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Record W3112685217 · doi:10.19088/ictd.2020.005

Tax Amnesties in Africa: An Analysis of the Voluntary Disclosure Programme in Uganda

2020· preprint· en· W3112685217 on OpenAlexaboutno aff
Solomon Rukundo

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEnforcementRevenueBusinessTax avoidanceAmnestyTax revenueCompliance (psychology)Public economicsIndirect taxTax reformAd valorem taxValue-added taxAccountingEconomicsPolitical scienceLaw

Abstract

fetched live from OpenAlex

Tax amnesties have taken centre stage as a compliance tool in recent years. The OECD estimates that since 2009 tax amnesties in 40 jurisdictions have resulted in the collection of an additional €102 billion in tax revenue. A number of African countries have introduced tax amnesties in the last decade, including Nigeria, Namibia, South Africa and Tanzania. Despite their global popularity, the efficacy of tax amnesties as a tax compliance tool remains in doubt. The revenue is often below expectations, and it probably could have been raised through effective use of regular enforcement measures. It is also argued that tax amnesties might incentivise non-compliance – taxpayers may engage in non-compliance in the hope of benefiting from an amnesty. This paper examines the administration of tax amnesties in various jurisdictions around the world, including the United States, Australia, Canada, Kenya and South Africa. The paper makes a cost-benefit analysis of these and other tax amnesties – and from this analysis develops a model tax amnesty, whose features maximise the benefits of a tax amnesty while minimising the potential costs. The model tax amnesty: (1) is permanent, (2) is available only to taxpayers who make a voluntary disclosure, (3) relieves taxpayers of penalties, interest and the risk of prosecution, but treats intentional and unintentional non-compliance differently, (4) has clear reporting requirements for taxpayers, and (5) is communicated clearly to attract non-compliant taxpayers without appearing unfair to the compliant ones. The paper then focuses on the Ugandan tax amnesty introduced in July 2019 – a Voluntary Disclosure Programme (VDP). As at 7 November 2020, this initiative had raised USh16.8 billion (US$6.2 million) against a projection of USh45 billion (US$16.6 million). The paper examines the legal regime and administration of this VDP, scoring it against the model tax amnesty. It notes that, while the Ugandan VDP partially matches up to the model tax amnesty, because it is permanent, restricted to taxpayers who make voluntary disclosure and relieves penalties and interest only, it still falls short due to a number of limitations. These include: (1) communication of the administration of the VDP through a public notice, instead of a practice note that is binding on the tax authority; (2) uncertainty regarding situations where a VDP application is made while the tax authority has been doing a secret investigation into the taxpayer’s affairs; (3) the absence of differentiated treatment between taxpayers involved in intentional non-compliance, and those whose non-compliance may be unintentional; (4) lack of clarity on how the VDP protects the taxpayer when non-compliance involves the breach of other non-tax statutes, such as those governing financial regulation; (5)absence of clear timelines in the administration of the VDP, which creates uncertainty;(6)failure to cater for voluntary disclosures with minor errors; (7) lack of clarity on VDP applications that result in a refund position for the applicant; and (8) lack of clarity on how often a VDP application can be made. The paper offers recommendations on how the Ugandan VDP can be aligned to match the model tax amnesty, in order to gain the most from this compliance tool.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.088
GPT teacher head0.249
Teacher spread0.160 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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