Lessons from Voluntary Compliance Window (VCW): Malawi's tax amnesty programme
Bibliographic record
Abstract
The Malawi Revenue Authority (MRA) implemented the Voluntary Compliance Window (VCW) in 2013/2014 fiscal year as a means of bringing non-compliant taxpayers into the tax net. The programme was a huge success in terms of revenue and the cost of collection at 0.8% was by far below the 3% benchmark for the gross tax revenues of the Authority. However, the long-term compliance impact of the programme was not evaluated. Hence, the paper intends to bridge knowledge gap. Firstly, observing trends in debts and penalty payments covering and extending beyond the amnesty period, the paper finds that debts substantially declined after amnesty period while penalties rose sharply after VCW. Secondly, by constructing a counterfactual for large taxpayers, the paper finds that tax payments of participants significantly improved after the programme. Thirdly, the paper examines tax payment patterns by VCW participants one year after the programme. About 75 percent of amending filers subsequently paid their taxes one year after VCW with a higher mean income than the non-subsequent taxpayers. Lastly, the paper finds that smuggling remains high after observing variations in customs offences during VCW and a year later, in FY2015/16. The question remains “Should Malawi reconsider another Voluntary Compliance Window after 3 years?” The paper agrees with most previous studies by strongly discouraging another amnesty while encouraging post-amnesty enforcement efforts.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".