Does Currency Devaluation Improve the Trade Balance in the Long Run? Evidence from Malawi
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract: This paper explores the impact of nominal exchange rate devaluation on the trade balance for Malawi. A small‐open economy IS‐LM aggregate supply model of Malawi estimated using time series data covering the period 1967–96 is used in the simulation analysis. The results of the simulation experiment show that devaluation helps to improve export performance and to curtail the growth of imports in the long run, which lead to improvement in the trade balance position. The results provide evidence supporting the view that nominal devaluation can indeed be a quite powerful tool in minimizing the imbalances in Malawi's international trade. Résumé: Le présent article analyse l’incidence de la dévaluation du taux de change nominal sur la balance commerciale du Malawi. Ses auteurs se servent d’un modèle IS‐ML d’entreprise en économie ouverte spécialisée dans la fourniture d’agrégats, censé utiliser des données chronologiques concernant la période 1967 à 1996, a pour effectuer cette analyse. Les résultats de cet exercice de simulation montrent que la dévaluation contribue, à long terme, à améliorer la qualité des exportations — et, partant, la situation de la balance commerciale — et à freiner la croissance des importations. Ils viennent, en outre, appuyer la théorie selon laquelle la dévaluation peut, en réalité, contribuer sensiblement à réduire les déséquilibres du commerce international au Malawi.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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