The Causal Link between Financial Sector and Economic Development: The Case of Kazakhstan
Bibliographic record
Abstract
This study empirically explores the causal relationship between financial depth and economic growth in Kazakhstan. The specific objective of this study is to investigate whether the causality direction between financial depth and economic growth in Kazakhstan does in fact apply. With this aim, the data from 20 banks operating between 2006 and 2015 was used. This data was obtained from the National Bank of Kazakhstan Statistical Bulletin. Quarterly observations are collected in aggregate form during 1Q 2006 – 4Q 2015 for GDP in level and stock market index value, and individual observations from the 20 largest banks during 1Q 2006 – 4Q 2015 for total lending volume in level in percent point, and total deposit volume in level. These quarterly data are employed in the panel study framework. The results of the study show that banks’ lending significantly strongly affects economic growth in Kazakhstan. At the same time, GDP also significantly strongly affects banks’ lending. Therefore, there is mutual causality between banks’ lending and the economy (Gross Domestic Product) in Kazakhstan. Both the economy and the financial sector do affect each other positively and significantly.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".