Bibliographic record
Abstract
We estimate trade costs under large zero trade by using daily data on agricultural goods trade within a country. Because of the nature of daily data, there is a prominent zero daily trade between regions and daily delivery is subject to noisy demand and supply shocks, which tends to create heteroskedasticity of the data. Hence, we use Poisson Pseudo Maximum Likelihood (PPML) to estimate gravity model and investigate non-linear nature of trade costs. Empirical analysis shows a statistically significant, but economically subtle non-linearity in trade costs. We also aggregate daily data to monthly level to examine whether shocks are smoothed and thus those impacts are dampened. Our estimation shows that the difference is minor. Comparison of the results with other estimation methods such as the least squares of linear-in-log model and various Tobit procedures is also conducted. There is a large difference in the results between simple least squares and PPML, suggesting the significant heteroskedasticity. We also calculate outward and inward multilateral resistance terms to derive the incidence of trade costs and find that a large portion of trade costs is the buyers' burden.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".