Does the rural digital economy promote shared prosperity among farmers? Evidence from China
Bibliographic record
Abstract
Introduction Ensuring shared prosperity among rural populations remains a central challenge in achieving inclusive and sustainable development. New opportunities for rural development have been created by the growth of the digital economy, yet empirical evidence on its capacity to promote shared prosperity among farmers remains limited. This study examines the connection between the growth of the rural digital economy and shared prosperity, highlighting the function of high-quality agricultural development as a moderating factor. Methods This study uses a fixed effects regression approach to evaluate how the development of the rural digital economy affects farmers' shared prosperity using balanced provincial panel data from 2013 to 2022. To investigate the influence of high-quality agricultural development, the mediation model is constructed. Multiple robustness tests, including lagged variables, alternative indicators, and sub-sample analyses, are carried out to guarantee the validity of the findings. Results The results of the study indicate that the development of the digital economy in rural areas significantly promotes farmers' shared prosperity. The mediating role of high-quality agricultural development is confirmed, highlighting its importance in channeling digital economic benefits. Regional heterogeneity is observed, with stronger effects found in western provinces compared to eastern ones. Furthermore, the impact follows a U-shaped trajectory, indicating that as digital infrastructure matures, its capacity to promote shared rural prosperity increases. Discussion The findings suggest that advancing rural digital infrastructure and services, alongside improvements in agricultural quality, is essential for fostering equitable development outcomes. The evidence underscores the need for context-specific strategies, particularly in underdeveloped regions where digital integration can yield the greatest marginal benefits. This study adds to the expanding discussion about digital inclusion and rural revitalization in the global effort toward sustainable and inclusive food systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".