An Introduction to Digital Tax Payment Systems in Low-and Middle-Income Countries
Bibliographic record
Abstract
National tax administrations are increasingly investing in the digital facilities needed to make it possible for taxpayers to go online both to file their routine tax returns (e-filing) and remit the tax payments due (e-payment). These facilities potentially benefit both taxpayers and tax administrations. This paper first maps the landscape, explaining which filing and payment technologies are used for tax collection in Africa. We then examine why these technologies are not used to their full potential. Some constraints are on the demand side. These include taxpayers’ preferences for cash and in-person relations and low familiarity with and trust in digital technology. Other constraints lie in infrastructure deficits and broader political, regulatory, and institutional factors. Unlocking the full potential of e-filing and e-payment systems thus seems to depend on meeting several pre-conditions, including solid political will, sound regulatory frameworks, reliable payment infrastructure and adequate investment in human capital. However, there is relatively little reliable evidence of the actual effectiveness of e-services in tax collection. We conclude by outlining some research priorities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".