The role of digital financial inclusion in China on urban—rural disparities in healthcare expenditures
Bibliographic record
Abstract
Introduction: The digital financial inclusion (DFI) provides opportunities to improve the relative capacity to pay for healthcare services by rural residents who are usually underserved by traditional finance in China. This paper provides empirical evidence on how the development of DFI affects the healthcare expenditure disparities between urban and rural residents. Methods: We employed the fixed effects model and instrumental variable method to estimate the impact of DFI on the Theil index of urban-rural disparities in healthcare expenditures, using panel data from 31 provinces (2011 ~ 2020) in China. We further adopted a moderating effect model to test whether the intensity of the impact would vary depending on the level of local government health expenditures. Results: The results suggest a negative association between the development level of DFI and the urban-rural healthcare expenditure disparities in China. For every 1% increase in the DFI index, the Theil index of urban-rural disparities in healthcare expenditures would fall by 0.0013. After changing the measurement method for the dependent variable and adjusting the sample, the results remain robust. Moreover, the result of the moderating effect model indicates that, a high level of government health expenditures is conducive to the impact of DFI. Discussion: Our research reveals that DFI plays an important role in bridging the urban-rural gap in healthcare expenditures. This finding provides new information for addressing the issue of urban-rural healthcare inequality in China. Chinese government needs to accelerate the construction of digital infrastructure and increase the penetration rate of digital tools in rural areas to promote the beneficial effects of DFI. Additionally, it is also necessary for local government to address the unbalanced allocation of medical resources between urban and rural areas, especially the shortage of rural human resources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".