IDEAS home Printed from https://ideas.repec.org/h/nbr/nberch/15415.html

The Missing Value of Data

In: NBER Macroeconomics Annual 2026, volume 41

Author

Listed:
  • Ankit Bhutani
  • Guillermo Ordoñez
  • Laura Veldkamp

Abstract

Data assets are increasingly vital in modern economies, yet macroeconomic measurement is not well-adapted to capturing their value. Part of the problem is that data is an intangible asset: investments in data are missed in national accounts, and depreciation losses are missed in firms’ balance sheets. Another part, unique to data, is that it serves as a means of payment in the modern economy: consumption bartered for data is also omitted from national accounts. We propose an output-based approach to measure the missing value of data. We treat data as an asset, measure its volume based on the quality of firms’ revenue forecasts, and endogenously determine its depreciation. We then capitalize the data value and explore what the measured GDP would be if the data were treated and transacted similarly to a physical asset. Our findings suggest that the aggregate value of data is about 1.5% of GDP.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Ankit Bhutani & Guillermo Ordoñez & Laura Veldkamp, 2026. "The Missing Value of Data," NBER Chapters, in: NBER Macroeconomics Annual 2026, volume 41, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberch:15415
    Note: CF EFG PR
    as

    Download full text from publisher

    File URL: http://www.nber.org/chapters/c15415.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Maryam Farboodi & Laura Veldkamp, 2021. "A Model of the Data Economy," NBER Working Papers 28427, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ehsan Valavi & Joel Hestness & Newsha Ardalani & Marco Iansiti, 2022. "Time and the Value of Data," Papers 2203.09118, arXiv.org.
    2. Bianchi, Milo & Bouvard, Matthieu & Gomes, Renato & Rhodes, Andrew & Shreeti, Vatsala, 2023. "Mobile payments and interoperability: Insights from the academic literature," Information Economics and Policy, Elsevier, vol. 65(C).
    3. Kaiwen Hou, 2023. "Adaptive Bayesian Learning with Action and State-Dependent Signal Variance," Papers 2311.12878, arXiv.org, revised Nov 2023.
    4. Chu, Zhaopeng & Chen, Xin & Yang, Jun, 2025. "Impact of data factor and data integration on economic development: Empirical insights from China," Telecommunications Policy, Elsevier, vol. 49(8).
    5. Toni Ahner & Katrin Assenmacher & Peter Hoffmann & Agnese Leonello & Cyril Monnet & Davide Porcellacchia, 2024. "The Economics of Central Bank Digital Currency," International Journal of Central Banking, International Journal of Central Banking, vol. 20(4), pages 221-274, October.
    6. Romeo Victor Ionescu & Monica Laura Zlati & Valentin Marian Antohi & Florina Oana Virlanuta & Silvius Stanciu, 2022. "Quantifying the Digitalisation Impact on the EU Economy. Case Study: Germany and Sweden vs. Romania and Greece," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 24(59), pages 1-61.
    7. Liang Chen, 2026. "Ecosystem Competition and Cross-Market Subsidization: A Dynamic Theory of Platform Pricing," Papers 2601.15303, arXiv.org.
    8. Gillian K. Hadfield & Andrew Koh, 2025. "An Economy of AI Agents," NBER Chapters, in: The Economics of Transformative AI, National Bureau of Economic Research, Inc.
    9. Agrawal, Ajay & McHale, John & Oettl, Alexander, 2024. "Artificial intelligence and scientific discovery: a model of prioritized search," Research Policy, Elsevier, vol. 53(5).
    10. Qian, Yifan, 2025. "Public data openness and corporate total factor productivity," Economic Analysis and Policy, Elsevier, vol. 85(C), pages 733-753.
    11. Michael Choi & Guillaume Rocheteau, 2024. "Information acquisition and price discrimination in dynamic, decentralized markets," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 53, pages 1-46, July.
    12. Zhong, Yuan & Lai, Huisu & Zhang, Liang & Guo, Lixiang & Lai, Xiaobing, 2025. "Does public data openness accelerate new quality productive forces? Evidence from China," Economic Analysis and Policy, Elsevier, vol. 85(C), pages 1409-1427.
    13. 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).
    14. Alistair Macaulay, 2026. "The Causal Effects of Heterogeneous Expectation Formation in General Equilibrium," School of Economics Discussion Papers 0326, School of Economics, University of Surrey.
    15. Koski, Heli & Fornaro, Paolo, 2024. "Digitalization and Resilience: Data Assets and Firm Productivity Growth During the COVID-19 Pandemic," ETLA Working Papers 113, The Research Institute of the Finnish Economy.
    16. 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).
    17. Liu, Ying & Huang, Hongyun & Mbanyele, William & Li, Xin & Balezentis, Tomas, 2025. "Harnessing supply chain digital innovation for enhanced corporate environmental practices and sustainable growth," Energy Economics, Elsevier, vol. 142(C).
    18. Bessen, James & Impink, Stephen Michael & Reichensperger, Lydia & Seamans, Robert, 2022. "The role of data for AI startup growth," Research Policy, Elsevier, vol. 51(5).
    19. Allen, Franklin & Barbalau, Adelina, 2024. "Security design: A review," Journal of Financial Intermediation, Elsevier, vol. 60(C).
    20. Wang, Zhen & Tang, Pei, 2024. "Substantive digital innovation or symbolic digital innovation: Which type of digital innovation is more conducive to corporate ESG performance?," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 1212-1228.

    More about this item

    JEL classification:

    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberch:15415. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.