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Competition and Collaboration in the AI Race: Country-LevelDirectional Evidence for Risk Monitoring and Policy
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DOI：10.1111/risa.70287.png)
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Abstract
En 中文
Artificial intelligence (AI) is reshaping national economies, yet country-level AI–macro relationships remain poorly understood. Using annual data for the United States and China, 1980–2020, we develop a four-layer triangulation framework—Pattern Causality, Granger causality, VECM-based cointegration, and lead–lag correlation—to map directional associations between AI activity indicators and macro aggregates through weighted networks and heatmaps. Three patterns recur. First, nonlinear AI–macro dependence is moderate and mostly positive, making AI indicators useful monitoring signals rather than stand-alone decision triggers. Second, publications and patents carry short-run predictive content in Granger tests, making them candidate early-warning indicators for macro surveillance. Third, cointegration places AI indicators mainly on the adjustment margin: Macro fundamentals condition long-run AI–macro co-movement more than AI indicators lead it. The international layer adds an important qualification. United States–China collaboration variables raise AI node centrality, especially for China, but a mechanical-expansion null benchmark shows that most of this increase is expected from enlarging the network; beyond-mechanical collaboration evidence concentrates in the cointegration layer. Overall, the US pathway is patent-oriented and selective, whereas China's is denser and more collaboration-intensive. The framework supports AI–macro risk monitoring and hypothesis generation, not structurally identified causal claims.
Keywords:
AI–macro monitoring
artificial intelligence
directional associations
risk-informed policy
time-series analysis
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