Digital empowerment and the development resilience in rural households: causal inference based on double machine learning

Lin Li, Kecheng Wei, Jiliang Han () and Yuchun Zhu ()
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Lin Li: Northwest A&F University
Kecheng Wei: Shandong Agricultural University
Jiliang Han: Northwest A&F University
Yuchun Zhu: Northwest A&F University

Empirical Economics, 2025, vol. 69, issue 3, No 4, 1187-1227

Abstract: Abstract The digital economy has gradually become an essential engine for rural modernization, with significant implications for enhancing rural household development resilience and consolidating the achievements in poverty alleviation (Wang et al. in Energy 282:128692, 2023). This paper analyzes the impact mechanism and heterogeneity of digital empowerment on rural household development resilience drawing on the empowerment theory (Perkins and Zimmerman in Am J Commun Psychol 23:569–579, 1995). It utilizes a double machine learning estimation approach (Chernozhukov et al. in Economet J 21(1):C1–C68, 2018) by relying on data from 5174 rural households collected in the Yellow River Basin in 2020 and 2022 in China. The findings reveal that digital empowerment contributes to improving rural household development resilience, and the results remain robust after replacing key variables, re-processing samples, and resetting the double machine learning model. The mechanism analysis indicates that digital empowerment can enhance social capital accumulation, broaden information channels, and promote equal access to public services, thereby enhancing rural household development resilience. Heterogeneity analysis found that digital empowerment is particularly beneficial for enhancing the development resilience of low-income rural households headed by individuals with low education levels and supported by relevant policies. This research provides policy implications for bridging the “digital divide,” shaping “household resilience,” and advancing common prosperity.

Keywords: Digital empowerment; Developing resilience; Rural households; Double machine learning model (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00181-025-02767-4

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