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Data-intensive innovation and the state: evidence from AI firms in China

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  • Beraja, Martin
  • Yang, David Y.
  • Yuchtman, Noam

Abstract

Developing artificial intelligence (AI) technology requires data. In many domains, government data far exceed in magnitude and scope data collected by the private sector, and AI firms often gain access to such data when providing services to the state. We argue that such access can stimulate commercial AI innovation in part because data and trained algorithms are shareable across government and commercial uses. We gather comprehensive information on firms and public security procurement contracts in China’s facial recognition AI industry. We quantify the data accessible through contracts by measuring public security agencies’ capacity to collect surveillance video. Using a triple-differences strategy, we find that data-rich contracts, compared to data-scarce ones, lead recipient firms to develop significantly and substantially more commercial AI software. Our analysis suggests a contribution of government data to the rise of China’s facial recognition AI firms, and that states’ data collection and provision policies could shape AI innovation.

Suggested Citation

  • Beraja, Martin & Yang, David Y. & Yuchtman, Noam, 2022. "Data-intensive innovation and the state: evidence from AI firms in China," LSE Research Online Documents on Economics 124859, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:124859
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    6. Mary Merva & Adrian Stoian & Simona Costagli, 2021. "Effective information, political structure and economic growth," Economics of Transition and Institutional Change, John Wiley & Sons, vol. 29(4), pages 597-620, October.
    7. Yan Wang & Ping Han, 2023. "Digital Transformation, Service-Oriented Manufacturing, and Total Factor Productivity: Evidence from A-Share Listed Companies in China," Sustainability, MDPI, vol. 15(13), pages 1-24, June.
    8. Ding, Jeffrey, 2022. "Techno-industrial Policy for New Infrastructure: China’s Approach to Promoting Artificial Intelligence as a General Purpose Technology," Institute on Global Conflict and Cooperation, Working Paper Series qt1sb844ws, Institute on Global Conflict and Cooperation, University of California.
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    11. Matheus Eduardo Leusin, 2022. "The Development of Al in Multinational Enterprises - Effects upon Technological Trajectories and Innovation Performance," Bremen Papers on Economics & Innovation 2201, University of Bremen, Faculty of Business Studies and Economics.
    12. Tiago C. Peixoto & Otaviano Canuto, & Luke Jordan, 2024. "AI and the Future of Government: Unexpected Effects and Critical Challenges," Policy briefs on Economic Trends and Policies 2408, Policy Center for the New South.
    13. David Karpa & Torben Klarl & Michael Rochlitz, 2021. "Artificial Intelligence, Surveillance, and Big Data," Papers 2111.00992, arXiv.org.
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    17. Freeman, Richard B. & Yang, Buyuan & Zhang, Baitao, 2023. "Data deepening and nonbalanced economic growth," Journal of Macroeconomics, Elsevier, vol. 75(C).
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    More about this item

    Keywords

    data; innovation; artificial intelligence; China; innovation policy; privacy; surveillance;
    All these keywords.

    JEL classification:

    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • P00 - Political Economy and Comparative Economic Systems - - General - - - General
    • E00 - Macroeconomics and Monetary Economics - - General - - - General
    • L50 - Industrial Organization - - Regulation and Industrial Policy - - - General
    • L63 - Industrial Organization - - Industry Studies: Manufacturing - - - Microelectronics; Computers; Communications Equipment
    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General

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