Taxation and Development: What Have We Learned from Fifty Years of Research?
Bibliographic record
Abstract
Summary This paper considers how economic thinking about taxation in developing countries has changed over the last half century. It suggests that three different ‘models’ of development taxation may be discerned over this period. The key element in the first model, which was derived from the dominant public finance literature in the 1950s and 1960s, was the introduction of a comprehensive progressive personal income tax. Experience proved that this approach was not very useful. Fortunately, increased knowledge of the reality of conditions in developing countries, combined with post‐1970 theoretical and empirical studies of taxation, soon led to the emergence of a second model for development taxation, centered on a broad‐based VAT and much lower rate income taxes, both personal and corporate. While there is still much to be said for this model, more recent investigations of the political and administrative as well as economic dimensions of tax systems in developing countries have led to the gradual emergence of a third ‘model’– or, perhaps better, framework – for development taxation. Unlike the earlier approaches, this approach focuses on the need to ‘custom build’ the different components of the tax system as well as the system as a whole and emphasizes the extent to which sustainable reforms must be developed ‘in house’ by countries themselves.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".