With the rapid evolution of the rural digital economy, analyzing the impact of digital literacy level on farm households’ formal borrowing is crucial for easing credit constraints and fostering rural economic growth. Leveraging the data from the 2020 China Family Panel Studies (CFPSs) and applying binary probit models and the Karlson–Holm–Breen (KHB) method, this study delineates the positive correlation between the digital literacy level and increased formal borrowing among farm households. The findings, which were robust against endogeneity and robustness tests, underscore the role of digital literacy level in augmenting farmers’ earnings and social networks, with a notably stronger mediation by earnings. The effects are particularly significant for middle-aged and older, less educated males in the central and western regions, in contrast with younger, highly educated females in the east. This research advocates for enhancing rural digital infrastructure and education, alongside financial system reforms, to advance rural financial development and support sustainable rural revitalization.

Using farm machinery plays a significant role in easing the issue of slowing growth of operating income among farm households in China. Drawing data from CFPS2018, this study adopts a multinomial endogenous switching regression (MESR) to analyze the factors influencing farm households’ choices regarding self-owned farm machinery and outsourced machinery services, as well as their subsequent impact on operating income. The results of the study show that the characteristics of the head of household, family, village, and region have a significant impact on the farm households’ selection of whether to use self-owned machinery or outsourced services. Furthermore, the exclusive use of self-owned farm machinery and the combined use of both self-owned and outsourced machinery substantially enhance farm households’ operating income. An additional analysis indicates that these two types of machinery are complementary, and their combined use generates a superimposed effect that further boosts income. These findings suggest that the combined use of self-owned and outsourced machinery is optimal for farm households who wish to expand their operating income.