Sub-national property tax reform and tax bargaining: Lessons from a quasi-randomized reform program in Sierra Leone
Bibliographic record
Abstract
• We test the link between expanded taxation and improvements in governance. • We capture impacts of tax reform on perceptions of service quality, attitudes toward compliance and indicators of political engagement. • We find positive impacts on perceptions of service quality, and on multiple indicators of political engagement and mobilization. • Increased political engagement is greatest among those who feel that new revenues were not translated into improved service delivery. We evaluate the impact of a quasi-randomized property tax reform implemented in Sierra Leone beginning in 2013 in order to provide evidence about the extent to which expanded taxation results in “tax bargaining” and increased responsiveness and accountability. The paper draws on a panel survey conducted in both treatment and control districts immediately prior to the implementation of a large-scale property tax reform program in 2012 and again in early 2017 in order to offer a uniquely direct and holistic tests of theories linking taxation to expanded responsiveness and accountability. The paper first presents evidence that the tax reform program resulted in large and significant improvements in the perceived quality of public services, consistent with theories linking expanded taxation to improvements in governance. It then provides evidence of individual level changes in attitudes and behaviors that can explain those aggregate improvements in service delivery outcomes: a large expansion of political knowledge, increases in important forms of political engagement, and the emergence of more conditional attitudes toward tax compliance.
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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 | 0.031 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".