TY  - JOUR
AU  - Beraja, Martin
AU  - Yang, David Y
AU  - Yuchtman, Noam
TI  - Data-intensive Innovation and the State: Evidence from AI Firms in China
JF  - National Bureau of Economic Research Working Paper Series
VL  - No. 27723
PY  - 2020
Y2  - August 2020
DO  - 10.3386/w27723
UR  - http://www.nber.org/papers/w27723
L1  - http://www.nber.org/papers/w27723.pdf
N1  - Author contact info:
Martin Beraja
Department of Economics, E52-504
MIT
77 Massachusetts Avenue
Cambridge, MA 02139
Tel: 617/258-6022
E-Mail: martinberaja@gmail.com

David Y. Yang
Department of Economics
Harvard University
Littauer Center M-31
Cambridge, MA 02138
E-Mail: davidyang@fas.harvard.edu

Noam Yuchtman
London School of Economics
Houghton St.
London WC2A 2AE
United Kingdom
Tel: 44 (0)20 7955 1262
E-Mail: n.yuchtman@lse.ac.uk
M3  - presented at "SI 2020 Income Distribution and Macroeconomics ", July 15-16, 2020
AB  - Artificial intelligence (AI) innovation is data-intensive. States have historically collected large amounts of data, which is now being used by AI firms. Gathering comprehensive information on firms and government procurement contracts in China’s facial recognition AI industry, we first study how government data shapes AI innovation. We find evidence of a precise mechanism: because data is sharable across uses, economies of scope arise. Firms awarded public security AI contracts providing access to more government data produce more software for both government and commercial purposes. In a directed technical change model incorporating this mechanism, we then study the trade-offs presented by states’ AI procurement and data provision policies. Surveillance states’ demand for AI may incidentally promote growth, but distort innovation, crowd-out resources, and infringe on civil liberties. Government data provision may be justified when economies of scope are strong and citizens’ privacy concerns are limited.
ER  -