Does the Exchange Rate and Its Volatility Matter for International Trade in Ethiopia?
Bibliographic record
Abstract
This study was carried out to investigate the impact of the Ethiopian exchange rate and its volatility on international trade. Trade openness was used as a proxy for international trade in the study. The study’s general objective was to investigate how international trade responds to exchange rate levels and volatility. The study relied solely on secondary time-series data spanning the years 1992 to 2019. The Autoregressive Distributive Lag (ARDL) model was used in the study to investigate the long-term relationship between exchange rate level, volatility, and international trade performance. An error correction model was used to estimate the variables in the short term. To conduct the regression analysis, Foreign Direct Investment (FDI), Gross Domestic Product (GDP), and inflation were used as control variables. The finding of the study implies that: in the short term, the exchange rate level was found to negatively and significantly influence international trade. However, exchange rate volatility positively and significantly affects international trade both in the short and in the long term. In addition, gross domestic product, foreign direct investment, and inflation have a positive effect on international trade both in the short term and long term. This finding lends support to the J-curve effects, which suggest an initial loss in the short term followed by a dramatic gain in the long term. However, the findings of this study suggest that there is no significant gain from international trade to justify currency depreciation in Ethiopia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".