Evaluating the Relationship Between Taxation and Economic Growth in Zambia
Bibliographic record
Abstract
From inception, taxation has been the main source of Government revenue across the globe, in the past 30 years, the Zambian government has been raising revenue through taxes despite the Country failing to raise enough revenue to finance the national budget. In many developing countries including Zambia, the prominent source of tax revenue is direct tax despite this tax type being identified as a threat to the growth of Small and Medium Enterprises (SMEs). This study sought to evaluate the effect of taxation on the economic growth of Zambia following various policy changes aimed at achieving middle income status as enshrined in the Vision 2030. The study used multiple regression analysis to analyse time series data. The Augmented Dicker Fuller (ADF), Auto Regressive Distributed Lag (ARDL) and Error Correction Models (ECM) were employed to test the stationarity of data in order to establish both the short-run and the long-run relationship between taxation and economic growth. The study revealed that despite various tax types giving varying results on how they affect economic growth both in the short-run and long-run, they have a positive effect on the growth of the Zambian economy. It is recommended that the Zambian Government improves efficiency in the collection of taxes by further digitalising their systems and embark on tax payer education programs in the quest to increase tax compliance. Further, there is need to reduce on tax exemptions or incentives as this narrows the tax base and introduce systems that will make tax payments easier for tax payers. Lastly, there is need to improve audit capacity by increasing the number of inspectors across the country, this increase poised to improve efficiency in the collection of the tax revenues.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".