Data-intensive Innovation and the State: Evidence from AI Firms in China / Martin Beraja, David Y. Yang, Noam Yuchtman.

Format:

Book

Author/Creator:

Beraja, Martin.

Contributor:

National Bureau of Economic Research.

Yang, David Y.

Yuchtman, Noam.

Series:

Working Paper Series (National Bureau of Economic Research) no. w27723.

NBER working paper series no. w27723

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2020.

Summary:

Developing AI technology requires data. In many domains, government data far exceeds 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 indicates a contribution of government data to the rise of China's facial recognition AI firms, and suggests that states' data collection and provision policies could shape AI innovation.

Notes:

Print version record

August 2020.