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Record W4392754716 · doi:10.5539/ijef.v16n4p78

Examining the Impact of Baumol’s Cost Disease in Brazilian Municipal Education: A Decade Analysis (2009-2019)

2024· article· en· W4392754716 on OpenAlexvenueno aff
Ricardo Da Costa Nunes, André Nunes

Bibliographic record

VenueInternational Journal of Economics and Finance · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Public Policy
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsPublic economicsDevelopment economicsEconomic growthRegional scienceSociology

Abstract

fetched live from OpenAlex

This paper examines the phenomenon known as Baumol’s cost disease within the Brazilian educational sector between 2009 and 2019, marked by substantial changes in teacher salaries and student-teacher ratios. Baumol’s cost disease describes the rise in salaries despite low productivity growth in sectors that do not benefit substantially from technological improvements. In education, salaries increased by establishing a national wage floor and decreasing student-teacher ratios. The study adapts Baumol’s model to the modern educational context, analysing the correlation between teacher remuneration and productivity and incorporating contemporary economic and policy dynamics. The findings indicate that, contrary to the theoretical expectation of a U-shaped curve for per capita educational spending, costs per student tend to decrease with the increase in municipal population size, with an exception observed in the largest cities. This paper contributes to the understanding of public spending on education in Brazil, highlighting the need for differentiated policy approaches to manage escalating costs in smaller municipalities and ensure equitable education quality across different municipal sizes.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.501
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.035
GPT teacher head0.383
Teacher spread0.349 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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