<p>Amid the rapid development of the digital economy, digital literacy has become a crucial factor associated with labor market outcomes and wage levels, particularly in China, where urban–rural disparities and structural inequalities remain pronounced. Drawing on data from the 2022 China Family Panel Studies (CFPS 2022), this study constructs a multidimensional digital literacy index using the entropy–TOPSIS method, covering five behavioral dimensions: digital tool usage, online social interaction, online learning, entertainment consumption, and e-commerce activities. Stepwise OLS regressions are employed as the main analytical approach to examine how digital literacy is associated with wage income, while a supplementary SEM analysis is conducted to validate the robustness of the observed relationships and to explore potential mediating pathways. The results indicate that digital literacy is positively and significantly associated with wage income after controlling for key covariates, with a stronger association observed among urban workers, highlighting its structural role as a new form of human capital. However, the mediating association of non-farm employment is not statistically significant, suggesting that the linkage between digital skills and employment outcomes may be constrained by skill mismatches, institutional barriers, and unequal access to resources. Overall, this study contributes to a correlational understanding of the relationship between digital literacy and income distribution and provides empirical evidence and policy insights for enhancing digital skills, promoting labor mobility, and narrowing the urban–rural income gap.</p>

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The associational relationships between multidimensional digital literacy and wage income: evidence from urban–rural heterogeneity

  • Jiangwei Hu,
  • Chunyun Tan,
  • Yamei Jiao,
  • Xu Mou,
  • Pengjun Wu

摘要

Amid the rapid development of the digital economy, digital literacy has become a crucial factor associated with labor market outcomes and wage levels, particularly in China, where urban–rural disparities and structural inequalities remain pronounced. Drawing on data from the 2022 China Family Panel Studies (CFPS 2022), this study constructs a multidimensional digital literacy index using the entropy–TOPSIS method, covering five behavioral dimensions: digital tool usage, online social interaction, online learning, entertainment consumption, and e-commerce activities. Stepwise OLS regressions are employed as the main analytical approach to examine how digital literacy is associated with wage income, while a supplementary SEM analysis is conducted to validate the robustness of the observed relationships and to explore potential mediating pathways. The results indicate that digital literacy is positively and significantly associated with wage income after controlling for key covariates, with a stronger association observed among urban workers, highlighting its structural role as a new form of human capital. However, the mediating association of non-farm employment is not statistically significant, suggesting that the linkage between digital skills and employment outcomes may be constrained by skill mismatches, institutional barriers, and unequal access to resources. Overall, this study contributes to a correlational understanding of the relationship between digital literacy and income distribution and provides empirical evidence and policy insights for enhancing digital skills, promoting labor mobility, and narrowing the urban–rural income gap.