Digital Technology Adoption and the Optimal Allocation of Rural Household Labor, Land, and Capital: Evidence from the Yellow River Basin

Digital technologies have rapidly penetrated rural China, transforming household economic structures and reshaping agricultural factor markets. Drawing on 3930 household-level observations from six provinces in the Yellow River Basin (2020–2023), this research aims to examine how digital technology adoption influences the optimal allocation of household labor, land, and capital. To address self-selection and endogeneity, we employed an Endogenous Switching Probit (ESP) model and conducted counterfactual analysis, supplemented by propensity score matching (PSM), instrumental variable probit (IV-Probit), replacement of the core explanatory variable, and exclusion of special samples as four robustness checks. The Average Treatment Effects on the Treated show that digital technology adopters would have reduced the probabilities of non-farm employment, farmland transfer-out, and productive loan access by 24.5, 19.3, and 16.7 percentage points, if they had not adopted digital technology. Similarly, digital technology non-adopters would have improved 27.4, 22.2, and 18.9 percentage points if they adopted digital tools. These impacts are stronger over time, in the upper reaches of the Yellow River Basin, among households with larger landholdings, and among younger farmers. Mechanism analysis further indicates that digital technologies expand information access, strengthen social networks, and ease credit constraints, thereby jointly promoting more efficient labor, land, and capital allocation. The policy implications of these findings are as follows: the importance of improving rural digital infrastructure, tailoring regional policies, and enhancing farmers’ digital skills to narrow the digital divide and support inclusive rural revitalization.