Developing Local Currency Bond Markets: Local Currency Lending's impact on developing Government Local Currency Bond Markets
Bibliographic record
Abstract
The "original sin" describes when countries cannot access financing in their local currency, exposing countries to increasing debt servicing costs from their currency depreciating (Hausmann and Eichengreen 1999, 11).Local currency bond markets (LCBM) are thought to reduce exchange rate risk and increase financial stability.Multilateral development banks (MDB) have become increasingly interested in local currency lending (LCL), often with a secondary goal of helping grow LCBMs.The thesis investigates if MDB's LCL can increase a country's LCBM size.The thesis uses the synthetic control method (SCM) and case study analysis.The SCM is statistically insignificant, while the East Asia and Kenya case studies show MDBs can support developing LCBMs.However, significant growth comes from comprehensive domestic-led policies.The thesis concludes analyzing whether developing LCBMs can improve countries' financial stability, arguing that relative success is based on the size and ability of countries to leverage domestic demand for LCBMs. Table of contents List of figures vList of tables vi
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".