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Record W1926992579 · doi:10.1093/afraf/adv022

The political economy of property tax in Africa: Explaining reform outcomes in Sierra Leone

2015· article· en· W1926992579 on OpenAlexaff
Samuel Jibao, Wilson Prichard

Bibliographic record

VenueAfrican Affairs · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProperty taxSierra leoneTax reformDecentralizationPoliticsOpposition (politics)Political economyEliteIncentiveTax avoidancePolitical scienceCorporate governanceEconomicsEconomic policyDevelopment economicsMarket economyFinance

Abstract

fetched live from OpenAlex

Effective local government taxation is critical to achieving the governance benefits widely attributed to decentralization, but in practice successful tax reform has been rare because of entrenched political resistance. This article offers new insights into the political dynamics of property tax reform through a case study of Sierra Leone, focusing on variation in experiences and outcomes across the country's four largest city councils. Based on this evidence, the article argues that elite resistance has posed a particularly acute barrier to local government tax reform, but that ethnic diversity has sometimes served to strengthen reform by fragmenting elite resistance. Furthermore, opposition councils have had stronger incentives to strengthen tax collection than councils dominated by the ruling party, in order to increase their fiscal autonomy. More generally, heightened electoral competition can lead to sustained revenue gains by encouraging city councils to adopt a more contractual approach to tax reform that stresses transparency, engagement, and equity.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.042
GPT teacher head0.279
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations67
Published2015
Admission routes1
Has abstractyes

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