External Debt & Economic Growth: Case of Tunisia
Bibliographic record
Abstract
The main objective of this paper is to explore the influence of external debt (measured by two indicators that are outstanding debt and debt service in relation to exports of goods and services) on investment and economic growth in Tunisia during a period of 51 years which runs from 1961 to 2011, using vector autoregressive model (VAR). The empirical results show that, in the short term, outstanding debt and debt service in relation to exports of goods and services do not cause economic growth. In the long term, the external debt service is detrimental to Tunisian economy. In Tunisia, the problem is not a debt problem in itself, but the problem concerns the use of this debt. External debt is allocated to activities with both low added value and profitability. In the absence of a clear industrial policy, these activities are traditional activities that do not create wealth, correspond with competitive advantages of the country and employ the qualified workforce. Therefore, the unemployment rate of graduates increases regularly. Furthermore, a significant share of borrowed funds are intended to pay the salaries of the public sector, that have represented more than a third of the state budget in recent years. Then, external debt did not help the country to develop because those salaries went into consumption of some imported goods that are not produced in the home market.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".