Critical risk factors of the project finance loan spread in the infrastructure sector: Experience from the ASEAN countries
Bibliographic record
Abstract
This paper finds that in ASEAN-4, the micro loan characteristics: loan amount and LIBOR whilst the macro characteristics: inflation, net export and GDP growth influence the loan spread in the project finance. However, simultaneously at the country level, the determinants of the loan spread are distinctive to each country’s infrastructure industry characteristic. The paper’s main contribution relates to the determinants of the project finance loan spread at the country level and regional level, ASEAN-4. The purpose of this paper is to fathom the critical risk factors behind the project finance loan pricing differential across the ASEAN-4 countries. Hence, the policy makers, project developers and lenders can have a better understanding of the drivers behind the project finance loan spread pricing. The study adopted an ordinary least square (OLS) regression methodology and collected data from ASEAN-4 countries consisting of Indonesia, Malaysia, Philippines, and Thailand.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".