The Politics of Taxation and Tax Reform in Times of Crisis: Covid-19 and Attitudes Towards Taxation in Sierra Leone
Bibliographic record
Abstract
The Covid-19 pandemic has had significant fiscal implications around the world. A key question facing governments is whether and how the pandemic has shaped taxpayer attitudes and what that means for the prospects for tax reform and new revenue raising in the wake of the pandemic. We aim to understand the impacts of the Covid-19 pandemic on attitudes toward taxation and, in turn, to unpack what the crisis reveals about the dynamics and politics of taxation more broadly. We do so in the context of 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 onset of the crisis we see increased support for taxation in Freetown, despite escalating economic challenges. Second, however, we also see taxpayers express increasingly conditional attitudes toward taxation; that is, at the same time that they 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 from before the pandemic on support for progressive taxation, we find rising and sustained support for progressive taxation over the course of the pandemic. Finally, although we see an initial increase in willingness to pay more for taxes for services immediately after the onset of the pandemic, we find evidence of that support eroding over time, potentially reflecting a combination of continued economic hardship, declining feelings of social solidarity, and some disappointment with government taxation. These findings have potentially significant implications for understanding both immediate responses to the pandemic, and the broader politics of taxation and tax reform.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".