Retirement Age Farmers’ Exit and Disinvestment from Farming
Bibliographic record
Abstract
The aging of farmers in the US today coincides with fluctuating incomes resulting from recent market price volatility and policy changes. We evaluate how farmers’ retirement or exit, as well as their disinvestment from farming in preparation for retirement, are affected by economic and demographic factors. Exit and disinvestment are modeled as the outcome of intertemporal utility maximization, and farm-level data from the Census of Agriculture are used to estimate the probability of retirement-age farmers’ exit and disinvestment for the 1992-2012 period. The results show that farm size matters the most, with larger farms less likely to exit but more likely to disinvest and scale back, presumably to a new optimal size. Demographic factors such as gender, race, and age have statistically significant but relatively small impacts. Regional differences, the size of the non-farm economy, and opportunities to diversify income also affect exit. However, flow economic variables, such as current year return-on-assets and agricultural support payments, are not associated with exit and disinvestment. Given that US farmers are now facing significant income volatility, the findings point to a level of resilience. The results suggesting that current and recent income fluctuations are less likely to drive the exit of retirement age farmers have important policy implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".