Welfare and Distributional Impacts of Financial Liberalization in an Open Economy: Lessons from a Multi-Sectoral Dynamic CGE Model for Nepal
Bibliographic record
Abstract
By equalizing rates of return across sectors, financial liberalization improves efficiency and equalizes the distribution of income. Efficiency gained in the allocation of resources increases capital usage more in previously heavily repressed sectors such as agriculture and textile, allowing up to a 19 percent expansion in production and employment. The savings and investment responses, degree of factor substitutions, are higher in the complete liberalization than in partial or piecemeal liberalization. Income, consumption, utility and overall welfare of rural and urban households increase. Liberalization is not effective if savings are used in accumulations of unproductive assets i.e. gold, jewellery, urban land, and foreign exchange. Financial liberalization improves the distribution of income by raising the wage rate of rural labor than for urban labor as rural labour-intensive sectors invest more with increased access to financial institutions and demand more labor to complement additional capital employed in these sectors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".