Measuring the Foreign Exchange Premium and the Premium for Non‐Tradable Outlays for 20 Countries in <scp>A</scp>frica
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Bibliographic record
Abstract
Abstract In this paper, we develop an analytical general equilibrium framework to measure the foreign exchange premium and the premium for non‐tradable outlays for a country. The framework allows us to capture in a consistent manner the impacts of the sourcing of funds and their expenditure on tradable and non‐tradable goods and services of investment projects. An application of the model is carried out for 20 countries in Africa. The results show that the foreign exchange premiums range from 2.4% to 9.0% and the premium for non‐tradable outlays from −0.7% to 2.9%. The empirical values depend on a number of factors, including the indirect taxes, production subsidies and international trade distortions of a country. These premiums should be incorporated into the economic evaluation of investment projects.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it