Climate Variability and Agricultural Loan Delinquency in the US
Bibliographic record
Abstract
Inter-annual climate variability in the Southeastern US that affects farm productivity and cash flows is largely dependent on the predictable El Nino Southern Oscillation (ENSO) phenomenon. In this paper, we estimate the association between the ENSO anomalies and the performance of agricultural loan portfolios of the Farm Credit System (FCS) institutions - the largest agricultural lender in this region. We find that, compared to neutral years, the share of delinquent loans in the FCS portfolio decreases by 1.5 to 2 percentage points following La Nina years and increases by 1.5 to 2 percentage points following El Nino years. These delinquencies are generally resolved because the impact on loan write-offs is much smaller, although statistically significant which suggests that the FCS institutions have well-diversified portfolios. The results also suggest that agricultural insurance markets are complementary to credit markets, that land values at loan origination have a positive impact on delinquencies, and that loan write-offs decrease with the lender’s size.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".