How Effective is Farmer Early Retirement Policy
Bibliographic record
Abstract
Financial support for EU farmers seeking early retirement is a discretionary element of CAP rural development policy and some EU member states, most notably France, Ireland and Greece have chosen to implement the measure. We explore whether the introduction of such schemes is likely to represent good value for money. We use data from Northern Ireland, a region with a relatively small-scale family-farm structure, where there have been periodic calls from farmer groups to introduce support for early retirement. We estimate the benefits that might arise from the introduction of such a scheme using FADN data and a separate survey of 350 farmers aged 50 to 65. We find that farm scale is a significant determinant of profit per hectare but that operator age is not. Benefits from releasing land through an early retirement scheme are conditional on such transfers bringing about significant farm expansion and changes in land use. Even when these conditions are satisfied, however, pensions payments of only about one-third the statutory maximum could be justified in a best-case scenario. Almost a quarter of all payments would incur deadweight losses, i.e., go to farmers who would be retiring anyway. Overall, the economic case for such a scheme is considered to be weak. Copyright (c) 2008 The Authors. Journal compilation (c) The Agricultural Ecomomics Society and the European Association of Agricultural Economists 2008.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".