Agricultural production in many developing countries, including China, relies on extensive methods with high inputs and low technical efficiency. This limits agricultural development, food security, and environmental sustainability. Excessive fertilizer and pesticide use leads to significant pollution. Transitioning to efficient agriculture requires both institutional innovation (e.g., secure land ownership, effective credit policies) and the adoption of new technologies (e.g., agricultural machinery, climate adaptation technologies). However, farmers' production decisions are central; their behavior is influenced by factors such as preferences, knowledge, resource constraints, and cost-benefit analyses. Farmers may not immediately adjust to institutional changes or adopt new technologies due to inertia, lack of information, insufficient skills, limited financial resources, and information asymmetry. The internet offers significant potential for overcoming these challenges through readily available information, rapid dissemination, and overcoming geographical barriers. While existing research has focused on the internet's impact on agricultural product market efficiency, research on its effect on the technical efficiency of grain production, especially for smallholders, remains limited. This study addresses this gap by investigating the impact of internet use on the technical efficiency of grain production among smallholders in China, considering selection bias and examining the underlying mechanisms.

Existing literature highlights various factors influencing agricultural technical efficiency. Institutional innovations such as secure land ownership and effective credit policies are shown to be beneficial. Similarly, the adoption of new production technologies like agricultural machinery services and green technologies improves efficiency. However, these studies often neglect the farmer's central role in production decisions. Research on the internet's impact on agriculture primarily focuses on enhancing market efficiency of agricultural products. Some studies examine the link between mobile phone communication and agricultural efficiency but often fail to address sample selection bias. Other research explored the impact of internet use on the technical efficiency of specific cash crops (apples and bananas) in China, demonstrating positive effects, but this may not generalize to grain crops due to differing technical requirements and market dynamics. The present study seeks to fill this research gap by exploring the impact of internet use specifically on grain crop production, addressing selection bias, and identifying the underlying mechanisms.

This study utilizes a multi-stage procedure to address selection biases and obtain an unbiased estimate of the impact of internet use on technical efficiency. First, a sample selection-corrected stochastic production frontier (SPF) model, proposed by Greene (2010), is employed to estimate technical efficiency while accounting for unobserved factors. This model uses a translog production function with output (total output value of grain crops), labor input (number of laborers), capital input (investment in cultivation, fertilizer, pesticides, etc.), and land input (planting area) as variables. The technical efficiency (TE) is calculated as the ratio of actual output to potential output on the production frontier. Second, propensity score matching (PSM) is used to address the self-selection bias associated with internet use. A logit model estimates the probability of internet use based on observed characteristics (individual characteristics of the household head, household characteristics, and village characteristics). Matching is performed to create a counterfactual scenario, comparing the technical efficiency of internet users with a matched control group. Third, an endogenous switching regression (ESR) model is used to verify the robustness of the findings, accounting for both observed and unobserved factors. The proportion of internet users in the same village serves as an instrumental variable to address endogeneity in the ESR model. The data for this study comes from the 2018 China Labor Dynamics Survey (CLDS), comprising 1699 households involved in grain production across 25 provinces. Various statistical tests (likelihood ratio test, balance test, common support domain test, sensitivity analysis using the boundary method) are conducted to validate the models and ensure the reliability of the results. The study also empirically tests the three proposed mechanisms through which internet use affects technical efficiency: alleviation of financial constraints (measured by loan access), strengthening of social capital (measured by household gift giving), and promotion of mechanized farming (measured by machinery use).

The logit regression analysis reveals that several factors influence households' decisions to use the internet. The age of the household head has a negative impact, while the education level, health status, household income, household size, and plain village topography all have significant positive effects. The bias-corrected SPF model shows that internet use has a significant positive effect on technical efficiency, with selection bias leading to an underestimation of this effect. The average technical efficiency of internet-using households is higher than that of non-users. The PSM results, using nearest neighbor, radius, and kernel matching methods, consistently show a significant positive effect of internet use on technical efficiency, regardless of whether conventional or bias-corrected SPF models are used. The average treatment effect (ATT) on technical efficiency from using the internet is estimated to be between 0.008 and 0.012, depending on the matching method. The sensitivity analysis confirms the robustness of the PSM estimates, indicating that the impact of unobserved factors is negligible. The ESR model, addressing both observed and unobserved factors, further confirms the positive effect of internet use on technical efficiency. Finally, the mechanism analysis demonstrates that internet use significantly impacts technical efficiency through the three proposed channels: (1) increasing the probability of borrowing, (2) increasing household gift giving, and (3) increasing the likelihood of using machinery in production. This suggests that improved access to financial resources, expanded social networks, and enhanced access to mechanization services are key factors mediating the positive effect of internet use on technical efficiency.

The findings of this study strongly support the hypothesis that internet use significantly enhances the technical efficiency of grain production among smallholders in China. The positive effect is robust to various econometric techniques, including bias correction for sample selection and the use of instrumental variables to address endogeneity. The results highlight the importance of considering selection bias when studying the impact of information and communication technologies on agricultural productivity. The identification of three key mechanisms—alleviation of financial constraints, enhancement of social capital, and increased mechanization—provides valuable insights into how the internet contributes to improved efficiency. These findings have significant implications for policy interventions aimed at promoting agricultural modernization and enhancing food security in developing countries. The consistent positive effect across different econometric models strengthens the generalizability of the findings, suggesting that the positive impact of internet access on agricultural efficiency is not merely a statistical artifact but a real effect.

This study demonstrates a significant positive impact of internet use on the technical efficiency of grain production among Chinese smallholders. Selection bias needs to be considered for accurate estimation. The internet's effect is channeled through improved financial access, stronger social capital, and enhanced mechanization. Policy recommendations include investment in rural internet infrastructure, technical training programs, and collaborative policy measures that provide financial, technological, and production support to farmers.

The study uses cross-sectional data, limiting the analysis of dynamic effects. Measurement of internet usage is limited to basic access, not the depth of its use for production decisions. Future research using panel data and more nuanced measures of internet usage would strengthen the findings. The generalizability of findings might be affected by the specific context of the Chinese agricultural sector.