The relationship between financial development indicators and human capital in Iran
Bibliographic record
Abstract
Human capital is considered as one of the major factors to promote economic stability, especially in developing countries. Furthermore, one of the most important factors in developing human capital is taking the advantage of facilities and economic capabilities in education. Development of financial system provides such abilities for the prospective countries. This paper studies the influence of financial development on human capital in Iran over the period 1977-2010 with the application of a VAR model. The results indicate the cash flow in Iran has a negative effect on human capital, which is the main cause of the increase in inflation. Education is a long term investment and when inflation hikes, people switch to alternative investments. However, the facilities provided by the banking system has negative effect on human capital due to the lack of the best financial resource allocation. However, since most of university graduate students in Iran practically have adequate skills and education, they do not have enough capital to start a business. Providing financial assistance for the private sector can lead to a business in which they can use their skills and education towards promoting production. Financial development could only slightly contribute to human developments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".