Bibliographic record
Abstract
This thesis explores the argument that the need for governments to raise tax revenue, as \nopposed to relying on resource rents or other sources of non-tax revenue, may increase the \nlikelihood that they will be responsive and accountable to their citizens. It employs a \ncombination of quantitative and qualitative methods, first testing the relationship between \ntax reliance and accountability econometrically using cross-country data and then turning to detailed case studies from Ghana, Kenya and Ethiopia. The econometric results conclude that while existing data is consistent with the argument that tax reliance contributes to greater responsiveness and accountability, it is not possible to establish causality due to a combination of data limitations and the complexity of the underlying causal processes. This ambiguous finding provides motivation for the detailed case studies that follow. The causal model developed here proposes that the need for governments to rely on taxation may strengthen taxpayer demands for responsiveness and accountability, owing to the possibility of tax resistance and the role of taxation as a catalyst for collective action. Consistent with this model, the case study chapters present detailed historical narratives that capture significant examples from each of the three countries in which the need for taxation has contributed significantly to the expansion of responsiveness and accountability. As importantly, the case study evidence provides a nuanced understanding of the nature of the connections between taxation, responsiveness and accountability, highlighting three distinct types of causal processes at work, as well as the most significant social, political and economic contextual factors that shape the potential for tax bargaining. These lessons point toward important policy implications for foreign aid and tax reform more broadly.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".