Taxation and Development: What Have We Learned from Fifty Years of Research?
Bibliographic record
Abstract
We have learned a great deal about taxation and development over the last half-century but even the best research answers to particular questions have been difficult to apply in practice. The standard approach to tax and development has undergone a number of major model changes over the years but no magical fiscal medicine suitable for all has been found. This brief paper provides a perspective on a half century of research on taxation and development and notes some questions that call for more research. Moreover, since even the best research is only one of many inputs shaping public policy to some extent the task is not so much to improve research as to improve how we market what we learn to those who can, if they wish, put the knowledge to use. Building up adequate institutional capacity in the tax field, both inside and outside government, is critical to being able to adapt policies to changing circumstances and needs, thus ensuring some degree of robustness and resiliency. The role of outsiders like academics and aid agencies in this process is more to be supportive when countries want to reform their systems than to tell them when and how to do it.
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 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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".