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Knowledge Accumulation, Privacy, and Growth in a Data Economy

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  • Lin William Cong

    (SC Johnson College of Business, Cornell University, Ithaca, New York 14853)

  • Danxia Xie

    (Institute of Economics, Tsinghua University, Beijing 100084, China)

  • Longtian Zhang

    (Institute of Economics, Tsinghua University, Beijing 100084, China)

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.

Suggested Citation

  • Lin William Cong & Danxia Xie & Longtian Zhang, 2021. "Knowledge Accumulation, Privacy, and Growth in a Data Economy," Management Science, INFORMS, vol. 67(10), pages 6480-6492, October.
  • Handle: RePEc:inm:ormnsc:v:67:y:2021:i:10:p:6480-6492
    DOI: 10.1287/mnsc.2021.3986
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    3. Cong, Lin William & Wei, Wenshi & Xie, Danxia & Zhang, Longtian, 2022. "Endogenous growth under multiple uses of data," Journal of Economic Dynamics and Control, Elsevier, vol. 141(C).
    4. Zhuo, Chengfeng & Chen, Jin, 2023. "Can digital transformation overcome the enterprise innovation dilemma: Effect, mechanism and effective boundary," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
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    6. Zhao, Haoran & Guo, Sen, 2023. "Analysis of the non-linear impact of digital economy development on energy intensity: Empirical research based on the PSTR model," Energy, Elsevier, vol. 282(C).
    7. Liu, Rui & Zheng, Linhao & Chen, Zheang & Cheng, Mengyao & Ren, Yuzhuo, 2024. "Digitalization through supply chains: Evidence from the customer concentration of Chinese listed companies," Economic Modelling, Elsevier, vol. 134(C).
    8. Francesco Angelini & Luca V. Ballestra & Massimiliano Castellani, 2022. "Digital leisure and the gig economy: a two-sector model of growth," Papers 2212.02119, arXiv.org.
    9. Ma, Dan & Zhu, Qing, 2022. "Innovation in emerging economies: Research on the digital economy driving high-quality green development," Journal of Business Research, Elsevier, vol. 145(C), pages 801-813.
    10. Li, Quan & Chen, Huimin & Chen, Yang & Xiao, Tong & Wang, Li, 2023. "Digital economy, financing constraints, and corporate innovation," Pacific-Basin Finance Journal, Elsevier, vol. 80(C).
    11. Allen, Franklin & Gu, Xian & Jagtiani, Julapa, 2022. "Fintech, Cryptocurrencies, and CBDC: Financial Structural Transformation in China," Journal of International Money and Finance, Elsevier, vol. 124(C).
    12. Itay P. Fainmesser & Andrea Galeotti & Ruslan Momot, 2023. "Digital Privacy," Management Science, INFORMS, vol. 69(6), pages 3157-3173, June.
    13. Olivier Armantier & Sebastian Doerr & Jon Frost & Andreas Fuster & Kelly Shue, 2024. "Nothing to hide? Gender and age differences in the willingness to share data," BIS Working Papers 1187, Bank for International Settlements.
    14. Ichihashi, Shota, 2022. "Comments on Cong, Wei, Xie, and Zhang (2021) “Endogenous Growth with Multiple Uses of Data”," Journal of Economic Dynamics and Control, Elsevier, vol. 141(C).
    15. Yangjun Ren & Xin Zhang & Hui Chen, 2022. "The Impact of New Energy Enterprises’ Digital Transformation on Their Total Factor Productivity: Empirical Evidence from China," Sustainability, MDPI, vol. 14(21), pages 1-17, October.
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    18. Li, Hao & Wang, Gaowang & Yang, Liyang, 2024. "Data-driven innovation and growth," MPRA Paper 122388, University Library of Munich, Germany.

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