Examining the Impact of Baumol’s Cost Disease in Brazilian Municipal Education: A Decade Analysis (2009-2019)
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".