Dynamic Econometrics Analysis of Influential Factors for the Fluctuation of Farmers' Cash Income in Sichuan: Based on VEC Model
Bibliographic record
Abstract
【Objective】 To develop the dynamic econometrics analysis of influential factors for the fluctuation of farmers' cash income in Sichuan.【Method】 Choosing the sample of quarter data of the farmer cash income,the household management income,the wage income and the transfer income in Sichuan province from the first quarter of 2003 to the third quarter of 2010,the paper analyzed the influence of household management income,the wage income and the transfer income for the fluctuation of the farmer cash income,which uses the Johansen cointegration test and the VEC model(Vector Error Correction model).【Results】 In short run,the household management income,the wage income and the transfer income of the preceding term have a positive effect on the cash income of current term,among which the household management income has the most positive impression.In the long run,the wage income and the transfer income have positive influence,while the household management income has negative impact.【Conclusion】 This study is useful for the government of Sichuan province to guide the agricultural production and increase the cash income of farmers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".