Financial repression, financial deepening and their effects on Iranian industrial development
Bibliographic record
Abstract
This research analyzes the effects of financial deepening as well as repression on industrial development in Iran. Using some time series data, the proposed study applies the method originally introduced by Johansen and Maximum likelihood estimation and inference on cointegration-with applications to the demand for money. Oxford Bulletin of Economics and statistics, 52(2), 169-210.] to measure the effects of these two factors on market development over the period 1970-2011. The results indicate that as the bank deposit increases, we may expect an increase on financial deepening and market development. On the other hand, as inflation increases, we could easily verify market repression and a reduction on market development. In addition, when there was an increase on loans dedicated to private sector, there was an increase on market development. Finally, there were some evidences to believe that currency devaluation could hurt market development by increasing the price of raw materials and market uncertainty.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".