
OpenAlex
W3193815270
Cited by
267
References
27
FWCI
19.76
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Knowledge Accumulation, Privacy, and Growth in a Data Economy. https://app.cochat.ai/papers/10.1287/mnsc.2021.3986
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Cong, L. W., Xie, D., & Zhang, L. (2021). Knowledge Accumulation, Privacy, and Growth in a Data Economy. Management Science. https://doi.org/10.1287/mnsc.2021.3986

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.