Labor shortages and agricultural trucking rates
Bibliographic record
Abstract
Abstract In the United States, truck rates for perishable food, the per‐mile rate charged for trucking services to move perishable food from farms to stores, rose substantially in the post‐COVID‐19 pandemic era. We argue that rising truck rates is a signal of a broader shortage of truckers, but the connection between labor shortages, rising truck rates, and a lack of trucking services has yet to be established empirically. In this paper, we develop an empirical examination based on an equilibrium job search, matching, and bargaining framework in which we estimate the role of labor shortages in accelerating driver‐wage growth, and truck rates for agricultural products. We estimate the model by combining US Bureau of Census Current Population Survey data on truck driver wages with USDA‐Agricultural Marketing Service Service data on truck rates to establish the linkage between trucker supply and the demand for trucking services. We find that the COVID‐19 pandemic was responsible for a rise in for‐hire trucker wages of some , a rise in average truck rates of nearly and that the gap between trucker‐job openings and successful matches explains a significant, but small, rise in truck rates.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".