Do Non-Traditional Loans Help Beginning and Female Farmers?
Bibliographic record
Abstract
Beginning farmers and ranchers (BFRs) and women farmers (WFRs) are growing cohorts within the U.S. agriculture. Compared to traditional producers, they are more credit constrained because of limited collateral usually required by traditional agricultural lenders. In recent years, non-traditional lenders entered agricultural credit markets but their role remains unclear. At the same time, traditional lenders consolidated and even closed facilities. We combine data on the use of Non-Traditional Loans (NTLs) from the 2018 Agricultural Research Management Survey and data on geographic location of branches of traditional agricultural lenders (Farm Credit System Institutions, Commercial Banks, and Credit Unions) and of providers of Alternative Financial Services (AFS) to evaluate whether the use of NTLs and access to lending facilities relates to productivity of BFRs and WFRs. The main finding is that the use of NTLs and access to credit from AFS is not associated with productivity. However, credit constraints remain relevant because BFRs and WFRs with larger number of loans are less productive. We conclude that concerns of producers and policy makers that nontraditional lenders may replace traditional ones to the detriment of productivity of vulnerable groups such as BFRs and WFRs are not supported by the data.
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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 | 0.001 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".