Impact of economic and financial factors on tax revenue: Evidence from the Middle East countries
Bibliographic record
Abstract
This paper examines the impact of economic and financial factors on tax revenue of Bahrain and Oman from 1990 to 2010. For this purpose, panel regression analysis is performed by considering economic and financial factors including growth domestic product (GDP), Deposit Interest Rate, Lending Interest Rate, Interest Rate Spread, Real Interest Rate, Bank Capital to Asset Ratio, Bank nonperforming loans to total gross loans, Risk premium on lending, Foreign direct investment net inflow and Cash surplus deficit. A conceptual model is developed for this purpose and the key findings are explained. The outcomes of the study explain that there was a significant relationship between Tax revenue and both economic and financial factors i.e. GDP growth, Bank capital to asset ratio, the Risk premium on lending, Foreign direct investment net inflow and Cash surplus/deficit over the period of study. The findings of the study are very much useful for the policymakers to consider which factors are affecting the tax revenues and in which direction. However, the findings of the study can be more meaningful with the addition of more economic and financial factors as well. Besides, the consideration of other Asian states will provide more evidence for the generalization of the findings. Meanwhile, this study will be a policy note on on-going tax reforms in selected Middle East countries and will be helpful for policymakers and researchers in conceptualizing the tax revenue model for them.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".