Optimal Taxation and Economic Growth in Tunisia: Short and Long Run Analysis
Bibliographic record
Abstract
Tax policy is among the most common and relevant instruments in the toolkit of policy-makers when thinking about promoting growth, yet there is not compelling evidence regarding its effect in Tunisia. Using a variety of approaches, we measure firstly the optimal tax burden rate using Scully's static model and the quadratic model. For Scully's static model, gross domestic product is the dependent variable. For the quadratic model, growth rate is a dependent variable explained by tax rate in level and in square. Secondly and according to stationary and cointegration test results, we focus on the long-term effects on gross domestic product of the important taxes, namely tax revenue and private receipts. In this second study, we use a basic Scully model and we develop a vector error correction model technique. Our results show that optimal tax burden rate has to be situated between 12.8% and 19.6% of gross domestic product which is widely lower than the current rates. The long-term analysis estimates an optimal rate of 14% of gross domestic product which can participate to increase economic growth, to stabilize the tax evasion and to encourage investment especially after the Tunisian revolution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".