Does reliance on tax revenue build state capacity in sub-Saharan Africa?
Bibliographic record
Abstract
Academics and donors have increasingly argued that African states can enhance their general administrative capacity by improving tax revenue collection. Proponents argue that administratively demanding improvements in tax administration may spillover to other areas of public administration by introducing improved practices, necessitating improvements elsewhere and providing data for other government activities. We make the first effort to test this hypothesis empirically, using an improved cross-country data set for sub-Saharan Africa. We find some evidence that from 1973 until the late 1990s improvements in tax administration tended to precede broader administrative improvements, consistent with the research hypothesis. By contrast, we find no evidence of such a pattern over the past decade. We conclude that these results provide tentative support for the hypothesis that improvements in tax collection can be a catalyst for broader gains in state capacity, but that such linkages are not guaranteed and depend on the particular character of reform. Points for practitioners Those involved with public administrative reform efforts have long been confronted with the question of whether reform is, or should be, an essentially system-wide process, or focused on developing ‘pockets of effectiveness’. This debate is particularly relevant to tax reform: a growing academic literature has argued that tax reform can be a catalyst for system-wide change, but the dominant reform model has focused on the creation of autonomous revenue agencies to achieve rapid but focused capacity gains. This research examines the case for believing that tax reform can be a catalyst for broader reform, and thus the case for adopting a reform model that focuses more explicitly on system-wide change.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".