Efficiency and Capacity of Iran’s Cropland Products Exports: An Application of Stochastic Frontier Gravity Model
Bibliographic record
Abstract
Iran’s agricultural exports have grown significantly in recent years. Cropland products (HS07) have become the second most important group in Iran’s agricultural exports over the last years. However, few studies have investigated the export potentials of cropland products. Therefore, this study aims to determine the main factors of Iran’s cropland products exports and calculate the export efficiency and potential in the trading partners. For this aim, the stochastic frontier gravity model is estimated based on balanced panel data covering 21 importing countries over the period of 2001 to 2021. The results indicated that economic and physical size of importing countries have positive and significant effect on the exports of Iran’s cropland products. In addition, common border between Iran and trading partners and economic sanctions have also positive and significant effect on the cropland products exports, while geographical distance between Iran and importing countries has negatively effects on the exports. The results of export efficiency showed that Iran does not have 100 percent efficiency in any destination market over the period of 2015 to 2021. Iran has an export efficiency of more than 50 percent only in Afghanistan, United Arab Emirates, Canada and Iraq. According to the results, Iran has the highest potential for exports of cropland products in Iraq. Hence, considering the high potentials in neighboring countries and significantly positive effect of common border partners, it is suggested that trading countries with common border like Iraq should be a top priority for the exports of cropland products.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".