Position of Iran's Economic Vulnerability and Resilience among Oil-Dependent Countries
Bibliographic record
Abstract
The exogenous economic shocks cause countries with a higher degree of vulnerability to be more influenced by them and be less able to reduce the negative effects of such shocks because in most cases, they are beyond control of countries. In contrast, countries with a high level of resilience can repair and reconstruct their economy after facing such economic shocks. In this regard and by considering economic exogenous shocks, especially oil price shocks on economy of oil-dependent countries that are mostly beyond their control, identifying the level of economic resilience and vulnerability in these countries has a considerable importance. In the present research, there were calculated and extracted resilience and vulnerability indices for the selected oil countries from 2005 to 2012 using the indicators introduced by Brigoglio et al. The results showed that in 2012 economy of Iran, compared to some countries in the Persian Gulf region, had a better performance in terms of economic vulnerability index (a lower level of vulnerability); but in terms of resilience, it is at lower level in comparison with majority of the selected oil-dependent countries: in terms of economic resilience, Iran is in 16th place among 18 selected countries. Based on this indicator, Canada and Venezuela have the most and the least resilience respectively. In vulnerability index, Iran is in 12th place among 18 selected countries. Such a situation in areas of vulnerability and resilience indicates high vulnerability potential of the country against external shocks. By comparing the performance of different countries in areas of resilience and vulnerability we can say that oil income cannot be blamed as the sole reason of low resilience and high vulnerability. It is actually the style and method of management of different sectors of countries, especially management of oil incomes, that determines the level of their resilience and vulnerability. This issue requires special attention of economic and non-economic officials and politicians to resilience and vulnerability of the country.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".