Banking and Insurance Services Liberalization and Development in Bangladesh, Nepal, and Malaysia: A Comparative Analysis
Bibliographic record
Abstract
This paper draws from three country case studies of the liberalization and development of the banking and insurance service sectors in Bangladesh, Nepal and Malaysia, which were undertaken as part of an ARTNeT regional study on trade in services led by the author. The paper first explores the relationship between financial and economic development, and the causality between service sector liberalization and financial deepening. An overview of the growth and importance of the banking and insurance sectors as well as of the regulatory frameworks in place in the three economies is then presented, followed by comparative case studies of bank performance according to ownership structure. The case studies reveal that private banks (including joint-venture banks) tend to outperform state-owned banks in the two least developed countries. The following three main challenges are identified for financial sector development in the three economies: (a) non-performing loans in government banks; (b) the failure of insurance companies to undertake long-term investments; and (c) the continued limited access by the poor and small businesses to credit. The paper concludes with policy implications.
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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 | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".