The Politics of Taxation and Tax Reform in Times of Crisis: Covid-19 and Attitudes towards Taxation in Sierra Leone
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The pandemic has had significant fiscal implications around the world. A key question facing governments is how the pandemic has shaped taxpayer attitudes and what that means for the prospects for tax reform and new revenue raising. We aim to understand the impacts of the pandemic on attitudes toward taxation in Sierra Leone with novel survey data, collected before the pandemic, shortly after the pandemic’s onset, and for almost a year afterwards. Four key findings emerge. First, immediately after the crisis onset we see increased support for taxation in Freetown, despite escalating economic challenges. Second, at the same time that taxpayers show greater general support for taxation they become more likely to believe that one could refuse to pay taxes if government fails to deliver services in return. Third, while we lack baseline data on support for progressive taxation, we find rising and sustained support for it over the course of the pandemic. Finally, although we see an initial increase in willingness to pay more taxes for services, that support erodes over time. These findings have significant implications for understanding both immediate responses to the pandemic, and the broader politics of taxation and tax reform.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it