An Analysis of the Aspects Hampering Informal Sector Tax Administration: Case of the Zimbabwe Revenue Authority
Bibliographic record
Abstract
The main focus of the study was to ascertain the potential of the informal sector to provide much-needed revenue for the government. It also focused on the challenges faced in informal sector revenue taxation and possible solutions thereof. The Zimbabwe revenue authority has maintained presumptive tax for the sector and subcontracting to the city of Harare for the collection of revenue from the informal sector. Despite all this, the industry still underperformed in terms of revenue raised. The study sought to find out challenges of taxing the informal sector, the potential of the informal sector, the effectiveness of the Zimbabwe revenue authority in taxing the informal sector, and possible ways of improving the taxing of this rampant sector. The study found out that there is great potential from the informal sector, but turning it into tangible gains has been elusive due to political interference, lack of proper infrastructure, unfair application of tax laws and general mistrust of the government. The study recommended that the government ought to play an active role by making sure there is the political will to make sure that players in the informal sector contribute to the focus in line with Adam Smith’s general principles which include fairness and equity. There is a need for staffing levels to be commensurate with the workloads and also the motivation of the employees. The research also recommended the adaptation of Information Communication Technology to ensure accountability and traceability of transactions in the informal sector as they move away from a cash-based system recommendation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".