Democracy and Taxation: Evidence from African Countries
Bibliographic record
Abstract
ABSTRACT Using a database on fiscal performance from the African Economic Outlook (AEO) for 51 African countries, this article examines the link between regime type and taxation. The existing theoretical and empirical literature on this issue has thus far yielded very contradictory results, and the relationship between regime type and taxation in the African case has not been studied as much, largely because of insufficient data. Yet, the African case is interesting for several reasons. First, many African countries have democratized in the past two decades. At the same time, tax revenue as a percentage of gross domestic product (GDP) has increased, in part due to resource-related tax revenues, and several African countries are already making significant tax effort. There has also been much emphasis in recent years about the need for African countries to mobilize more resources domestically in order to reduce their reliance on external sources. Examining the determinants of taxation and tax structure is thus an important question, which this article attempts to answer. Controlling for various socioeconomic factors, the authors find some evidence of a positive relationship between regime type and taxation, as well as some support for the idea that this relationship varies according to the type of taxes that are considered. Based on the authors’ findings, this article presents some policy recommendations that can strengthen the link between democracy and taxation in African countries.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".