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Record W3015345097 · doi:10.1080/01900692.2020.1741616

The Differential Impact of Democracy on Tax Revenues in Developing and Developed Countries

2020· article· en· W3015345097 on OpenAlexaff
Harun Ur Rashid, Hussein A. Warsame, Shahid Khan

Bibliographic record

VenueInternational Journal of Public Administration · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDeveloping countryDemocracyTax revenueRevenueEconomicsPublic economicsDevelopment economicsBusinessPolitical scienceEconomic growthAccountingPolitics

Abstract

fetched live from OpenAlex

This paper investigates the extent to which democracy affects tax revenues in developing countries in comparison to developed countries across various categories of tax revenues. Based on a sample consisting of 30 developed and 29 developing countries for 2006–2013, the authors find that while democracy has a positive association with tax revenues in developed countries, the association is generally negative for developing countries compared to their counterparts. This study finds that the tax revenues most negatively affected by democracy in developing countries are corporate. The positive findings for developed countries support predictions of the compatibility perspective: that democracy results in economic growth. For developing countries, the relationship is either negative or weaker, matching the predictions of the conflict perspective that democracy results in various groups increasing rent-seeking activities from the state. These findings have implications for tax-related public policies.

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)0.007
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.079
GPT teacher head0.317
Teacher spread0.238 · 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

Citations22
Published2020
Admission routes1
Has abstractyes

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