Journal article
Data-intensive innovation and the state: evidence from AI firms in China
- 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
-
-
(Preview, Accepted manuscript, pdf, 3.3MB, Terms of use)
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- Publisher copy:
- 10.1093/restud/rdac056
Authors
- Publisher:
- Oxford University Press
- Journal:
- Review of Economic Studies More from this journal
- Volume:
- 90
- Issue:
- 4
- Pages:
- 1701–1723
- Publication date:
- 2022-08-13
- Acceptance date:
- 2022-08-01
- DOI:
- EISSN:
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1467-937X
- ISSN:
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0034-6527
- Language:
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English
- Keywords:
- Pubs id:
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1557073
- Local pid:
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pubs:1557073
- Deposit date:
-
2023-11-02
Terms of use
- Copyright holder:
- Beraja et al.
- Copyright date:
- 2022
- Rights statement:
- © The Author(s) 2022. Published by Oxford University Press on behalf of The Review of Economic Studies Limited. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model.
- Notes:
- This is the accepted manuscript version of the article. The final version is available online from Oxford University Press at https://dx.doi.org/10.1093/restud/rdac056
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