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Cited by 211

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Data-intensive Innovation and the State: Evidence from AI Firms in China

Open access
Beraja, Martin, Yang, David Y, Yuchtman, Noam·2023·The Review of Economic Studies

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.

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Metadata

Authors
Beraja, M., Yang, D. Y., & Yuchtman, N.
Venue
The Review of Economic Studies
Published
2022-08-13
Volume
90
Issue
4
Pages
1701-1723
ISSN
  • 0034-6527 (ISSN-L)
  • 1467-937X
OpenAlex
W3126140529
Cited by
211
References
4
FWCI
27.06
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Data-intensive Innovation and the State: Evidence from AI Firms in China. https://app.cochat.ai/papers/10.1093/restud/rdac056
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Beraja, M., Yang, D. Y., & Yuchtman, N. (2023). Data-intensive Innovation and the State: Evidence from AI Firms in China. The Review of Economic Studies. https://doi.org/10.1093/restud/rdac056

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

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