Tax Revenue Productivity of Tax Reforms in Kenya
Bibliographic record
Abstract
Kenya has continued to experience increasing budget deficits. This is despite implementing various tax reforms. To finance the deficit, the Kenyan government should either raise more tax revenue or resort to borrowing. Domestic borrowing crowds out investment while external debt specifically non-concessional loans are tied to some unpopular conditions. The government has an option of considering non-concessional loans but this comes with a price of high interest rates and short payment periods. This means raising more tax with minimum burden is the best option. This study therefore seeks to investigate the responsiveness of Kenya’s tax system to GDP and Discretionary tax measures for the period between 1970 and 2018. Variables used in the study are integrated of order one. Johansen cointegration test reveals presence long run relationship thus informing the study to consider Vector Error Correction Model (VECM). The results reveal that Kenya’s tax system is inelastic but buoyant. This implies that the Kenyan tax system is unresponsive to GDP but responsive to discretionary tax measures. The finding of inelastic tax system has implications for the fiscal policy. The fiscal policy’s managers should target reducing or eliminating the tax exemptions, which might be eroding the effective tax base.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".