Analysis of Scale-Based Growth Capacities in Iran's Manufacturing Sector
Bibliographic record
Abstract
The main goal of this research is to evaluate and analyze the condition of economies of scale in Iran's manufacturing sector and examine its developments using the econometric methodology with the Translog cost function approach.The findings of this research show that Iran's industries have not yet used the benefits of their economies of scale during the past two decades, and from this point of view, they still have small scales. The ratio of added value to the number of enterprises and the ratio of real investment to the number of enterprises as indicators of the scale of production in the 1390s compared to the 1380s has become smaller. In other words, the scale of production in Iran's manufacturing sector has decreased during the last decade, and this is a confirmation of a long-term stagnation in Iran's industrial sector. Also, the results of the investigation at the level of two-digit ISIC codes also shows that the industries of food products, production of tobacco and tobacco products, production of clothing, production of leather and related products, production of other means of transportation, and production of furniture in They have moved to reduce the scale. On the other hand, the industries of production of various types of beverages, production of paper and paper products, printing and reproduction of recorded media, production of chemicals and chemical products, and production of drugs and chemical and herbal medicinal products have clearly moved towards increasing scales. The most important policy recommendation of this study is predicting the price and access to raw materials, access to financial resources, reforming the ownership and management structure of large industrial companies by moving towards the private sector and especially market development for domestic industrial products and the possibility of accessing markets. The external goal is to increase industrial exports along with increasing competitiveness in global markets
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".