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Record W4292608436 · doi:10.47814/ijssrr.v5i8.416

Fiscal decentralization, federal resources and municipal public revenues in Mexico

2022· article· en· W4292608436 on OpenAlexaboutno aff
José Antonio Villalobos López

Bibliographic record

VenueInternational Journal of Social Science Research and Review · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBusiness, Innovation, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsRevenueTax revenueDecentralizationProperty taxLatin AmericansBusinessTotal revenueEconomic policyEconomicsFinancePolitical sciencePublic economicsLawMarket economy

Abstract

fetched live from OpenAlex

In 1983 all municipalities in Mexico absorbed 2.6% of national public revenues, being that by 2019 it reached 6.6%, thus showing a substantial growth in 36 years. Of the total municipal public revenues, federal participations represented 37.1% in 2018 and 37.1% in 2019, while federal aportments represented 35.9% in 2018 and 35.3% in 2019; both federal resources meant 73% in 2018 and 73.8% in 2019, that is, out of every 4 pesos of revenues 3 come from the federation. In 2019, the main revenues of the municipalities that come from federal funds are: General Participation Fund (23.1%); FORTAMUN (15.2%); Social Infrastructure Aportments Fund (12.79%). The tax effort or municipal own revenues accounted for 22.6% of total municipal public revenues in 2018 and 23.1% in 2019. Property tax is the main figure of municipal own revenues, representing 47.1% and 45% respectively in 2018 and 2019.As an international comparison point property tax related to GDP yielded these figures: France 4.03%, Great Britain 4.08%, Canada 3.87%, United States 2.96%, Spain 2.43%, Colombia 1.79%, Chile 1.12% and Mexico only 0.33% of GDP; appreciating a very low percentage in relation to the two Latin American nations and much lower compared to developed countries.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.477
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.116
GPT teacher head0.368
Teacher spread0.252 · 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 teacher head, 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

Citations0
Published2022
Admission routes1
Has abstractyes

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