The role of subsidies in stabilising farm income: Evidence from Estonia
Bibliographic record
Abstract
This study examines the role of agricultural subsidies in the farm income (FI) variation in Estonia in the period 2006–2019 using data from a balanced panel of Farm Accountancy Data Network (FADN) farms. FI variability is decomposed to three components: market income, total subsidies, and the cost of external factors. The results reveal that subsidies have a larger share in income of smaller farms. Variability of FI differs in farm size quartiles and is significantly lower in case of largest 25% of farms. Revenues are the largest source of variation of FI. While subsidies reduce FI variation, there is no evidence that they are counter-cyclical. We argue that in addition to subsidies, smaller farms need advice on farm management to increase market income, improve its stability, and expand farm size, as larger farms have more stable incomes despite relatively lower producer support estimate (PSE) levels.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".