Personal Income Taxes in the Middle East and North Africa: Prospects and Possibilities
Bibliographic record
Abstract
With the exception of a few North African countries, personal income taxes (PITs) play little or no role in the Middle East and North Africa (MENA), often yielding less than 2 percent of gross domestic product (GDP) in revenue. This paper examines how PITs have evolved in recent decades, and what they might look like in the next 20 years. Throughout the region, top marginal tax rates on labour and business income of individuals have declined substantially, a trend that mirrors reductions in advanced and developing economies. Taxation of passive capital income has changed very little, and the revenue contribution from this source remains low throughout the region, averaging less than 1 percent of GDP and concentrated in oil-importing non-fragile states. Social security contributions (SSCs) have increased in importance in nearly all MENA countries, and some countries have introduced additional payroll taxes and levies. The combination of reduced marginal tax rates, light taxation of income from capital and business activities, and increases in SSCs has resulted in income tax systems that create disincentives to work and incentives for informality, and contribute little to government revenue and income redistribution. Given differences in economic and political structures, demographics, and starting points, the path to PIT and SSC reforms will vary across the region. Countries with relatively mature PIT/SSC systems, where revenue performance has improved in the past two decades, will increasingly need to balance revenue and equity objectives against efficiency objectives (in particular, labour market incentives and informality). Countries without a PIT will have to weigh whether a consumption tax/SSC system that mimics a flat tax on labour income is sufficient to diversify revenue away from oil, and whether to adopt PITs to address rising income and wealth inequality. Finally, fragile states, which face more political volatility and have weaker fiscal institutions than non-fragile states, will have to focus on simplicity of tax design and collection to be able to raise revenue from PITs.
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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 | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".