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AI-driven unemployment risk and household financial decision: Evidence from China

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  • Zhang, Qianyi

Abstract

This paper examines unemployment risk driven by artificial intelligence (AI) on household risky asset investment, focusing on how labour market risk affected by AI influences financial decisions in Chinese household. Using 2016, 2018, and 2020 data from the China Family Panel Studies (CFPS), we analyze the relationship between unemployment risk, qualified by the probability of AI replacing occupations and household decisions in financial risky asset investment, constructing the Probit and Tobit models. After several methods dealing with endogeneity issues, we find a positive effect of unemployment risk on household decisions, especially among highincome households, who are more likely to invest in risky assets to hedge against potential work loss. Furthermore, we also find that material aspiration is essential in moderating these investment behaviours. This study provides the first empirical evidence of unemployment risk as a determinant of household risky asset investment in China, utilizing forward-looking data to measure labour market uncertainty across occupations and complementing the 'participation puzzle' explanation for emerging economies. Additionally, it pioneers micro-level analysis of AI-induced unemployment risk, revealing its influence on household financial decisions and introducing the unemployment risk--material aspiration--risky asset investment framework, which expands the understanding of AI's microeconomic effects.

Suggested Citation

  • Zhang, Qianyi, 2025. "AI-driven unemployment risk and household financial decision: Evidence from China," Journal of Asian Economics, Elsevier, vol. 99(C).
    • Handle: RePEc:eee:asieco:v:99:y:2025:i:c:s1049007825000879
    DOI: 10.1016/j.asieco.2025.101963
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