Sectoral Impacts of Public Expenditures on Real Wages: Evidence from Brazilian States
Bibliographic record
Abstract
This work analyzes the sectoral impacts of public spending on real wages in Brazilian states. A panel covering the period of 1995-2014 for Brazilian states is used, with information on public expenditures, gross domestic product and average real wages in seven sectors of activity, with impulse response functions extracted from panel VAR models. The sectors analyzed are Agriculture, Plant Extraction, Hunting and Fishing, Services, Commerce, Construction, Manufacturing, Public Utility Services and Mineral Extraction. The sectoral analysis is relevant given the possibility of asymmetries in real wage responses to fiscal policy in different sectors. From this exercise, it is possible to make inferences about the labor market in these sectors in Brazil, and infer if the outcomes are similar to those found in the Real Business Cycles tradition, i.e., with negative responses of real wages to shocks in the budget spending of states, or if the fiscal shocks increase real wages, as observed in the New Keynesian models. The evidence suggests that both GDP and real wages respond positively to shocks in public spending in all sectors analyzed and that these impacts are greater in the states in the South, Southeast and Midwest regions of Brazil.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".