Does External Debt Service Devalue Local Currency in the Long Run? Empirical Evidence from Egypt
Bibliographic record
Abstract
The aim of this study is to identify the extent to which there is an effect of external debt service on the exchange rate in Egypt in the long run, where the change in the exchange rate has great importance in changing currency value and thus affecting its function as a store of value and a standard for forward payments and then in the redistribution of income and wealth, It also has an effect on some macroeconomic variables, such as inflation, exports, imports, and thus the current account. The study examines the estimation of the long-run relationship between the external debt service and the exchange rate in Egypt in the period 1980-2019 and relies on the exchange rate of the dollar against the Egyptian pound as a dependent variable, while the explanatory variables were the external debt service, gross capital formation, broad money growth, deposit interest rate, household final consumption expenditure, gross savings, and terms of trade adjustment. The methodology is based on Vector Error Correction (VEC) and the study concluded that there is a significant long-term relationship between the value of the Egyptian pound and all the variables explained in the study, as the error correction coefficient is negative and significant. Also, there is an inverse statistically significant relationship between the value of the Egyptian pound and each of the external debt service, the deposit interest rate, and gross savings; any change of 1% in the external debt service, the deposit interest rate, and gross savings leads to a devaluation of the Egyptian pound against the dollar by 4.8%, 0.04%, and 0.05%, respectively. The study also concluded that there is a positive, statistically significant relationship in the long term between the value of the Egyptian pound and each of gross capital formation, broad money growth, households' and NPISHs' final consumption expenditure, and terms of trade adjustment, as any change of 1% in these variables leads to an increase in the value of the Egyptian pound by 0.16%, 0.05%, 0.27%, and 6%, respectively. This study recommends that decision makers consider all the reasons that would reduce the external debt service in order to preserve the value of the Egyptian currency in the long run.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".