Reassessing Tax and Development Research: A New Dataset, New Findings, and Lessons for Research
Bibliographic record
Abstract
There is growing concern with the weaknesses of economic statistics relating to developing countries, and the risks that poor data have generated misleading research findings and poor policy advice. Cross-country tax data offer a striking example, with existing datasets frequently highly incomplete, analytically imprecise, plagued by errors, and sharply lacking in transparency. This paper introduces the new Government Revenue Dataset from the International Centre for Tax and Development, which provides a more reliable, transparent, and comprehensive basis for cross-national research. This new dataset has initially been used to re-examine major questions about the relationships between tax and aid, elections, economic growth, and democratization. The results deepen some previous conclusions and call others seriously into question—notably the assertion that aid dependence consistently undermines domestic revenue efforts. Above all, the research demonstrates the value of the new dataset, the broader sensitivity of many results to changes in data quality and coverage, and the consequent importance of expanded attention to, and investments in, data quality.
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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 | 0.029 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.026 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".