Effects of Tax Reforms on Buoyancy and Elasticity of the Tax System in Kenya: 1963–2010
Bibliographic record
Abstract
The study examined the effects of tax reforms on tax buoyancy and elasticity estimates. The specific objectives of the study were; to determine the effect of tax modernization programme and revenue administration reforms and modernization programme on tax buoyancy and tax elasticity. The study employed regression analysis and used annual time series data for the period 1963 to 2010. Secondary data from Kenya National Bureau of Statistics, Kenya Revenue Authority, Central Bank of Kenya and World Bank was used. Elasticity estimates were determined by adjusting data for discretionary changes using the proportional adjustment method. The study revealed that both revenue administration reform and modernization programme (RARMP) and tax modernization programme (TMP) were important in explaining the variations in buoyancy and elasticity of the tax system in Kenya. Although the reforms analyzed had positive effect on both tax buoyancy and elasticity, the results indicate that this was not sufficient to help generate adequate revenue to finance the ever increasing government expenditure. With an inelastic tax system, the Kenya government has to re-evaluate the implementation strategies and pursue further reforms for it to fully exploit the tax revenue potential in the economy.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".