The Casual Nexus of Banking Sector Development and Poverty Reduction
Bibliographic record
Abstract
The paper aims to investigate the inter-temporal causal relationship between banking sector development and poverty reduction in Bangladesh from 1976 to 2010. We have applied new methods using modern time series econometrics techniques based on simulations that are robust to the violation of statistical assumptions, especially when the sample size is small, as is the case in this paper. The results reveal that there is a long-term equilibrium relationship between banking sector development and poverty reduction in the case of Bangladesh. Surprisingly, poverty reduction appears to be a long-term forcing variable to the explanation of Bangladesh’s banking sector development. There is bidirectional causality between these variables. The diagnostic tests show that the underlying desirable assumptions are fulfilled. Time series data on poverty in many developing countries, particularly in Bangladesh, is scant and inadequate. The empirical results of this study will help policy makers determine whether poverty reduction in Bangladesh is a spur to financial sector development. This implies that, in the long term, Bangladeshi policy makers can influence the reduction of poverty through financial sector development. Although several attempts have been made to investigate the relationship between financial development and growth, this paper is the first of its kind to empirically examine the causal relationship between poverty and the development of the banking sector in Bangladesh. Keywords: Poverty; Banking Sector Development; Cointegration; Error Correction; ARDL Bounds Testing; Bangladesh JEL Classifications: C32; F24; F43
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".