Economic fluctuations and unemployment by gender in Brazil: an empirical verification of Okun`s Law (2002 - 2015)
Bibliographic record
Abstract
This work aims to empirically verify the Okun's Law (1962), negative relationship between macroeconomic variables of product and unemployment, for the Brazilian economy in the period stretching from the second quarter of 2002 to the first quarter of 2015. The data used is the data available at the Instituto Brasileiro de Geografia e Estatística (IBGE), for quarterly GDP of the Brazilian economy and data of the quarterly total unemployment rate and by gender of the workforce, collected by the Pesquisa Mensal do Emprego (PME), carried out by IBGE, to the metropolitan areas of Recife, Salvador, Belo Horizonte, Rio de Janeiro, Sao Paulo and Porto Alegre. In order to verify the econometric Okun s relationship, the study relies on gap models, via Ordinary Least Squares (OLS), and these models consider that the first difference of the unemployment rate is negatively dependent on the output gap. The results confirm the Okun s relationship in the analyzed period, with coefficients values of 0.09, when considering the overall national unemployment, of 0.09 for male unemployment and 0.11 for female unemployment. These results are comparable to Ball et al. (2013), for the total unemployment model and Hutengs and Stadtmann (2014), for models considering the employee's gender. The coefficients allow us to infer that women are more sensitive to economic fluctuations than men, indicating that the inclusion of both occurs differently in the labor market.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".