Fiscal Policy and Optimal Taxation in Sierra Leone: Testing for Tax Smoothing Hypothesis
Bibliographic record
Abstract
This paper empirically investigate whether the budget imbalances in Sierra Leone over the review period is consistent with optimal tax policy. The procedure involves testing if tax smoothing hypothesis hold for Sierra Leone. In this regard, three different empirical approaches were performed. Firstly, I examine the random walk property of the tax rate. The null hypothesis of non-stationarity of tax rate could not be rejected, which implies the tax rate follows random walk. Second, I examined whether changes in tax rate is predictable by regressing changes in tax rate by its own lagged values. The result shows that tax rate is unpredictable, as changes in tax cannot be determined by its lagged values. Finally, a VAR model was employed to examine whether tax rate can be predicted by its own lagged values together with changes in the government spending rate and the growth rate of real GDP. The results indicate that all the variables employed were found not be significant is predicating the tax rate. Overall, all the empirical estimations support the existence of tax smoothing over the sample period and that the budget inbalances over the review period is consistent with optimal tax policy.
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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 | 0.003 | 0.018 |
| 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 | 0.000 |
| 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".